April 25, 2024 • 41 mins
AI is all the rage right now. There are billions of dollars now flowing into the space, with large and small companies all competing to create the next big thing. But in addition to lots of money, building new AI models requires top-tier researchers. So, who's attracting the best? And what does it take to be considered top talent in AI anyway? On this episode we speak with Damien Ma, managing director at MacroPolo, the in-house think tank of the Paulson Institute. Damien helps put together MacroPolo's Global AI Talent Tracker, which monitors the flow of top-tier AI researchers around the world. We discuss who's winning the AI talent war so far, the purported talent drain in China, competition from India, and much more.
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Episode Transcript
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Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2 (00:17):
Hello and welcome to another episode of the Odd Lots Podcast.
I'm Tracy Alloway.
Speaker 3 (00:22):
And I'm Joe Whysenthal.
Speaker 2 (00:23):
Joe, have you watched The Three Body Problem?
Speaker 3 (00:25):
No, but I really want to, and I didn't read
the book. So in case you're going to ask that
I didn't, I want to do that too, but I
intend to at some point.
Speaker 2 (00:33):
There goes my carefully crafted intro where we talk about
the Three Body Problem. Okay, well, this will work well.
As everyone knows except for Joe, there's sort of two
types of people in the world when it comes to
the Three Body Problem. There are those who see it
as an allegory for climate change, so humans coming together
to unite against a common threat, which, in this case,
(00:56):
since you haven't read the book, is an alien.
Speaker 4 (01:00):
Yeah.
Speaker 3 (01:00):
A friend of mine this weekend told me like two
plot points.
Speaker 2 (01:03):
Okay, good, good, good, yes, okay. And then there are
also those who see it as sort of an allegory
for the trade or tech war between the US and China,
So the idea that humans are going up against a
much more technologically advanced opponent, and in this scenario, I
guess Earth is China and the aliens are the US. Well,
(01:25):
today we are firmly in that second camp. We're going
to talk about US China rivalry in tech, and in
particular one area of tech AI.
Speaker 3 (01:35):
Right, so obviously AI AI AI, everyone talks about it
all the time. We don't really know where it's going
to go, but we know a few things in the meantime,
which is that people are spending money like crazy on chips,
but they're also spending money like crazy on talent. And
anyone who is capable of doing sort of cutting edge
research in AI, from what I can tell based on articles,
(01:57):
like they basically just get to pick where they want
to work, can basically pick their salary. There's a great
article in the Information a couple of weeks ago about
Facebook hiring top researchers without even doing an interview. It's like,
if you know this stuff, someone will hire you and
pay you a lot of money.
Speaker 2 (02:11):
Yeah, And I have so many questions in this space.
So first of all, like who is an AI talent
or what is an AI talent? Where do they come from?
Is it the same as being a software engineer, but
you have a slightly different area of expertise. I really
don't know. And then secondly, I'm kind of curious how
fungible the jobs are. From what you just said and
(02:32):
the fact that companies are hiring without interviews and things
like that, and that demand is so strong, it seems
like you can just do AI anywhere, whether it's China
or the US or somewhere else in the world, or
whether it's a specific company versus another one. But so
many questions on this AI talent war. I guess you
could say totally.
Speaker 3 (02:52):
And there's two things. So I sort of consider myself
a bit of an AI talent because I think I'm
pretty good at coming up with chet GPT.
Speaker 2 (02:58):
You are, actually I listeners. I have learned a lot
from watching Joe enter his prompts, and I still find
it incredibly endearing that you say please and thank you.
Speaker 3 (03:07):
Well, it's important for when AI becomes sentient that they're
going to remember who said please and thank you. But
beyond that, you know, there's this other element, and you
already sort of alluded to it. But it's clear that
for whatever reason, countries feel like AI, almost as if
it's a commodity there it must be some every country,
(03:28):
or there's this narrative being pushed by the industry, and
maybe it's just a narrative to sell chips or subscriptions
to the open AI APIs.
Speaker 2 (03:36):
Et cetera.
Speaker 3 (03:36):
But there seems to be this narrative that every country
must have some sort of homegrown AI strategy data center
or something like. Something about this technology seems to engender
political and nationalistic anxieties.
Speaker 2 (03:52):
Yes, I think that's absolutely true, and we're back to
sort of the three yady geopolitical tension point. But I
am very pleased to say that we in fact have
the perfect guest to talk about all of this. We're
going to be speaking with Damien ma He is the
managing director at macro Polo, which is the think tank
at the Paulson Institute, and they publish something called the
(04:14):
Global AI Talent Tracker, so actually keeping track of where
AI talent is coming from, how much there is, and
where it's going. So Damian, thank you so much for
coming on all.
Speaker 4 (04:25):
Thoughts, Thank you so much, it's great to be here.
Speaker 2 (04:28):
How long have you guys been doing this talent tracker?
And what was the genesis because for me chat, GPT
and all the chatbots seem to have come out of
nowhere almost basically a year ago. So how did you
get an early start on tracking AI?
Speaker 4 (04:44):
Well, the original conception is that we thought a little
bit hard about, you know, what would you need to
have a robust AI ecosystem or an AI industry, And
we thought there are three key pieces. You need. Obviously,
compute power, so things like chips and the infrastructure need.
Obviously a lot of training data. Data is obviously everywhere now.
And we thought the last piece that people haven't thought
(05:05):
too much about is human capital because it is a
very human capital intensive area and discipline because it's highly
complex and complicated and you need to highly trained people
to be able to do it. So we thought, nobody's
really looked at the human capital side of things. Is
there a way to do that? And then so we
sort of found this one conference that's widely known in
the AI community as one of the most prestigious, and
(05:28):
so we looked at papers and researchers that went to
that conference. This was back in twenty twenty during the pandemic.
Was when we first launched the initial tracker. That gave
us our idea that's a proxy for sort of the
top twenty percent of global AI talent. So this is
not all AI talent, This is not everybody in the world,
but this is really sort of what we might call
(05:49):
it the cream of the crop, top twenty percent, and
within that there's also the top two percent. So we're
looking at really kind of the elite people, which is
probably the type of people that's being thought of, you know,
most fiercely, because people want the top talent real quickly.
Speaker 3 (06:03):
What's the conference.
Speaker 4 (06:04):
It's called the Newer IPS. It's a conference that's held
I think, I think every year, but we didn't track
it every year. We tracked in twenty twenty and then
we did it again and we looked at the twenty
twenty two. We were trying to see, you know, had
there been any changes after the three year pandemic, to
see if there were different mobility patterns. This is a
conference that's mainly focused on neuro networks, large language models,
(06:28):
so a lot of things that are currently really pushing
up frontiers of a generative AI. So we thought that
those are the kinds of people that would probably want
to work for the Googles that open AIS and you know,
buy dues of the world, and so that seemed like
a good sampling. Again, we don't pretend that this is comprehensive,
but it is sort of the elite twenty percent sample.
Speaker 3 (06:48):
Just real quickly. Since you say you're able to distinguish
between the top twenty percent and the top two percent,
how do you do that part? I mean, it can't
just be people who attend the conference, Like how do
you sort of grade or figure out like who is
this specific ultra elite AI engineering talent?
Speaker 4 (07:04):
So we looked at authors whose papers got accepted, and
within that acceptance there's a oral presentation. You don't get
accepted to oral presentation unless you're really really good. So
they are only about two percent of people that got
accepted at oral presentation, So that to us was sort
of the proxy for the two percent.
Speaker 2 (07:21):
This kind of leads into what I was wondering, which
is what makes a really good AI engineer? Like what
is it that would lead them to be someone who
presents at a conference like this.
Speaker 4 (07:32):
I mean, Joe just said, you know, he's a really
good prompt engineer.
Speaker 1 (07:36):
So.
Speaker 3 (07:38):
They would let me present.
Speaker 2 (07:39):
Joe, I'm sure your invites.
Speaker 4 (07:40):
In the mail, you know, like really curate the questions. Well,
but I think that's really good.
Speaker 2 (07:46):
It's not just curating the questions, right, it's like actually
coming up with the natural language models and things like that.
Speaker 4 (07:50):
Okay, yeah, so so I think it's a really good question,
and I'm not sure the distinction is huge. I think
the foundation of AI is all computer science. Most AI
people would call them those computer scientists first and foremost,
or people that have a lot of mathematical training. And
in fact, I think some of those people I think
back into two thousands and two thousand tens, we're the
same people that got attracted to big finance, right and
(08:12):
went to build algorithms for you know, trading desks. Those
are probably a similar type of people now they're just
doing AI. And the AI specific apply part is being
able to train large amounts of data and be able
to write out algorithms. But those are the things that
you would get from computer science training with a bit
of sort of a you know, added AI specific component
to it. And I think the neuro networks thing is
(08:34):
probably you know, one distinguishing characteristic is trying to really
figure out how do you make the computer mimic the
human brain in a way. But fundamentally it's just mathematics,
quantitative computer science. All those things you know eventually can
become AI scientists.
Speaker 3 (08:49):
So there's a certain type of person who is seeking
out the hardest or maybe most lucrative sort of real
world math problem or computer science problem. At any time.
