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Letter from Beijing 2: Tsinghua University – #113

1:20:52EnglishTranscribed Jul 5, 2026
0:00

So I think many people outside of the

0:02

academic system don't really understand

0:05

the rankings. It also has to be split

0:08

between like undergrad and graduate. So

0:11

so without a doubt Chinha undergraduate

0:13

students are the best in the world. I

0:15

would say on on average much better than

0:17

the best universities in the US. But

0:20

this doesn't actually mean that the

0:21

education quality itself is higher. It

0:23

just means this the filtering system

0:25

they can filter better students. But

0:27

then for graduate it's still without a

0:29

doubt that the front like most of the

0:31

frontier research is still in the US

0:33

although China is catching up very

0:35

quickly. That's why we see uh that most

0:38

of the PhD students in the US are

0:40

actually Chinese because they're high

0:42

quality students.

0:47

[music]

0:54

[music]

0:59

Welcome to Manifold. This is a special

1:01

episode. We here at Chinua University in

1:04

Beijing, China. I have four guests in

1:08

the room with me today. They are all

1:10

Americans with some knowledge of this

1:12

university. First, we have Han Feduk,

1:15

who has been a guest before on the

1:18

podcast. We'll link to that episode in

1:20

the show notes. He is a resident of

1:22

Beijing. He is [music] a former banker

1:26

and also a novelist and uh he is going

1:30

to be part of the conversation but he's

1:31

not the main focus of the conversation.

1:34

Welcome to the show Heda.

1:35

>> Hello everyone.

1:38

>> The main guests today are three

1:40

individuals who are actually part of

1:43

Chinua University. First we have Gabriel

1:46

who's an American and who is finishing

1:49

his undergraduate degree here. Gabriel

1:51

say hi.

1:52

>> Hello. Next we have Justin who did his

1:55

undergraduate degree in the University

1:57

of California system but is doing a PhD

2:00

here in AI related research um here at

2:04

Chinua. Justin say hi.

2:06

>> Hi everyone.

2:07

>> Finally we have Alex who is a professor

2:11

also AI researcher here at Chinua

2:13

University. Alex say hi.

2:16

>> Hello. Okay. We're actually recording in

2:19

a research seminar room at the Yao

2:22

Mathematical Sciences Center uh at

2:25

Chinua. And Yao, as many of you

2:27

physicists will know, it's the Yao of

2:29

Calaba Yao manifolds. And he won the

2:32

Fields Medal and is one of the great

2:34

geometers in the history of mathematics.

2:37

We're we're borrowing a room in his

2:39

research center right now. And

2:40

acoustics, I think, might be less than

2:42

ideal, but hopefully my engineers will

2:44

fix this in post-prouction.

2:47

So Gabriel, let's start with you. You

2:50

are finishing your undergraduate degree

2:52

here

2:52

>> in computer science.

2:54

>> Yes.

2:54

>> Tell us about

2:57

your journey to Chinua. Why are you at

2:59

Chinua and not at a US university?

3:02

>> How has your experience been here?

3:05

>> To preface, I think it's important to

3:07

say that I grew up in Hong Kong. I went

3:10

to international school in Hong Kong and

3:13

yeah so most of my classmates they went

3:15

abroad to do a university or to stay in

3:17

Hong Kong. So the main option for Hong

3:18

Kong um Canada, the UK and the states.

3:23

I've also applied to the states during

3:25

my um undergraduate application season.

3:29

In my opinion is it's been trending

3:32

upwards that more people are considering

3:34

mainland universities. And my personal

3:37

reasons were one uh yeah I just one one

3:42

aspect was I have never got an education

3:46

in mainland and also uh like language

3:50

wise I was much weaker in Mandarin. So I

3:53

felt like not only was Singua is Tinua

3:56

really good for the field I'm interested

3:58

in, so computer science, but also

4:01

another aspect of it would be I would be

4:03

able to learn about China as a system

4:05

and just being in that new environment.

