Here's Who's Winning the Global Fight for AI Talent

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1:22Hello and welcome to another episode of the Odd Lots podcast. I'm Tracey Alloway. And I'm Joe Weisenthal. Joe, have you watched The Three Body Problem? 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. 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.

1:55So humans coming together to unite against a common threat, which in this case, since you haven't read the book, is an alien civilization. I did. Yeah. A friend of mine this weekend told me like two plot points. OK, good, good, good. Yes. OK. And then there are also those who see it as sort of an allegory for the trade or tech war between the U.S. 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 U.S. Well, today we are firmly in that second camp. We're going to talk about U.S.-China rivalry in tech and in particular one area of tech, AI.

2:41Right. 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, I write like they basically just get to pick where they want to work and 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.

3:13It's like if you know this stuff, someone will hire you and pay you a lot of money. 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 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 U.S.

3:48or 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. And there's two things. So, A, I sort of consider myself a bit of an AI talent because I think I'm pretty good at coming up with chat GPT. You are, actually. Listeners, I have learned a lot from watching Joe enter his proms, and I still find it incredibly endearing that you say please and thank you. 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.

4:30There must be some every country 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, etc. But there seems to be this narrative that every country must have some sort of homegrown AI strategy or data center or something like something about this technology seems to engender political and nationalistic anxieties. Yes, I think that's absolutely true. And we're back to sort of the three body geopolitical tension point. But I am very pleased to say that we, in fact, have the perfect guest to talk about all of this.

5:09We're going to be speaking with Damian Ma. He is the managing director at Macro Polo, which is the think tank at the Paulson Institute. And they publish something called the 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 Thoughts. Thank you so much. It's great to be here. How long have you guys been doing this talent tracker and what was the genesis? Because for me, ChatGPT 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?

5:49Well, 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. And you need, obviously, a lot of training data. Data is obviously everywhere now. And we thought the last piece that people haven't thought 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 highly trained people to be able to do it.

6:21So we thought nobody's really looked at the human capital side of things. Is there a way to do that? And so we sort of found this one conference that's widely known in the AI community as one of the most prestigious. And so we looked at papers and researchers that went to that conference. This was back in 2020 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 20 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 the cream of the crop, top 20 percent.

6:57And within that, there's also the top 2 percent. So we're looking at really kind of the elite people, which is probably the type of people that's being fought over most fiercely because people want the top talent. Real quickly, what's the conference? It's called the NeuroIPS. It's a conference that's held, I think, every year, but we didn't track it every year. We tracked it in 2020, and then we did it again, and we looked at the 2022. 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 neural networks, large language models, so a lot of things that are currently really pushing the frontiers of a generative AI.

7:38So we thought that those are the kinds of people that would probably want to work for the Googles and open AIs and, you know, Baidu's 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 20 % sample. Just real quickly, since you say you're able to distinguish between the top 20 percent and the top 2 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? So we looked at authors whose papers got accepted.

8:12And within that acceptance, there is a oral presentation. You don't get accepted to oral presentation unless you're really, really good. So there are only about 2 % of people that got accepted to the oral presentation. So that to us was sort of the proxy for the 2%. 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? I mean, Joe just said, you know, he's a really good prompt engineer. So they would let me present. Joe, I'm sure your invite's in the mail. You know, like really curate the questions well.

8:49But I think that's a really good question. But it's not just curating the questions, right? It's like actually coming up with the natural language models and things like that. Okay. Yeah. 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 themselves 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 in the 2000s and 2010s were the same people that got attracted to big finance, right? and went to build algorithms for, you know, trading desks.

9:20Those are probably a similar type of people. Now they're just doing AI. And the AI-specific apply part is being able to, you know, train large amounts of data and being 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 neural networks thing is 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.

9:54So 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 2000s, they were going to Wall Street to figure out the best way to create new securitized products and derivatives. In the 2010s, they went to Facebook and Google to figure out the ways to 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 like what the data shows. Big picture. When you first started collecting the data in 2020, where were they coming from and where were they going?

10:32A lot of them came out of China in the United States in 2020. 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 2023. Although I would say the big surprise was that China has done a really good job really ramping up its domestic supply of top AI scientists. So 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 has obviously been focusing on its own AI industry. And as we already said, people go where the jobs are.

11:12And if you look at the major economies where they're focused on building out AI industry opportunities, it's probably the United States and China. And if you look at Europe, Europe actually, I think, punches way below its way in terms of having an AI industry. And so they don't tend to attract as many top-tier AI talent as China or the U.S. And if you look within top U.S. institutions where top AI talent work, it really is almost a Chinese-American duopoly. Chinese origin and American AI scientists are 75 % of the top AI talent within U.S. institutions. 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?

12:03What are the factors that would go into them making a decision? Like, are there immigration considerations? I imagine pay and remuneration would have to factor into that. How easy is it for them to switch from China to the U.S.? I think the skills and, you know, and the training is fairly similar if you come out of a top program, whether it's Tsinghua in China 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 to do your master's or PhD somewhere.