Maybe in the two thousand they were going to Wall
Street to figure out the best way to create new
securitized products and derivatives. In the twenty tens, they went
to Facebook and Google to figure out the ways to
(09:10):
pack the most number of ads on a smartphone or
get you to click on them. And now apparently they're
going into AI research. So let's start with what the
data shows. Big picture. When you started first started collecting
the data in twenty twenty, where were they coming from
and where were they going?
Speaker 4 (09:27):
A lot of them came out of China in the
United States in twenty twenty, that was pretty clear. Most
of them ended up in the United States by far,
and we're still seeing that in our latest update in
twenty twenty three. Although I would say the big surprise
was that China has done a really good job really
ramping up its domestic supply of top AS scientists, so
(09:48):
they're producing nearly half of the world's top tier AI
scientists now, and many of them are actually also staying
in China. And the reason is, I think it's pretty simple,
is that China is obviously been focusing on its own
AI industry, and as we already said, you know, people
go where the jobs are, and if you look at
the major economies where they're focused on building out AI
(10:11):
industry opportunities, it's probably the United States and China. And
if you look at Europe, actually I think punches way
below its way in terms of having an AI industry,
and so they you know, they don't tend to attract
as many top tier AI talent as China or the US.
And if you look within top US institutions where top
(10:33):
A italent work, it really is almost a Chinese American doopoly.
Chinese origin and American AI scientists are seventy five percent
of the top aalent within US institutions.
Speaker 2 (10:46):
What are the factors that would go into say a
computer scientist who has been educated in China and they're
surveying the different opportunities available to them, what are the
factors that would go into them making a decision, like
are there immigration considerations? I imagine pay and renewneration would
(11:07):
have to factor into that how easy is it for
them to switch from China to the US.
Speaker 4 (11:14):
I think the skills and you know, and the training
is fairly similar if if you come out of a
top program, whether it's Chinhua in China or or you know,
Stanford in California. I think the key from what we're seeing,
you know, one key indicator of where people end up
for work, you know, is really where they go to
graduate school. That's probably not a surprise. If you're going
(11:34):
to do your master's or PhD somewhere, you generally start
to search for job opportunities you know, near you, around you,
unless you happen to be in a country in an
area where there's not a lot of opportunities post graduation.
And of course, when you're considered an elite AI talent,
you generally have a terminal degree, usually a PhD, but
at least a master's, so, you know, I think where
(11:56):
you choose to go to graduate school is really important,
and we see that in the data. You know, those
who come to the United States or graduate school, by
and large tend to stay in the US to work
unless there's some very lucrative opportunity that attracts them back
home or somewhere else, But generally there's a bit of
a path dependence between you know, graduate school and staying
in that country to work.
Speaker 3 (12:15):
There has been a lot of anxiety for years in
the tech industry where you see CEOs and leaders complaining
that the US immigration policy has made it too hard
to keep talent who has graduated in the United States,
and there's this idea of like, hey, if they're going
to come here for education, why are we not reaping
the benefits of the US educated talent. It does seem
(12:36):
like from your data that still many are staying in
the United States, But the numbers have changed since twenty twenty.
Speaker 4 (12:43):
Yes, yes they have, you know, gone down a little bit.
We didn't go into really exploring exactly what happened over
the last three years, in part because I think many
people realize the pandemic years have been a little strange,
whether it's for economic data or just general ability for people,
where people work, how people work. So there's going to
be a lot of distortions in those last three years.
(13:04):
But there has been a relative decline, especially among the
Asian talent. It's not just China. India has also done
a better job retaining its own top tier AI talent
South Korea. Interestingly, that's not on our data set yet,
but we're about to publish Regional South Korea. They've retained
ninety percent of their talent, they've not let anybody leave,
(13:24):
and they've been really good at doing that. And places
like France have actually done a very good job on
retaining their talent. So I can't say definitively what the
reason is. Whether countries have stepped up their gain to
retain domestic talent, or there's been other things that happen
in the pandemic that's triggered it, or there could be
immigration challenges and so on. I think maybe in the future,
(13:44):
when we do the next iteration, we will have more
clarity to see the pattern. So I'd be a little
hesitant to give definitive conclusions at this point.
Speaker 3 (14:07):
Tracy, If France does a really good job keeping their talent,
who will fill the niche of blowing up trading desks
with exotic derivatives? If all those it called polytechnique in
sciences PO graduates going to AI.
Speaker 2 (14:19):
Onstay, Yeah, Yes, it is always a French person working
in equity derivatives with a mathematics degree. You're absolutely correct,
but on the degree topic. So I hadn't realized that
in China and Damian, I think this factoid was in
one of the reading materials that you sent. But Chinese
universities have launched more than two thy three hundred undergraduate
(14:42):
programs since twenty eighteen, when the Ministry of Education designated
AI as a separate major that's distinct from computer science. So,
first of all, how common is that that you would
get a separation between computer science versus AI? Is that
the standard in other parts arts of the world or
is it still relatively new? And then secondly, presumably this
(15:04):
is part of China trying to build up its domestic
AI talent pool and eventually its capabilities in this area.
What else is it doing on that front?
Speaker 4 (15:15):
Yeah, So that's why one of the reasons we think
that China has really seen this boom on top AI
talent is you have just kind of a graduating class
in twenty twenty two. If you start in twenty eighteen,
some of them are graduate students, some of them are
undergrad so they've really pushed really hard to grow at
the AI talent when now not all of them are
the top twenty percent, But I think China looks at
(15:35):
it as a way that they're going to need a
lot of AI specific technicians. China's not really thinking about
AI in the generative AI sense. I think there are
definitely some startups and folks pursuing things like chatbt Chatboss,
but my understanding is that China's probably going to focus
much more on industrial applications of AI, manufacturing, robotics, probably healthcare, biotech.
(15:58):
I'm going to bet that's going to be a huge
application for China. And I think for obvious reasons, generative
AI is probably not as copesthetic with the governance system
in China ultimately, and I think that's a pretty clear
thing that I think everyone knows. But I think they're
really looking at how to apply artificial intelligence to energy,
to industry, to advanced manufacturing, or things like climate. That's
(16:21):
where China's really focused on, and I think they feel
like they need a lot more people, not just the
cream of the crop, but sort of you know, middle
level technicians, people that are just familiar with being able
to like run data or to run Python, or to
just check all the data. So I think they're viewing
AI as a very wide, expansive way of creating certain jobs.
Speaker 2 (16:42):
Yeah, I can't imagine China's ambition here is to have
like five thousand different chatbots. Like there is clearly a
tendency towards industrial sort of real world applications of this technology.
On which note, do you think there's currently enough places
for AI graduates or specialists to actually go within China,
(17:04):
Because in some respects it feels like this might be
a very hot degree. People are being encouraged to do it,
but at the moment companies aren't necessarily at the same
sort of level. It feels like there's sort of a
mismatch in the evolution of this.
Speaker 4 (17:19):
At the moment, I think you're absolutely right. So we've
seen these kinds of bubbles before that you know, the
new hottest sector in China, everyone goes there because they
think that's where the opportunities are. And then you know,
China already had what we would call a college bubble
for the last ten years, and that's why you have,
you know, really high youth joblessness in China. Though. The
way I think about how China works in that respect
(17:40):
specifically is that they're basically two different cycles. In China,
there is a policy induced cycle, and then there's an
actual Marcus cycle that comes after that. So right now
we're in sort of this policy driven like you know,
you guys got to come in and we really like AI.
We're going to create all these programs and you should
just get AI. And then you know, parents are like, well,
well that seems like a good new thing, and that's
(18:02):
what the government's promoting. So all my kids that are
going to you know, do computer science, they're going to
add the AI component to it. So that's sort of
the policy induced cycle. And then after that, once the
bubble happens, it will kind of eventually get into a
market cycle where it'll correct a little bit. And then
and then people will be like, oh, well, actually we
probably not have an oversupply of a lot of these
you know, you know middle AI technicians that will have
(18:25):
no jobs. What are we going to do with them?
Speaker 5 (18:28):
We don't know.
Speaker 4 (18:29):
So I think this is a pattern that happens in
China a lot, and I wouldn't be surprised if that
happens with the AI talent pool as well.
Speaker 3 (18:36):
So there's a lot of interesting threads to pull on
already in this conversation, and I want to return to
the non chatbot applications of AI, like how can we
make better robots and factories and drug discovery, et cetera.
But I want to ask another question. So, okay, all
these new institutions or graduate programs have been launched in
(18:56):
China and more and more universities offering degrees and or
computer science or related fields. In my mind's eye, if
I imagine what a top AI researcher, I imagine maybe
they have a PhD from MIT or Stanford or something
like that. When you look at the institutions in China,
has there been any sort of broadening out of the
(19:17):
number of schools that are capable of producing either those
top twenty percent or top two percent talent beyond just
the sort of like handful of schools that we've for
a long time understood as the elite schools.
Speaker 4 (19:31):
There have been a little bit. And when it comes
to Asia specifically and China, I think they have the
eleven of the fourteen top AI institutions in Asia. But
in terms sort of you know, just top in general,
China has climbed quite a bit. Places like Due John University,
shanghaijel Tone, which are not your traditional names that you
would hear.