4:08

And I think yeah, now four years down

4:10

the line, I definitely see that I got a

4:14

lot out of my college experience here.

4:16

>> Let's start with the basic thing, your

4:18

Mandarin language capability. So

4:21

>> you probably studied it in high school

4:23

through the Hong Kong system. It sounds

4:25

like you feel you weren't fully fluent

4:27

when you went off to college.

4:28

>> Definitely. No, I studied it actually as

4:31

a first language. I did the IB system,

4:33

so international boret. I technically

4:35

got a bilingual diploma. We only kind of

4:37

practiced writing and reading whereas

4:39

spoken Mandarin because the playground

4:42

language in my school was just English.

4:44

So I never really got the opportunity to

4:47

really converse that much in Mandarin.

4:49

That was lacking.

4:51

>> And how was the were the classes you

4:53

were taking here in English or in

4:55

Mandarin?

4:56

>> Oh, when you say chinua

4:57

>> chinua.

4:58

>> Oh yeah, all like 95% of my classes were

5:01

Chinese men.

5:02

>> And was that a shock to you?

5:03

>> Uh yeah, first year was pretty hard.

5:06

That was one of the biggest challenges I

5:08

faced. Uh especially my first year. I

5:10

realized later on that it wasn't nearly

5:12

as big a problem as I thought because

5:15

your language skills improve a lot when

5:16

you start using it daily and I just

5:18

never had that alcohol.

5:19

>> But you could already read.

5:21

>> Yes, but like slowly.

5:23

>> Okay.

5:24

>> Yeah.

5:24

>> Was it an active effort to get your like

5:27

the number of characters that you could

5:29

recognize up to what a typical I mean

5:31

these are some of the top students in

5:33

China, right? This is like the average

5:34

student here is kind of like one in a

5:36

thousand.

5:36

>> Yes.

5:37

>> Talent level. So to preface, I think my

5:39

ability in Chinese was so I could

5:42

converse with people, but it's like when

5:44

I first came it was so awkward to the

5:46

point where they'd say like three

5:48

sentences and I have to think about what

5:50

I have to say. Like I wouldn't be able

5:51

to form like a multi-line dialogue and I

5:56

would actually have to think really hard

5:58

about what I was saying. But I realized

5:59

like two months down the line just

6:01

because it was really hard but after two

6:03

months uh it just got much more natural,

6:06

>> right? And in terms of like lecture

6:09

notes, like if you got some fat set of

6:10

lecture notes all in Mandarin, all in

6:12

Chinese characters, how did you handle

6:14

that?

6:14

>> Yes. So, um, pre GBT era, so that's when

6:18

I first entered college because, yeah, I

6:20

use a lot of translation software, but

6:23

also we just really had to try really

6:25

hard just read it.

6:26

>> Wow.

6:27

>> Yeah. So, we'll we'll come back to this

6:29

with some of the other guests here

6:30

today, but if you compared notes, and

6:33

the ideal case would be someone you went

6:35

to IB high school with in Hong Kong that

6:38

you know well that went to a Western

6:40

university to also study computer

6:42

science. Is there a case like that where

6:44

you can compare notes about what you

6:45

learned here and what the experience was

6:47

like compared to your friend?

6:49

>> When you say compare notes, like

6:50

physical notes or just like talking

6:52

about what we study? just talking about

6:54

what you learned, what the activities

6:56

were like, research projects, just

6:58

comparing your two CS education.

7:00

>> I work on I have a friend at NYU who did

7:02

undergrad for CS and another one at

7:04

Boston. I think course wise, just like

7:06

the courses we took like computer

7:08

networks, computer architecture,

7:10

operating system. So these courses we

7:12

all took separately and we all kind of

7:14

know the basics of it. But regarding

7:15

like opportunities, they're vastly

7:18

different. As in in Tijuana, if you just

7:21

want to dip your feet into research, you

7:24

kind of just text a teacher. It's it's

7:26

that simple. Whereas in NYU

7:28

specifically, I know um it's actually a

7:30

really competitive opportunity and most

7:32

people graduate without even uh getting

7:35

into it at all. Yeah.