12:41You generally start to search for job opportunities 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 I think where you choose to go to graduate school is really important. And we see that in the data too. Those who come to the United States for graduate school, by and large, tend to stay in the U.S. to work unless there's some very lucrative opportunity that attracts them back home or somewhere else.

13:15But generally, there's a bit of a path dependence between graduate school and staying in that country to work. There has been a lot of anxiety for years in the tech industry where you see CEOs and leaders complaining that the U.S. 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 this U.S. educated talent. It does seem like from your data that still many are staying in the United States, but the numbers have changed since 2020, yes?

13:48Yes, 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 mobility for people, where people work, how people work. So there's going to be a lot of distortions in those last three years, 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.

14:25They've retained 90 percent of their talent. They've not let anybody leave 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 game to retain domestic talent or there's been other things that happened in the pandemic that's triggered it or there could be, you know, immigration challenges and so on. I think maybe in the future when we do it, 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.

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16:25Go to public.com slash market and earn an uncapped 1 % bonus when you transfer your portfolio. That's public.com slash market. Paid for by Public Investing. All investing involves the risk of loss, including loss of principal. Brokered services for U.S.-listed registered securities, options, and bonds in a self-directed account are offered by Public Investing, Inc., member FINRA, and SIPC. Crypto trading provided by ZeroHash. Complete disclosures available at public.com slash disclosures. 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 Ecole Polytechnique and Sciences Po graduates go into AI instead?

17:05Yeah. 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 Damien, I think this factoid was in one of the reading materials that you sent, but Chinese universities have launched more than 2 ,300 undergraduate programs since 2018 when the Ministry of Education designated AI as a separate major. major that's distinct from computer science? So first of all, how common is that that you would get the separation between computer science versus AI? Is that the standard in other parts of the world or is it still relatively new?

17:48And then secondly, presumably this 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? 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 2022. If you started in 2018, some of them are graduate students, some of them are undergrads. So they've really pushed really hard to grow the AI talent. But now not all of them are the top 20%. But I think China looks at it as a way that they're going to need a lot of AI-specific technicians.

18:25China's not really thinking about AI in the generative AI sense. I think there are definitely some startups and folks pursuing things like chat, GBT, chatbots. But my understanding is that China is probably going to focus much more on industrial applications of AI manufacturing, robotics, probably healthcare, biotech. 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 copacetic 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.

19:06That's where China is 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. Yeah, I can't imagine China's ambition here is to have like 5 ,000 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?

19:49Because 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 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 10 years. And that's why you have, you know, really high youth joblessness in China.

20:23The way I think about how China works in that respect specifically is that there are basically two different cycles in China. There is a policy induced cycle and then there's an actual market 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 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 now have an oversupply of a lot of these you know you know middle ai technicians that will have no jobs what are we going to do with them we don't know so i i think this is a pattern that happens in china a lot and And I wouldn't be surprised if it happens with the AI talent pool as well.

21:21So 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, OK, all these new institutions or graduate programs have been launched in China and more and more universities offering degrees in AI or computer science or related fields. In my mind's eye, if I can 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 number of schools that are capable of producing either those top 20 % or top 2 % talent beyond just the sort of like handful of schools that we for a long time understood as the elite schools?

22:16There have been a little bit. And when it comes to Asia specifically and China, I think they have 11 of the 14 top AI institutions in Asia. But in terms sort of, you know, just top in general, China has climbed quite a bit. A place like Zhejiang University, Shanghai Jiao Tong, which are not your traditional names that you would hear. Yeah, I've never heard of either. It's not PKU. It's not Tsinghua. And interestingly, this is interesting you enter into 2022. Huawei is actually one of the top 25 institutions for AI research globally. So they've invested a lot in hiring top AI talent for obvious reasons.

22:54Oh, this is actually exactly what I wanted to ask you next, which is you mentioned Baidu 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 seen here in the U.S., but also that maybe some of those big consumer internet companies, the ones that had a very rough few years during Xi Jinping's big crackdown on disorderly capital expansion, that they're going to pivot as well? So I think that's basically correct.

23:37Baidu, 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? Because everyone's now focused on how to solve language, which is what generated AI. And a lot of the products we see today is kind of language-based. But Vision is a really tough nut to crack. And Baidu is the one in China that's really been trying to solve it.

24:10And I'm not sure their progress is any better than Google or anybody else. But in terms of some of the software companies like Alibaba, Tencent, Tencent has been doing a lot of AI investments and obviously Bydance. 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 invested less in software in the last few years, but actually invested more in sort of hard tech, you know, hardware. So similar things like the advanced manufacturing side.

24:45So 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'll be some startups in China that probably we haven't heard of today that's going to put a lot of money into AI. But the big guys are doing it. But Baidu 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. So going back to the other industrial applications of AI, like already there's this just tremendous anxiety in the U.S.