Speaker 3 (19:50):
Yeah, I've never heard of either.
Speaker 4 (19:52):
It's not Pku, it's not chin Hua. And interestingly, this
is an interesting you enter into into twenty twenty two.
Huawei is actually one of the top twenty five institutions
for AI researcher globally, so they've invested a lot in
hiring top AI talent for obvious reasons.
Speaker 2 (20:09):
This is actually exactly what I wanted to ask you next,
which is you mentioned I do as well earlier in
the conversation. But in terms of domestic destinations for AI specialists,
is the idea here that a lot of the existing
internet companies in China that they're going to devote more
development and more resources to this particular technology as we've
(20:32):
seen here in the US. But also that maybe some
of those big like consumer internet companies, the ones that
had a very rough few years during Shi Shinping's big
crackdown on disorderly capital expansion, that they're going to pivot
as well.
Speaker 4 (20:49):
So I think that's basically correct. I do, as far
as I'm concerned, has basically become an AI company, and
I think they made that strategic change many many years ago,
and one of their big focuses is I think like
Tesla autonomous driving, and no one has really been able
to crack that. I think that's sort of the AI
frontier that everyone's really focused on is how to solve vision, right,
(21:11):
because everyone's now focused on how to solve language, which
is what generative AI, and a lot of the products
we see today is kind of language based. But vision
is a really tough not to crack, and Baidu is
the one in China that's really been trying to solve it,
and I'm not sure their progress is any better than
Google or anybody else. But in terms of some of
the software companies like alibabacent has been doing a lot
(21:32):
of AI investments and obviously by Dance, so there's been
a lot of that. But what we're also seeing. We
did a recent piece where we looked at where Chinese
VC money has been going venture capital, whether venture capital
is going to a lot of these places, but in fact,
venture capital actually has been invested less in software in
the last few years, but actually you've invested more in
(21:53):
sort of hard tech hardware, so similar things like the
advanced manufacturing side. So I really think, you know, in
the next few years We're going to see a lot
of money, private and public going into sort of these
advanced manufacturing hard tech side of things that will have
AI applications. And I think there will be some startups
in China that probably we haven't heard of today that's
(22:14):
going to put a lot of money into AI. But
the big eyes are doing it. But by Do is
probably the one that's the most prominent in trying to
solve the sort of autonomous vision problem, and they will
be a big employer in China for sure for AI talent.
Speaker 3 (22:27):
So going back to the other industrial applications of AI,
like already there's this just tremendous anxiety in the US
and Europe about whether there's any way to catch up
with China's sort of advanced manufacturing prowess, whether we're talking
about cars, whether we're talking about batteries, whether certainly whether
we're talking about certain types of chips. Should the US
(22:48):
be concerned perhaps that here chatbots are the shiny new thing,
and everyone wants to work on a better chatbot, And
in the meantime, China gets even better at sort of
automated factories. Particularly imagine with better vision technology that factory
floor robots could be safer, or could be more agile, etc.
Do you see a sort of like further widening of
(23:11):
the nature of the US China competition as a function
of where the AI talent has gone.
Speaker 4 (23:17):
I'm not sure I can give you a very satisfying answer.
I guess the way I would think about that something
that would be emblematic of sort of both advanced manufacturing
and AI applications, sery software and hardware. I think the
key for both countries, and I think all countries is
probably going to be in robotics. That's sort of the
new frontier of whether it's the optimist humanoid robot China's
(23:40):
got I'm guessing like half a dozen robotics startups already.
So if one country, one company succeeds in that arena
and is able to really blend that hardware and software
and make it work and commercially viable, I think that
could send a lot of strong signals about the relative
capabilities of each country.
Speaker 2 (24:00):
Are you going to start a robotics talent tracker?
Speaker 4 (24:03):
Robots is that's going to involve a lot of supply chains,
So it's a little tougher than just looking at the people.
You got to bring in the chips. You got to
bring in the engineers, the mechanics. So it's more than
just EI scientists when it comes to robots. But interesting
for sure.
Speaker 2 (24:18):
So one thing I wanted to ask, because you're looking
at this world very carefully and sort of watching what
people are doing and saying, But what is the language
that I guess policy makers in China are using around
AI talent, Like what sort of statements do you tend
to hear? And I'm thinking back again to that famous
(24:39):
disorderly capital expansion phrase that She Shinping deployed when he
was cracking down on things like the education sector and
consumer internet companies and stuff like that. But like, how
is this whole dynamic, this talent war couched in among
policy makers.
Speaker 4 (24:58):
I think it's natural and it's given the you know,
no country generally likes brain drain. Everybody wants to have
brain gains, and I think you know that rhetoric aside
the actualization of that, And how do you set up
your own country, How do you set up the environment,
and you know, incentives, you know, compensation, all sorts of things.
The thing about top tier talent in any arena, but
(25:20):
particularly in computer science and these sort of frontier technologies.
Most of that talent, I would imagine would want to
be in the most competitive and dynamic industries. That's where
they probably feel the most comfortable. That's where they want
to make a difference with that's where they want to
make an impact and obviously the compensation all that stuff
follows that. But I think they want to have the
(25:40):
freedom to do the best cutting edge work possible. So
I think having dynamic industry is really important. And so
I'll bring the Europe example again. Europe doesn't seem to
have that, which is why they've consistently been sort of
underweighted when it comes to tracking top tier talent. And
if you look at the UK, which has been the
main place in Europe where most top tier AI talent work,
(26:03):
but in UK most of them work for Google DeepMind,
which is a US company. Right, having that industry is
I think really really important. And so in our current
debate about regulating AI and industry, I think it's going
to get controversial, it's going to get testy. We all
have known that, we all can see that, but I
think we have to think about, you know, if countries
(26:23):
want to attract the top tier talent. They want to
work in the most cutting edge, dynamic thing where they
can do the coolest, the most transformative stuff possible. And
if that's in America, great, But if China does that,
maybe it's China. But you know, right now, China still
mainly relies on its own domestic talent. They're not really
importing much foreign talent either. So to me, I think
(26:46):
having that industry is really really vital.
Speaker 3 (27:04):
What are US universities doing. I imagine a high schooler
graduating in twenty twenty four, probably way more than four
years ago or even one year ago, are saying like, oh, yeah, well,
this is what I want to do. I want to
work in AI or something in this realm. Have we
seen an expansion of what US universities are offering or
capable of offering. Has there been that sort of supply
(27:27):
side capacity increase here to take advantage of what is
almost certain an increased interest in this industry.
Speaker 4 (27:33):
Well, did you see the wsjpiece yesterday where all the
gen zs are becoming plumbers and electricians? Oh?
Speaker 2 (27:39):
I did, Yeah, a return to trades.
Speaker 4 (27:42):
Yeah. I mean, frankly, if I were anything, I might
consider that routes. But my understanding is that a lot
of the top tier technical schools or things that have
a technical school reputation, whether Stanford, cal Tech, Mit, Carnegie Mellon.
I mean, they definitely have AI programs. I don't know
if it's to d you know, extreme volume that China
(28:03):
has offered in a span of two or three years,
but they've definitely added those. But again, the foundation really
is computer science. So I think if you go in
and study computer science or some sort of you know,
you know, mathematics foundation, that's going to get you into
AI warming or another much easier than if you just
go straight into sort of you know AI, because you
(28:23):
can't really think about AI without having any foundational knowledge
from CS or mathematics.
Speaker 2 (28:29):
This might be a weird question, but it's related to
the idea of people choosing to become plumbers or plasterers
or whatever it might be. Do you sense a sort
of like note of caution among potential graduates in the
sense that a lot of people in recent decades were
encouraged to go into coding and become fluent in Python
(28:53):
or Rust or whatever it might be. And now we've
seen the rise of AI, We've seen model that can
actually write your code for you pretty much, and a
lot of software engineers are currently a little bit worried
about their job security and the outlook for their skills.
Does that impact the potential AI talent pool at all? Like,
(29:15):
is there a sense that, okay, I can get into this,
but then maybe in ten or twenty years the AI
is just going to be developing itself. Right? Self learning
models are already a thing, So why get into it
at all?
Speaker 4 (29:27):
Oh? Yeah, that's a tough question. Can AI be so
good that it doesn't need any human input anymore?
Speaker 2 (29:32):
Again, I've been watching the three body problems, so a
little bit of a side pipe.
Speaker 4 (29:37):
I don't know. I can't see that far into the future,
but what I will say, I guess kind of a
more realistic near term feature. I think we said earlier
that if AI is able to really solve human language,
which is obviously a big indicator of human intelligence, and
that seems to be a lot of the word the
efforts are large language models and you know, trying to
figure out how to mimic human language, human thought through language.
(30:00):
I would say one of the areas that's probably going
to be in trouble a lot is translators, that whole area,
it seems like it's going to be probably for lack
of a better term, disrupted quite a bit. Or if
you think about somebody that needs to do research in
different languages, maybe in two or three years, I can
read Japanese as easily as anyone else. Just get it
quickly translated on some AI software, and I can be
(30:21):
pretty fluent in reading Japanese. That doesn't mean you shouldn't
be studying foreign languages, so that there are a lot
of intellectual benefits to that, but I think as a
research tool and as the ability to kind of use
it as a way to understand the world. Once AI
really gets to that point, there are going to be
a lot of I think disciplines like translation, interpretation, those
(30:42):
kinds of things. It doesn't seem like there's going to
maybe be a huge need for that sort of stuff.