7:36

>> So, you were able to get involved in

7:38

research.

7:38

>> Yes. really has a thing called student

7:40

research training and

7:42

>> that's advertised to undergraduates uh

7:44

to just test out the research

7:46

environment.

7:46

>> Yeah.

7:47

>> Got it. Now my understanding from

7:50

talking to CS graduates in the US is

7:52

that generally the hardest courses for

7:55

them are maybe like a senior level

7:58

machine learning class

7:59

>> and because there's a lot of statistical

8:01

methods and stuff like or statistical

8:03

thinking that they're maybe not used to.

8:05

Then also there's usually an algorithms

8:07

class that maybe uses the book by

8:09

Revest.

8:10

>> Those are typically challenging. So

8:12

there are a lot of people who are good

8:13

at at least this is a you know pre-Cloud

8:15

code era but there are a lot of kids who

8:17

are actually good programmers but they

8:19

never really fully understand the

8:21

material in say the senior level machine

8:24

learning class. So they never really

8:25

fully understand all the algorithmic

8:27

thinking which is kind of basically

8:29

discrete math in that course. Do you

8:33

have any experience like that? Like are

8:34

the Chin Hua kids all really good and

8:36

they can master that material easily?

8:37

>> I went through college with AI, right? I

8:40

know first f firstand because all my

8:42

friends are like Chinese. I know I live

8:44

with Chinese people in my dorm. We just

8:47

use AI for everything. I can't say that

8:50

they know all the material like from a

8:53

very basic level.

8:54

>> Yeah. Um I think yeah the AI use and

8:57

like the just understanding the overall

9:00

concept but maybe not like the specific

9:02

detail I think that's universal. Wow.

9:04

Okay. So the the thing I was trying to

9:06

get at is you see at the more elite US

9:10

schools all the CS majors can really

9:12

understand the more mathematical stuff.

9:16

Whereas in a lot of like standard US CS

9:18

schools you might be a good programmer

9:21

but you actually don't understand the

9:22

math. and you and you just like look

9:23

back at your college CS degree and be

9:26

like, oh, that was all useless stuff

9:27

that I don't actually use now working at

9:28

Oracle,

9:29

>> right?

9:30

>> My question was like, oh, would Chinua

9:32

be different because the students are so

9:34

highly selected, but it sounds like

9:35

because of AI that my question is like

9:37

totally irrelevant now,

9:38

>> right? I I get what you mean. I think

9:40

for a lot of the classes, yeah, we were

9:41

forced to learn like theoretical math,

9:43

like the basics, like discrete math and

9:44

stuff.

9:45

>> Yeah, students pick up pick up on that

9:47

like really easily.

9:48

>> Yeah. Yeah, we also had like hard

9:49

machine learning courses and like AI and

9:51

yeah, they got the fundamentals down

9:53

really easily. But I think I was more

9:55

applying as a sense of like since AI

9:57

progresses so quickly and a lot of times

9:59

like these new methods we don't learn in

10:00

class. But regarding those methods like

10:03

for example what GDP has done for

10:06

language models, those are covered even

10:07

in like

10:08

>> basic AI classes.

10:10

>> Regarding those techniques and stuff,

10:12

yeah. Um maybe it's not that Chinese

10:15

people can't understand it, but it's

10:17

just because we didn't learn it in

10:19

class.

10:20

>> Yeah.

10:20

>> Yeah. So we don't really get to

10:22

>> I was just reflecting on my experience

10:23

in industry dealing with a lot of coders

10:26

who maybe often they don't have a CS

10:28

degree, but even if they do have a CS

10:30

degree, these sort of quote hardest

10:32

classes that CS majors take, a lot of

10:34

them basically didn't master it and

10:36

totally forgot it after being in

10:38

industry.

10:38

>> Like they just memorized.

10:40

>> Yeah. they got through the class

10:41

somehow, but they didn't really when you

10:43

start talking to them about it in the

10:44

real world, they don't really actually

10:45

know what you're talking about.

