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