Speaker 3 (30:48):
So in the earlier part of the conversation, you know,
we talked about three necessary components to have a domestic
AI industry. One is talent, one is sort of infrastructure,
and then the other one is just the pure compute.
And we see companies like Facebook, like they tout as
an advantage we just acquired so and so many h
(31:09):
one hundreds from in Nvidia, and we're spending ten billion dollars,
And I kind of get the impression that having a
lot of computing power is a recruiting tactic, and that
if you're a top AI researcher, you want to be
at the place that has the most advantage just sort
of raw computing capacity. We know that there's a lot
(31:30):
of restrictions on some of the cutting edge semiconductors going
into China, and Jensen Wong of Nvidia has talked about
this and the constraints there for a potential talented AI
researcher maybe from China or studied in China. Does that
factor into it the fact that, at least for now,
it looks like, still without question, that the US institutions,
(31:52):
whether we're talking about Meta, whether we're talking about Amazon,
Microsoft with OpenAI, have the most computing power to play with.
For lack of a better term.
Speaker 4 (32:01):
That could certainly be one attractive factor. But I can't
remember where I read it, but I was shown like
an interesting survey on one of those Chinese social media
sites where apparently our a talent tractor got some traction
in Chinese and so a bunch of AI people in
China wade in and if I remember correctly, don't quote
me on it, but I think one of the main
(32:21):
things that stood out was that one of the things
that really attract that kind of talent is the research
environment where they're able to have the freedom and the
ability to have free thought and be able to, you know,
kind of pursue things that they think are really interesting,
that are really worthwhile. So that stood out to me
as a really important factor. And beyond the compute you know,
of prowess and beyond beyond compensation obviously, but I think
(32:45):
it seems like, you know, at least I think the
United States still seems to really have that you know,
culture like default, and I think that's a really important
ingredient that people shouldn't forget about. Again. I just think
top tier talent tend to want to be unencumbered, restricted
because they want to pursue things that they think are
really really, really interesting and groundbreaking, and that's just the
(33:06):
way they work, and so you got to give them
that environment to work in.
Speaker 2 (33:10):
All right, Damien, that was such an interesting conversation. Thank
you so much for coming on odd lots and it
is the Global AI Talent Tracker, and you can look
it up online. It's got some really good charts and
sort of interactive elements that you can play around with.
So thanks Damian for coming on and walking us through
the latest work that you've been doing.
Speaker 4 (33:30):
Thank you so much.
Speaker 5 (33:30):
Great talking to you, Joe.
Speaker 2 (33:44):
That conversation answered a lot of questions for me. It
was just interesting to talk about the patterns that we're
seeing play out. I think it's kind of funny that
in many ways, like this is a new technology that
everyone is excited about, but it's kind of playing out
the way a lot of stuff has played out historically,
where the US has a lead at the moment, and
(34:04):
then China is like rapidly on its heels and trying
to build out its own capacity, and then Europe is
like in the background, publishing like thought pieces and new
pieces of regulation about it.
Speaker 3 (34:18):
It's kind of funny, It's it's exactly it's exactly right.
I'm really interested in this idea that you know. I
do think that in the US, if you say AI
at this point, either people think about the text generator, yes,
or the image generators, which are amazing, But this idea
and we've been and I think we're doing some more
episodes coming up on it, but like there's also a
(34:38):
lot of excitement that like there's more to AI than
just human language, And we talked about it a little
bit on the food Automation episode. The idea that like
if robots could sort of have the same framework where
they've had tons of data and then make better decisions
so the arms aren't swinging or slight deviation and on
the assembly line doesn't disrupt them, then you know, that
(35:00):
could be incredibly powerful if they had enough training data
about all of these different scenarios that they face. And
so it's interesting to see that China, which seems to
be you know, leading the world in many ways in
terms of sort of electrical engineering capacity, that's also in
alignment with where a lot of the AI researchers are going.
Speaker 2 (35:19):
Yes, absolutely, and I know I brought it up a
number of times now, but that's why the consumer Internet
crackdown was so interesting to me, because China explicitly said, like,
we don't want all this money pouring into another new
online retailer. We have enough of those. Why don't you
take that money and invest it in chips or something
(35:39):
tangible like that, and so I do think we are
seeing that tendency right now that focus on like real
world applications, industrial applications, manufacturing that you don't necessarily see
in the US and other places in the West, because
as you know very well, Jo, it's fun to play
around with the chat bots and have become the public
(36:01):
face of this entire new technology. So that's probably one
area where China does have an advantage. But the other
thing I think so first of all, Damien talked about
the brain drain aspect of it and the idea that well,
a lot of China AI talent does end up in
the US because they go to university in the US
and then they stay there and there's demand for their services,
(36:23):
et cetera, et cetera, although maybe that will change soon.
But then the other thing I was thinking is you
brought up that question of compute power and whether or
not that's sort of a carrot for AI developers. I
also wonder about data and data restrictions in China and
what data sets they're playing around with, you know, specifically
(36:44):
for the large language models, but maybe for other things
as well. That could maybe be a competitive advantage if
you're really interested in this area, maybe you want to
go to a place that has bigger and more wide
ranging data sets like came in was kind of alluding
to totally.
Speaker 3 (37:01):
The other thing I think is really important to watch.
I remember like twenty twenty five years ago, you know,
when the number of if you just looked at the
raw number of people graduating with an engineering degree, it
was like exploding in China, and there was a lot
of sneering and sort of Western publications it's like, oh,
these are trash degrees, Like, yeah, people graduate with a
(37:22):
degree in engineering, but it's like pretty mediocre talent and
you know, not really that good, and we sort of
have to take some of these numbers with a grain
of salt. I get the impression that's changed dramatically a
lot of these schools, and so the fact that you
know that there is you can sort of come up
with this subjective measure of talent, which is who gets
to speak at these big conferences, And if there is
(37:43):
a broadening out of the number of degree granting institutions
that are represented in that top two percent or top
twenty percent, that strikes me as like a very important
trend to watch and So these universities in China that
you know, I'm not familiar with any of them, but
if there's like, you know, beyond just the sort of
the equivalents of the MIT or Stanford are also contributing
(38:06):
to that elite, that strikes me as like a very
key indicator to watch.
Speaker 2 (38:09):
Absolutely and neural information processing systems conference organizers, if you're listening,
Joe's interested in going, so send him an invite please.
Speaker 3 (38:19):
Yeah, I'll demonstrate some of the great poems and songs. No,
I've done something I don't know and like I had to.
You know, AI come up with a new verb tense
for me is very impressive. So I come up with
creative stuff.
Speaker 2 (38:29):
Oh that's interesting. You didn't tell me about that one.
Speaker 3 (38:32):
I didn't want to bore you with all my it's
not boring, all right, all right, I'll show you. I'll
show you that one.
Speaker 2 (38:37):
Have you started using Claude?
Speaker 1 (38:38):
Yeah?
Speaker 3 (38:39):
I love Claude.
Speaker 2 (38:40):
It's better, right, there's something about it.
Speaker 3 (38:42):
I don't know objectively about it. But this is also
another interesting question. So while we're talking about this, this
is like another interesting thing I'm wondering about, which is,
what if it turns out that some of the sort
of motes that we associate with software do not end
up applying as well to AI. Absolutely. Yeah, it's like
I like, for whatever reason, because I like the interface,
(39:03):
I like the way the nature of the language it speaks.
I started using Claude a lot more in a way
that I could never just imagine and say, like going
back and forth between Like once I used Google in
two thousand, I never like went back to Yahoo after that,
you know, or something like that. I've been using Google
ever since. It does make me wonder whether, like it'll
turn out that a lot of institutions with sufficient talent,
(39:24):
with sufficient compute can kind of do the same thing,
and switching costs aren't that high.
Speaker 2 (39:29):
Yeah, I was wondering about this as well, because the
premise of this entire conversation was there's like a war
going on. People are trying to develop their AI capabilities
really fast because first one wins kind of. But it
does seem like some of these programs, like the motes
might not actually be that high, and once you crack
like one level, it might be kind of fungible in
(39:53):
other ways. I don't know. I guess it'll it'll be
interesting to see definitely all right, shall we leave it there?
Speaker 3 (39:58):
Let's leave it there.
Speaker 2 (39:59):
This has been an another episode of the aud Thoughts podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 3 (40:05):
And I'm Joe Wisenthal. You can follow me at the Stalwart.
Follow our guest Damien ma He's at Damian Nicks and
I'll also check out his AI talent tracker at macro Polo.
Follow our producers Carmen Rodriguez at Carmen armand dash Ol
Bennett at Dashbot and Kilbrooks at Kilbrooks. Thank you to
our producer Moses Onam. For more Oddlots content, go to
(40:25):
Bloomberg dot com slash odd Lots, where we have transcripts,
a blog in the newsletter and you can chat about
all of these topics twenty four to seven in the
discord Discord dot gg slash odlots.