10:47

>> But like I would guess most MIT CS

10:49

majors actually do really master that

10:51

material as undergrads and remember it

10:54

years later. So that's that's the

10:55

distinction I was trying to get at. I've

10:58

never talked to Martin student but like

11:00

I would assume that a lot of us like

11:03

even at the top universities um yeah

11:06

there's a big population that who just

11:08

don't bother understanding classes that

11:09

deeply because really a lot of people

11:11

the attitude in industry is like

11:14

>> yeah we got the degree that's all you

11:16

can

11:16

>> okay

11:17

>> that's that's another thing physics

11:19

physics people would find weird about CS

11:21

because it's got such a career driven

11:23

approach like a lot of the people are

11:24

not as deeply interested in the core

11:27

concepts and part maybe they're just

11:29

looking for like a signaling um kind of

11:32

uh certificate that lets them get the

11:34

job.

11:35

>> Right. Right. I think in computer

11:37

science at least

11:38

>> Yeah. a lot of us just

11:41

the main goal is the degree.

11:42

>> Yeah.

11:43

>> Whereas like the underlying architecture

11:45

it's actually kind of rare to see

11:46

someone that in tune with understanding

11:49

like very uh like lower level

11:52

everything. Yeah. Someone told me that

11:54

uh like one of the best CS curricula in

11:57

the US is the Berkeley one because it

11:59

actually forces you to like actually

12:00

understand how to build the machine from

12:03

you know very low-level considerations

12:05

all the way up and not all the some of

12:07

the top CS programs don't necessarily

12:09

force all the students through that that

12:11

sort of conceptual road map. I mean, I

12:14

know the computer science department,

12:16

one of their for undergrad, their

12:19

operating system class, they're forced

12:20

to like code a virtual CPU from scratch.

12:24

>> Yeah.

12:24

>> And that's considered like one of the

12:26

hardest projects I have to take. I

12:27

wasn't forced to do that because I do

12:28

software engineering.

12:29

>> Yeah.

12:30

>> But yeah, I think if I were forced to do

12:31

that, I Yeah. I would have to understand

12:33

things from the basic.

12:34

>> Yeah. Got it. Let me jump to uh Justin.

12:38

Justin is a graduate student here at

12:40

Shinua and as I mentioned he did his

12:42

undergrad degree in the University of

12:44

California system. He and I actually

12:46

communicated a little bit because he was

12:47

trying to decide what to do for his

12:49

graduate career and I actually just made

12:52

the comment to him that I thought Chinua

12:54

was exceptional in the sense that a lot

12:56

of the there was a lot of research going

12:57

on here. The students are really good

12:59

and also the professors seem to have

13:01

quite a lot of connection to industry

13:03

you know comparable to at least in my

13:05

perception like at Stanford like so many

13:07

professors they and their students are

13:08

involved in startups and my perception

13:10

was that that was true here too. Justin

13:12

I don't know how much influence my

13:14

remarks had but Justin ended up coming

13:15

here and uh you've been here one year

13:18

now. How long have you been here?

13:20

>> I got here last year in August.

13:22

>> So about almost one year. Okay. So tell

13:24

us how your experience has been. My

13:26

experience here at the good I think the

13:28

research environment and the resources I

13:31

got through my advisor are probably one

13:33

of the top ones. So in particular I

13:37

always have access to GUS

13:40

um when I need to run experiments. I

13:41

won't I won't say which ones but

13:43

[laughter]

13:45

>> made by company that starts with M.

13:47

>> Yeah. Or with an H. The best.

13:49

>> Yeah. Best the best. Yes.

13:52

B. Basically, my professor is one of the

13:54

top professors at Ching, which I got in

13:57

because I'm American. And it's also

13:59

great because I get to meet uh and

14:00

collaborate with the best students in

14:02

all of China as well. It's just a it's a

14:04

really great opportunity overall.