Speaker 2 (40:36):
And if you enjoy odlots, if you like it when
we have these conversations over artificial intelligence, if you want
a live demonstration of Joe's prompting of chat GPT or claude,
then please leave us a positive review on your favorite
podcast platform. And remember, if you are a Bloomberg subscriber,
you can listen to all of our episodes absolutely ad free.
(40:56):
All you need to do is connect your Bloomberg account
with Apple podcast Us. Thanks for listening.
Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2 (00:17):
Hello and welcome to another episode of the Odd Lots Podcast.
I'm Tracy Alloway.
Speaker 3 (00:22):
And I'm Joe Whysenthal.
Speaker 2 (00:23):
Joe, have you watched The Three Body Problem?
Speaker 3 (00:25):
No, but I really want to, and I didn't read
the book. So in case you're going to ask that
I didn't, I want to do that too, but I
intend to at some point.
Speaker 2 (00:33):
There goes my carefully crafted intro where we talk about
the Three Body Problem. Okay, well, this will work well.
As everyone knows except for Joe, there's sort of two
types of people in the world when it comes to
the Three Body Problem. There are those who see it
as an allegory for climate change, so humans coming together
to unite against a common threat, which, in this case,
(00:56):
since you haven't read the book, is an alien.
Speaker 4 (01:00):
Yeah.
Speaker 3 (01:00):
A friend of mine this weekend told me like two
plot points.
Speaker 2 (01:03):
Okay, good, good, good, yes, okay. And then there are
also those who see it as sort of an allegory
for the trade or tech war between the US and China,
So the idea that humans are going up against a
much more technologically advanced opponent, and in this scenario, I
guess Earth is China and the aliens are the US. Well,
(01:25):
today we are firmly in that second camp. We're going
to talk about US China rivalry in tech, and in
particular one area of tech AI.
Speaker 3 (01:35):
Right, so obviously AI AI AI, everyone talks about it
all the time. We don't really know where it's going
to go, but we know a few things in the meantime,
which is that people are spending money like crazy on chips,
but they're also spending money like crazy on talent. And
anyone who is capable of doing sort of cutting edge
research in AI, from what I can tell based on articles,
(01:57):
like they basically just get to pick where they want
to work, can basically pick their salary. There's a great
article in the Information a couple of weeks ago about
Facebook hiring top researchers without even doing an interview. It's like,
if you know this stuff, someone will hire you and
pay you a lot of money.
Speaker 2 (02:11):
Yeah, And I have so many questions in this space.
So first of all, like who is an AI talent
or what is an AI talent? Where do they come from?
Is it the same as being a software engineer, but
you have a slightly different area of expertise. I really
don't know. And then secondly, I'm kind of curious how
fungible the jobs are. From what you just said and
(02:32):
the fact that companies are hiring without interviews and things
like that, and that demand is so strong, it seems
like you can just do AI anywhere, whether it's China
or the US or somewhere else in the world, or
whether it's a specific company versus another one. But so
many questions on this AI talent war. I guess you
could say totally.
Speaker 3 (02:52):
And there's two things. So I sort of consider myself
a bit of an AI talent because I think I'm
pretty good at coming up with chet GPT.
Speaker 2 (02:58):
You are, actually I listeners. I have learned a lot
from watching Joe enter his prompts, and I still find
it incredibly endearing that you say please and thank you.
Speaker 3 (03:07):
Well, it's important for when AI becomes sentient that they're
going to remember who said please and thank you. But
beyond that, you know, there's this other element, and you
already sort of alluded to it. But it's clear that
for whatever reason, countries feel like AI, almost as if
it's a commodity there it must be some every country,
(03:28):
or there's this narrative being pushed by the industry, and
maybe it's just a narrative to sell chips or subscriptions
to the open AI APIs.
Speaker 2 (03:36):
Et cetera.
Speaker 3 (03:36):
But there seems to be this narrative that every country
must have some sort of homegrown AI strategy data center
or something like. Something about this technology seems to engender
political and nationalistic anxieties.
Speaker 2 (03:52):
Yes, I think that's absolutely true, and we're back to
sort of the three yady geopolitical tension point. But I
am very pleased to say that we in fact have
the perfect guest to talk about all of this. We're
going to be speaking with Damien ma He is the
managing director at macro Polo, which is the think tank
at the Paulson Institute, and they publish something called the
(04:14):
Global AI Talent Tracker, so actually keeping track of where
AI talent is coming from, how much there is, and
where it's going. So Damian, thank you so much for
coming on all.
Speaker 4 (04:25):
Thoughts, Thank you so much, it's great to be here.
Speaker 2 (04:28):
How long have you guys been doing this talent tracker?
And what was the genesis because for me chat, GPT
and all the chatbots seem to have come out of
nowhere almost basically a year ago. So how did you
get an early start on tracking AI?
Speaker 4 (04:44):
Well, the original conception is that we thought a little
bit hard about, you know, what would you need to
have a robust AI ecosystem or an AI industry, And
we thought there are three key pieces. You need. Obviously,
compute power, so things like chips and the infrastructure need.
Obviously a lot of training data. Data is obviously everywhere now.
And we thought the last piece that people haven't thought
(05:05):
too much about is human capital because it is a
very human capital intensive area and discipline because it's highly
complex and complicated and you need to highly trained people
to be able to do it. So we thought, nobody's
really looked at the human capital side of things. Is
there a way to do that? And then so we
sort of found this one conference that's widely known in
the AI community as one of the most prestigious, and
(05:28):
so we looked at papers and researchers that went to
that conference. This was back in twenty twenty during the pandemic.
Was when we first launched the initial tracker. That gave
us our idea that's a proxy for sort of the
top twenty percent of global AI talent. So this is
not all AI talent, This is not everybody in the world,
but this is really sort of what we might call
(05:49):
it the cream of the crop, top twenty percent, and
within that there's also the top two percent. So we're
looking at really kind of the elite people, which is
probably the type of people that's being thought of, you know,
most fiercely, because people want the top talent real quickly.
Speaker 3 (06:03):
What's the conference.
Speaker 4 (06:04):
It's called the Newer IPS. It's a conference that's held
I think, I think every year, but we didn't track
it every year. We tracked in twenty twenty and then
we did it again and we looked at the twenty
twenty two. We were trying to see, you know, had
there been any changes after the three year pandemic, to
see if there were different mobility patterns. This is a
conference that's mainly focused on neuro networks, large language models,
(06:28):
so a lot of things that are currently really pushing
up frontiers of a generative AI. So we thought that
those are the kinds of people that would probably want
to work for the Googles that open AIS and you know,
buy dues of the world, and so that seemed like
a good sampling. Again, we don't pretend that this is comprehensive,
but it is sort of the elite twenty percent sample.
Speaker 3 (06:48):
Just real quickly. Since you say you're able to distinguish
between the top twenty percent and the top two percent,
how do you do that part? I mean, it can't
just be people who attend the conference, Like how do
you sort of grade or figure out like who is
this specific ultra elite AI engineering talent?
Speaker 4 (07:04):
So we looked at authors whose papers got accepted, and
within that acceptance there's a oral presentation. You don't get
accepted to oral presentation unless you're really really good. So
they are only about two percent of people that got
accepted at oral presentation, So that to us was sort
of the proxy for the two percent.
Speaker 2 (07:21):
This kind of leads into what I was wondering, which
is what makes a really good AI engineer? Like what
is it that would lead them to be someone who
presents at a conference like this.
Speaker 4 (07:32):
I mean, Joe just said, you know, he's a really
good prompt engineer.
Speaker 1 (07:36):
So.
Speaker 3 (07:38):
They would let me present.
Speaker 2 (07:39):
Joe, I'm sure your invites.
Speaker 4 (07:40):
In the mail, you know, like really curate the questions. Well,
but I think that's really good.
Speaker 2 (07:46):
It's not just curating the questions, right, it's like actually
coming up with the natural language models and things like that.
Speaker 4 (07:50):
Okay, yeah, so so I think it's a really good question,
and I'm not sure the distinction is huge. I think
the foundation of AI is all computer science. Most AI
people would call them those computer scientists first and foremost,
or people that have a lot of mathematical training. And
in fact, I think some of those people I think
back into two thousands and two thousand tens, we're the
same people that got attracted to big finance, right and
(08:12):
went to build algorithms for you know, trading desks. Those
are probably a similar type of people now they're just
doing AI. And the AI specific apply part is being
able to train large amounts of data and be able
to write out algorithms. But those are the things that
you would get from computer science training with a bit
of sort of a you know, added AI specific component
to it. And I think the neuro networks thing is
(08:34):
probably you know, one distinguishing characteristic is trying to really
figure out how do you make the computer mimic the
human brain in a way. But fundamentally it's just mathematics,
quantitative computer science. All those things you know eventually can
become AI scientists.
Speaker 3 (08:49):
So there's a certain type of person who is seeking
out the hardest or maybe most lucrative sort of real
world math problem or computer science problem. At any time.
Maybe in the two thousand they were going to Wall
Street to figure out the best way to create new
securitized products and derivatives. In the twenty tens, they went
to Facebook and Google to figure out the ways to
(09:10):
pack the most number of ads on a smartphone or
get you to click on them. And now apparently they're
going into AI research. So let's start with what the
data shows. Big picture. When you started first started collecting
the data in twenty twenty, where were they coming from
and where were they going?
Speaker 4 (09:27):
A lot of them came out of China in the
United States in twenty twenty, that was pretty clear. Most
of them ended up in the United States by far,
and we're still seeing that in our latest update in
twenty twenty three. Although I would say the big surprise
was that China has done a really good job really
ramping up its domestic supply of top AS scientists, so
(09:48):
they're producing nearly half of the world's top tier AI
scientists now, and many of them are actually also staying
in China. And the reason is, I think it's pretty simple,
is that China is obviously been focusing on its own
AI industry, and as we already said, you know, people
go where the jobs are, and if you look at
the major economies where they're focused on building out AI
(10:11):
industry opportunities, it's probably the United States and China. And
if you look at Europe, actually I think punches way
below its way in terms of having an AI industry,
and so they you know, they don't tend to attract
as many top tier AI talent as China or the US.
And if you look within top US institutions where top
(10:33):
A italent work, it really is almost a Chinese American doopoly.
Chinese origin and American AI scientists are seventy five percent
of the top aalent within US institutions.
Speaker 2 (10:46):
What are the factors that would go into say a
computer scientist who has been educated in China and they're
surveying the different opportunities available to them, what are the
factors that would go into them making a decision, like
are there immigration considerations? I imagine pay and renewneration would
(11:07):
have to factor into that how easy is it for
them to switch from China to the US.
Speaker 4 (11:14):
I think the skills and you know, and the training
is fairly similar if if you come out of a
top program, whether it's Chinhua in China or or you know,
Stanford in California. I think the key from what we're seeing,
you know, one key indicator of where people end up
for work, you know, is really where they go to
graduate school. That's probably not a surprise. If you're going
(11:34):
to do your master's or PhD somewhere, you generally start
to search for job opportunities you know, near you, around you,
unless you happen to be in a country in an
area where there's not a lot of opportunities post graduation.
And of course, when you're considered an elite AI talent,
you generally have a terminal degree, usually a PhD, but
at least a master's, so, you know, I think where
(11:56):
you choose to go to graduate school is really important,
and we see that in the data. You know, those
who come to the United States or graduate school, by
and large tend to stay in the US to work
unless there's some very lucrative opportunity that attracts them back
home or somewhere else, But generally there's a bit of
a path dependence between you know, graduate school and staying
in that country to work.
Speaker 3 (12:15):
There has been a lot of anxiety for years in
the tech industry where you see CEOs and leaders complaining
that the US immigration policy has made it too hard
to keep talent who has graduated in the United States,
and there's this idea of like, hey, if they're going
to come here for education, why are we not reaping
the benefits of the US educated talent. It does seem
(12:36):
like from your data that still many are staying in
the United States, But the numbers have changed since twenty twenty.
Speaker 4 (12:43):
Yes, yes they have, you know, gone down a little bit.
We didn't go into really exploring exactly what happened over
the last three years, in part because I think many
people realize the pandemic years have been a little strange,
whether it's for economic data or just general ability for people,
where people work, how people work. So there's going to
be a lot of distortions in those last three years.
(13:04):
But there has been a relative decline, especially among the
Asian talent. It's not just China. India has also done
a better job retaining its own top tier AI talent
South Korea. Interestingly, that's not on our data set yet,
but we're about to publish Regional South Korea. They've retained
ninety percent of their talent, they've not let anybody leave,
(13:24):
and they've been really good at doing that. And places
like France have actually done a very good job on
retaining their talent. So I can't say definitively what the
reason is. Whether countries have stepped up their gain to
retain domestic talent, or there's been other things that happen
in the pandemic that's triggered it, or there could be
immigration challenges and so on. I think maybe in the future,
(13:44):
when we do the next iteration, we will have more
clarity to see the pattern. So I'd be a little
hesitant to give definitive conclusions at this point.
Speaker 3 (14:07):
Tracy, If France does a really good job keeping their talent,
who will fill the niche of blowing up trading desks
with exotic derivatives? If all those it called polytechnique in
sciences PO graduates going to AI.
Speaker 2 (14:19):
Onstay, Yeah, Yes, it is always a French person working
in equity derivatives with a mathematics degree. You're absolutely correct,
but on the degree topic. So I hadn't realized that
in China and Damian, I think this factoid was in
one of the reading materials that you sent. But Chinese
universities have launched more than two thy three hundred undergraduate
(14:42):
programs since twenty eighteen, when the Ministry of Education designated
AI as a separate major that's distinct from computer science. So,
first of all, how common is that that you would
get a separation between computer science versus AI? Is that
the standard in other parts arts of the world or
is it still relatively new? And then secondly, presumably this
(15:04):
is part of China trying to build up its domestic
AI talent pool and eventually its capabilities in this area.
What else is it doing on that front?
Speaker 4 (15:15):
Yeah, So that's why one of the reasons we think
that China has really seen this boom on top AI
talent is you have just kind of a graduating class
in twenty twenty two. If you start in twenty eighteen,
some of them are graduate students, some of them are
undergrad so they've really pushed really hard to grow at
the AI talent when now not all of them are
the top twenty percent, But I think China looks at
(15:35):
it as a way that they're going to need a
lot of AI specific technicians. China's not really thinking about
AI in the generative AI sense. I think there are
definitely some startups and folks pursuing things like chatbt Chatboss,
but my understanding is that China's probably going to focus
much more on industrial applications of AI, manufacturing, robotics, probably healthcare, biotech.
(15:58):
I'm going to bet that's going to be a huge
application for China. And I think for obvious reasons, generative
AI is probably not as copesthetic with the governance system
in China ultimately, and I think that's a pretty clear
thing that I think everyone knows. But I think they're
really looking at how to apply artificial intelligence to energy,
to industry, to advanced manufacturing, or things like climate. That's
(16:21):
where China's really focused on, and I think they feel
like they need a lot more people, not just the
cream of the crop, but sort of you know, middle
level technicians, people that are just familiar with being able
to like run data or to run Python, or to
just check all the data. So I think they're viewing
AI as a very wide, expansive way of creating certain jobs.
Speaker 2 (16:42):
Yeah, I can't imagine China's ambition here is to have
like five thousand different chatbots. Like there is clearly a
tendency towards industrial sort of real world applications of this technology.
On which note, do you think there's currently enough places
for AI graduates or specialists to actually go within China,
(17:04):
Because in some respects it feels like this might be
a very hot degree. People are being encouraged to do it,
but at the moment companies aren't necessarily at the same
sort of level. It feels like there's sort of a
mismatch in the evolution of this.
Speaker 4 (17:19):
At the moment, I think you're absolutely right. So we've
seen these kinds of bubbles before that you know, the
new hottest sector in China, everyone goes there because they
think that's where the opportunities are. And then you know,
China already had what we would call a college bubble
for the last ten years, and that's why you have,
you know, really high youth joblessness in China. Though. The
way I think about how China works in that respect
(17:40):
specifically is that they're basically two different cycles. In China,
there is a policy induced cycle, and then there's an
actual Marcus cycle that comes after that. So right now
we're in sort of this policy driven like you know,
you guys got to come in and we really like AI.
We're going to create all these programs and you should
just get AI. And then you know, parents are like, well,
well that seems like a good new thing, and that's
(18:02):
what the government's promoting. So all my kids that are
going to you know, do computer science, they're going to
add the AI component to it. So that's sort of
the policy induced cycle. And then after that, once the
bubble happens, it will kind of eventually get into a
market cycle where it'll correct a little bit. And then
and then people will be like, oh, well, actually we
probably not have an oversupply of a lot of these
you know, you know middle AI technicians that will have
(18:25):
no jobs. What are we going to do with them?
Speaker 5 (18:28):
We don't know.
Speaker 4 (18:29):
So I think this is a pattern that happens in
China a lot, and I wouldn't be surprised if that
happens with the AI talent pool as well.
Speaker 3 (18:36):
So there's a lot of interesting threads to pull on
already in this conversation, and I want to return to
the non chatbot applications of AI, like how can we
make better robots and factories and drug discovery, et cetera.
But I want to ask another question. So, okay, all
these new institutions or graduate programs have been launched in
(18:56):
China and more and more universities offering degrees and or
computer science or related fields. In my mind's eye, if
I imagine what a top AI researcher, I imagine maybe
they have a PhD from MIT or Stanford or something
like that. When you look at the institutions in China,
has there been any sort of broadening out of the
(19:17):
number of schools that are capable of producing either those
top twenty percent or top two percent talent beyond just
the sort of like handful of schools that we've for
a long time understood as the elite schools.
Speaker 4 (19:31):
There have been a little bit. And when it comes
to Asia specifically and China, I think they have the
eleven of the fourteen top AI institutions in Asia. But
in terms sort of you know, just top in general,
China has climbed quite a bit. Places like Due John University,
shanghaijel Tone, which are not your traditional names that you
would hear.
Speaker 3 (19:50):
Yeah, I've never heard of either.
Speaker 4 (19:52):
It's not Pku, it's not chin Hua. And interestingly, this
is an interesting you enter into into twenty twenty two.
Huawei is actually one of the top twenty five institutions
for AI researcher globally, so they've invested a lot in
hiring top AI talent for obvious reasons.
Speaker 2 (20:09):
This is actually exactly what I wanted to ask you next,
which is you mentioned I do as well earlier in
the conversation. But in terms of domestic destinations for AI specialists,
is the idea here that a lot of the existing
internet companies in China that they're going to devote more
development and more resources to this particular technology as we've
(20:32):
seen here in the US. But also that maybe some
of those big like consumer internet companies, the ones that
had a very rough few years during Shi Shinping's big
crackdown on disorderly capital expansion, that they're going to pivot
as well.
Speaker 4 (20:49):
So I think that's basically correct. I do, as far
as I'm concerned, has basically become an AI company, and
I think they made that strategic change many many years ago,
and one of their big focuses is I think like
Tesla autonomous driving, and no one has really been able
to crack that. I think that's sort of the AI
frontier that everyone's really focused on is how to solve vision, right,
(21:11):
because everyone's now focused on how to solve language, which
is what generative AI, and a lot of the products
we see today is kind of language based. But vision
is a really tough not to crack, and Baidu is
the one in China that's really been trying to solve it,
and I'm not sure their progress is any better than
Google or anybody else. But in terms of some of
the software companies like alibabacent has been doing a lot
(21:32):
of AI investments and obviously by Dance, so there's been
a lot of that. But what we're also seeing. We
did a recent piece where we looked at where Chinese
VC money has been going venture capital, whether venture capital
is going to a lot of these places, but in fact,
venture capital actually has been invested less in software in
the last few years, but actually you've invested more in
(21:53):
sort of hard tech hardware, so similar things like the
advanced manufacturing side. So I really think, you know, in
the next few years We're going to see a lot
of money, private and public going into sort of these
advanced manufacturing hard tech side of things that will have
AI applications. And I think there will be some startups
in China that probably we haven't heard of today that's
(22:14):
going to put a lot of money into AI. But
the big eyes are doing it. But by Do is
probably the one that's the most prominent in trying to
solve the sort of autonomous vision problem, and they will
be a big employer in China for sure for AI talent.
Speaker 3 (22:27):
So going back to the other industrial applications of AI,
like already there's this just tremendous anxiety in the US
and Europe about whether there's any way to catch up
with China's sort of advanced manufacturing prowess, whether we're talking
about cars, whether we're talking about batteries, whether certainly whether
we're talking about certain types of chips. Should the US
(22:48):
be concerned perhaps that here chatbots are the shiny new thing,
and everyone wants to work on a better chatbot, And
in the meantime, China gets even better at sort of
automated factories. Particularly imagine with better vision technology that factory
floor robots could be safer, or could be more agile, etc.
Do you see a sort of like further widening of
(23:11):
the nature of the US China competition as a function
of where the AI talent has gone.
Speaker 4 (23:17):
I'm not sure I can give you a very satisfying answer.
I guess the way I would think about that something
that would be emblematic of sort of both advanced manufacturing
and AI applications, sery software and hardware. I think the
key for both countries, and I think all countries is
probably going to be in robotics. That's sort of the
new frontier of whether it's the optimist humanoid robot China's
(23:40):
got I'm guessing like half a dozen robotics startups already.
So if one country, one company succeeds in that arena
and is able to really blend that hardware and software
and make it work and commercially viable, I think that
could send a lot of strong signals about the relative
capabilities of each country.
Speaker 2 (24:00):
Are you going to start a robotics talent tracker?
Speaker 4 (24:03):
Robots is that's going to involve a lot of supply chains,
So it's a little tougher than just looking at the people.
You got to bring in the chips. You got to
bring in the engineers, the mechanics. So it's more than
just EI scientists when it comes to robots. But interesting
for sure.
Speaker 2 (24:18):
So one thing I wanted to ask, because you're looking
at this world very carefully and sort of watching what
people are doing and saying, But what is the language
that I guess policy makers in China are using around
AI talent, Like what sort of statements do you tend
to hear? And I'm thinking back again to that famous
(24:39):
disorderly capital expansion phrase that She Shinping deployed when he
was cracking down on things like the education sector and
consumer internet companies and stuff like that. But like, how
is this whole dynamic, this talent war couched in among
policy makers.
Speaker 4 (24:58):
I think it's natural and it's given the you know,
no country generally likes brain drain. Everybody wants to have
brain gains, and I think you know that rhetoric aside
the actualization of that, And how do you set up
your own country, How do you set up the environment,
and you know, incentives, you know, compensation, all sorts of things.
The thing about top tier talent in any arena, but
(25:20):
particularly in computer science and these sort of frontier technologies.
Most of that talent, I would imagine would want to
be in the most competitive and dynamic industries. That's where
they probably feel the most comfortable. That's where they want
to make a difference with that's where they want to
make an impact and obviously the compensation all that stuff
follows that. But I think they want to have the
(25:40):
freedom to do the best cutting edge work possible. So
I think having dynamic industry is really important. And so
I'll bring the Europe example again. Europe doesn't seem to
have that, which is why they've consistently been sort of
underweighted when it comes to tracking top tier talent. And
if you look at the UK, which has been the
main place in Europe where most top tier AI talent work,
(26:03):
but in UK most of them work for Google DeepMind,
which is a US company. Right, having that industry is
I think really really important. And so in our current
debate about regulating AI and industry, I think it's going
to get controversial, it's going to get testy. We all
have known that, we all can see that, but I
think we have to think about, you know, if countries
(26:23):
want to attract the top tier talent. They want to
work in the most cutting edge, dynamic thing where they
can do the coolest, the most transformative stuff possible. And
if that's in America, great, But if China does that,
maybe it's China. But you know, right now, China still
mainly relies on its own domestic talent. They're not really
importing much foreign talent either. So to me, I think
(26:46):
having that industry is really really vital.
Speaker 3 (27:04):
What are US universities doing. I imagine a high schooler
graduating in twenty twenty four, probably way more than four
years ago or even one year ago, are saying like, oh, yeah, well,
this is what I want to do. I want to
work in AI or something in this realm. Have we
seen an expansion of what US universities are offering or
capable of offering. Has there been that sort of supply
(27:27):
side capacity increase here to take advantage of what is
almost certain an increased interest in this industry.
Speaker 4 (27:33):
Well, did you see the wsjpiece yesterday where all the
gen zs are becoming plumbers and electricians? Oh?
Speaker 2 (27:39):
I did, Yeah, a return to trades.
Speaker 4 (27:42):
Yeah. I mean, frankly, if I were anything, I might
consider that routes. But my understanding is that a lot
of the top tier technical schools or things that have
a technical school reputation, whether Stanford, cal Tech, Mit, Carnegie Mellon.
I mean, they definitely have AI programs. I don't know
if it's to d you know, extreme volume that China
(28:03):
has offered in a span of two or three years,
but they've definitely added those. But again, the foundation really
is computer science. So I think if you go in
and study computer science or some sort of you know,
you know, mathematics foundation, that's going to get you into
AI warming or another much easier than if you just
go straight into sort of you know AI, because you
(28:23):
can't really think about AI without having any foundational knowledge
from CS or mathematics.
Speaker 2 (28:29):
This might be a weird question, but it's related to
the idea of people choosing to become plumbers or plasterers
or whatever it might be. Do you sense a sort
of like note of caution among potential graduates in the
sense that a lot of people in recent decades were
encouraged to go into coding and become fluent in Python
(28:53):
or Rust or whatever it might be. And now we've
seen the rise of AI, We've seen model that can
actually write your code for you pretty much, and a
lot of software engineers are currently a little bit worried
about their job security and the outlook for their skills.
Does that impact the potential AI talent pool at all? Like,
(29:15):
is there a sense that, okay, I can get into this,
but then maybe in ten or twenty years the AI
is just going to be developing itself. Right? Self learning
models are already a thing, So why get into it
at all?
Speaker 4 (29:27):
Oh? Yeah, that's a tough question. Can AI be so
good that it doesn't need any human input anymore?
Speaker 2 (29:32):
Again, I've been watching the three body problems, so a
little bit of a side pipe.
Speaker 4 (29:37):
I don't know. I can't see that far into the future,
but what I will say, I guess kind of a
more realistic near term feature. I think we said earlier
that if AI is able to really solve human language,
which is obviously a big indicator of human intelligence, and
that seems to be a lot of the word the
efforts are large language models and you know, trying to
figure out how to mimic human language, human thought through language.
(30:00):
I would say one of the areas that's probably going
to be in trouble a lot is translators, that whole area,
it seems like it's going to be probably for lack
of a better term, disrupted quite a bit. Or if
you think about somebody that needs to do research in
different languages, maybe in two or three years, I can
read Japanese as easily as anyone else. Just get it
quickly translated on some AI software, and I can be
(30:21):
pretty fluent in reading Japanese. That doesn't mean you shouldn't
be studying foreign languages, so that there are a lot
of intellectual benefits to that, but I think as a
research tool and as the ability to kind of use
it as a way to understand the world. Once AI
really gets to that point, there are going to be
a lot of I think disciplines like translation, interpretation, those
(30:42):
kinds of things. It doesn't seem like there's going to
maybe be a huge need for that sort of stuff.
Speaker 3 (30:48):
So in the earlier part of the conversation, you know,
we talked about three necessary components to have a domestic
AI industry. One is talent, one is sort of infrastructure,
and then the other one is just the pure compute.
And we see companies like Facebook, like they tout as
an advantage we just acquired so and so many h
(31:09):
one hundreds from in Nvidia, and we're spending ten billion dollars,
And I kind of get the impression that having a
lot of computing power is a recruiting tactic, and that
if you're a top AI researcher, you want to be
at the place that has the most advantage just sort
of raw computing capacity. We know that there's a lot
(31:30):
of restrictions on some of the cutting edge semiconductors going
into China, and Jensen Wong of Nvidia has talked about
this and the constraints there for a potential talented AI
researcher maybe from China or studied in China. Does that
factor into it the fact that, at least for now,
it looks like, still without question, that the US institutions,
(31:52):
whether we're talking about Meta, whether we're talking about Amazon,
Microsoft with OpenAI, have the most computing power to play with.
For lack of a better term.
Speaker 4 (32:01):
That could certainly be one attractive factor. But I can't
remember where I read it, but I was shown like
an interesting survey on one of those Chinese social media
sites where apparently our a talent tractor got some traction
in Chinese and so a bunch of AI people in
China wade in and if I remember correctly, don't quote
me on it, but I think one of the main
(32:21):
things that stood out was that one of the things
that really attract that kind of talent is the research
environment where they're able to have the freedom and the
ability to have free thought and be able to, you know,
kind of pursue things that they think are really interesting,
that are really worthwhile. So that stood out to me
as a really important factor. And beyond the compute you know,
of prowess and beyond beyond compensation obviously, but I think
(32:45):
it seems like, you know, at least I think the
United States still seems to really have that you know,
culture like default, and I think that's a really important
ingredient that people shouldn't forget about. Again. I just think
top tier talent tend to want to be unencumbered, restricted
because they want to pursue things that they think are
really really, really interesting and groundbreaking, and that's just the
(33:06):
way they work, and so you got to give them
that environment to work in.
Speaker 2 (33:10):
All right, Damien, that was such an interesting conversation. Thank
you so much for coming on odd lots and it
is the Global AI Talent Tracker, and you can look
it up online. It's got some really good charts and
sort of interactive elements that you can play around with.
So thanks Damian for coming on and walking us through
the latest work that you've been doing.
Speaker 4 (33:30):
Thank you so much.
Speaker 5 (33:30):
Great talking to you, Joe.
Speaker 2 (33:44):
That conversation answered a lot of questions for me. It
was just interesting to talk about the patterns that we're
seeing play out. I think it's kind of funny that
in many ways, like this is a new technology that
everyone is excited about, but it's kind of playing out
the way a lot of stuff has played out historically,
where the US has a lead at the moment, and
(34:04):
then China is like rapidly on its heels and trying
to build out its own capacity, and then Europe is
like in the background, publishing like thought pieces and new
pieces of regulation about it.
Speaker 3 (34:18):
It's kind of funny, It's it's exactly it's exactly right.
I'm really interested in this idea that you know. I
do think that in the US, if you say AI
at this point, either people think about the text generator, yes,
or the image generators, which are amazing, But this idea
and we've been and I think we're doing some more
episodes coming up on it, but like there's also a
(34:38):
lot of excitement that like there's more to AI than
just human language, And we talked about it a little
bit on the food Automation episode. The idea that like
if robots could sort of have the same framework where
they've had tons of data and then make better decisions
so the arms aren't swinging or slight deviation and on
the assembly line doesn't disrupt them, then you know, that
(35:00):
could be incredibly powerful if they had enough training data
about all of these different scenarios that they face. And
so it's interesting to see that China, which seems to
be you know, leading the world in many ways in
terms of sort of electrical engineering capacity, that's also in
alignment with where a lot of the AI researchers are going.
Speaker 2 (35:19):
Yes, absolutely, and I know I brought it up a
number of times now, but that's why the consumer Internet
crackdown was so interesting to me, because China explicitly said, like,
we don't want all this money pouring into another new
online retailer. We have enough of those. Why don't you
take that money and invest it in chips or something
(35:39):
tangible like that, and so I do think we are
seeing that tendency right now that focus on like real
world applications, industrial applications, manufacturing that you don't necessarily see
in the US and other places in the West, because
as you know very well, Jo, it's fun to play
around with the chat bots and have become the public
(36:01):
face of this entire new technology. So that's probably one
area where China does have an advantage. But the other
thing I think so first of all, Damien talked about
the brain drain aspect of it and the idea that well,
a lot of China AI talent does end up in
the US because they go to university in the US
and then they stay there and there's demand for their services,
(36:23):
et cetera, et cetera, although maybe that will change soon.
But then the other thing I was thinking is you
brought up that question of compute power and whether or
not that's sort of a carrot for AI developers. I
also wonder about data and data restrictions in China and
what data sets they're playing around with, you know, specifically
(36:44):
for the large language models, but maybe for other things
as well. That could maybe be a competitive advantage if
you're really interested in this area, maybe you want to
go to a place that has bigger and more wide
ranging data sets like came in was kind of alluding
to totally.
Speaker 3 (37:01):
The other thing I think is really important to watch.
I remember like twenty twenty five years ago, you know,
when the number of if you just looked at the
raw number of people graduating with an engineering degree, it
was like exploding in China, and there was a lot
of sneering and sort of Western publications it's like, oh,
these are trash degrees, Like, yeah, people graduate with a
(37:22):
degree in engineering, but it's like pretty mediocre talent and
you know, not really that good, and we sort of
have to take some of these numbers with a grain
of salt. I get the impression that's changed dramatically a
lot of these schools, and so the fact that you
know that there is you can sort of come up
with this subjective measure of talent, which is who gets
to speak at these big conferences, And if there is
(37:43):
a broadening out of the number of degree granting institutions
that are represented in that top two percent or top
twenty percent, that strikes me as like a very important
trend to watch and So these universities in China that
you know, I'm not familiar with any of them, but
if there's like, you know, beyond just the sort of
the equivalents of the MIT or Stanford are also contributing
(38:06):
to that elite, that strikes me as like a very
key indicator to watch.
Speaker 2 (38:09):
Absolutely and neural information processing systems conference organizers, if you're listening,
Joe's interested in going, so send him an invite please.
Speaker 3 (38:19):
Yeah, I'll demonstrate some of the great poems and songs. No,
I've done something I don't know and like I had to.
You know, AI come up with a new verb tense
for me is very impressive. So I come up with
creative stuff.
Speaker 2 (38:29):
Oh that's interesting. You didn't tell me about that one.
Speaker 3 (38:32):
I didn't want to bore you with all my it's
not boring, all right, all right, I'll show you. I'll
show you that one.
Speaker 2 (38:37):
Have you started using Claude?
Speaker 1 (38:38):
Yeah?
Speaker 3 (38:39):
I love Claude.
Speaker 2 (38:40):
It's better, right, there's something about it.
Speaker 3 (38:42):
I don't know objectively about it. But this is also
another interesting question. So while we're talking about this, this
is like another interesting thing I'm wondering about, which is,
what if it turns out that some of the sort
of motes that we associate with software do not end
up applying as well to AI. Absolutely. Yeah, it's like
I like, for whatever reason, because I like the interface,
(39:03):
I like the way the nature of the language it speaks.
I started using Claude a lot more in a way
that I could never just imagine and say, like going
back and forth between Like once I used Google in
two thousand, I never like went back to Yahoo after that,
you know, or something like that. I've been using Google
ever since. It does make me wonder whether, like it'll
turn out that a lot of institutions with sufficient talent,
(39:24):
with sufficient compute can kind of do the same thing,
and switching costs aren't that high.
Speaker 2 (39:29):
Yeah, I was wondering about this as well, because the
premise of this entire conversation was there's like a war
going on. People are trying to develop their AI capabilities
really fast because first one wins kind of. But it
does seem like some of these programs, like the motes
might not actually be that high, and once you crack
like one level, it might be kind of fungible in
(39:53):
other ways. I don't know. I guess it'll it'll be
interesting to see definitely all right, shall we leave it there?
Speaker 3 (39:58):
Let's leave it there.
Speaker 2 (39:59):
This has been an another episode of the aud Thoughts podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 3 (40:05):
And I'm Joe Wisenthal. You can follow me at the Stalwart.
Follow our guest Damien ma He's at Damian Nicks and
I'll also check out his AI talent tracker at macro Polo.
Follow our producers Carmen Rodriguez at Carmen armand dash Ol
Bennett at Dashbot and Kilbrooks at Kilbrooks. Thank you to
our producer Moses Onam. For more Oddlots content, go to
(40:25):
Bloomberg dot com slash odd Lots, where we have transcripts,
a blog in the newsletter and you can chat about
all of these topics twenty four to seven in the
discord Discord dot gg slash odlots.
Speaker 2 (40:36):
And if you enjoy odlots, if you like it when
we have these conversations over artificial intelligence, if you want
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then please leave us a positive review on your favorite
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(40:56):
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