14:06

>> So, let me unpack that a little bit. So,

14:09

when you say I got in because I'm an

14:11

American, do you mean you were able to

14:13

work with one of the top professors in

14:15

your department because you're an

14:16

American? Did that play a role in it?

14:18

>> Yeah. So there's two separate systems

14:21

for international students and domestic

14:23

students and even for the Hong Kong,

14:25

Taiwan and Macau residents. So so the

14:29

domestic students um they have limited

14:32

quotas and they have to compete with all

14:34

the other domestic students. So the

14:36

competition is very intense and then

14:38

because there aren't many international

14:39

students applying in the first place, I

14:41

have much less competition. Do

14:43

>> you mean admission into the PhD program

14:45

or the seat in the lab of your adviser?

14:48

Both actually.

14:49

>> Both. Okay. So there's a quota is there.

14:51

Even at the level of like advising

14:52

students, there's kind of a quota for

14:55

international students.

14:56

>> Yeah. Al Alex can speak on this more

14:58

later because he's a professor here. But

15:01

I think for most professors, they have

15:03

um two domestic quota every year and

15:06

then one to two international quota

15:08

every year as well.

15:08

>> Okay.

15:09

>> And then the Taiwan HK Macau, they count

15:13

for international. they are forced to do

15:16

a bachelor's first and cannot directly

15:17

apply a PhD

15:18

>> and whereas you are you already in the

15:20

PhD program

15:21

>> yeah I for international uh since I

15:23

apply as American I can directly apply a

15:26

PhD

15:27

>> okay

15:27

>> I don't have to do masters first

15:29

>> now I have the same question for you

15:30

that I had for Gabriel which is if you

15:33

have a friend who's in the US also at

15:37

the same roughly stage of doing their

15:39

PhD in CS assuming you do have such a

15:42

friend how do you guys compare your two

15:44

experiences

15:46

So, so I can't say for CS, but I do have

15:49

friends uh who did do undergrad at

15:52

Berkeley and then are now doing some are

15:54

doing stats, some are doing physics PhD

15:56

in the US system and I think in the US

15:59

system at least for stats and um physics

16:04

the the advisor relationship is much

16:06

more individual because in particular in

16:09

China it's very common for these

16:10

professors the traditional departments

16:12

to have

16:14

Dozens of students for example one

16:16

professor he has 50 students

16:18

>> 50 PhD students or is it mix of masters

16:21

and PhD

16:22

>> mostly PhD and it's a whole stocks maybe

16:24

like

16:25

>> 30 to 40 PhD that's huge okay yes huge

16:29

and on top of that my professor is also

16:31

running a startup

16:33

>> so basically students aren't aren't even

16:37

able to reach them like we have um funny

16:40

funny story so so I walk to the lab and

16:43

then I saw my senior who's a fifth year

16:45

PhD about to graduate. He's like, "Oh, I

16:47

can't find our advisor." I'm like, "Oh,

16:49

he's teaching this class. Let me send

16:51

you the classroom so you can go find

16:52

him."

16:53

>> Yeah.

16:54

>> So, even the most senior have a very

16:56

hard time like contacting our advisor.

16:59

>> That sounds like a bad thing to me.

17:01

Doesn't sound like you're directly

17:02

learning from the professor. So, are you

17:04

learning from posttos or more senior

17:05

grad students in the group?

17:06

>> Yeah. So, I'm not learning directly from

17:08

the professor. Luckily the senior

17:11

students and the other PhD students

17:12

they're all they're already independent

17:14

research since they've gained those

17:15

skills in undergrad you're working with

17:17

other senior patient students and

17:19

posttos I can

17:20

>> yeah Alex jump in. Yeah.

17:22

>> Yeah. So um the way the traditional

17:25

Chinese professorship works is a little

17:27

bit different. So basically when you

17:29

come in as an assistant professor you

17:32

work under the lab of an associate

17:35

professor. So it's kind of like this

17:38

joint lab. So because your advisor

17:43

Justin is like a full or very high level

17:46

professor, he has some assistant

17:48

professors working under him. So you're

17:50

getting more of the advising from those

17:53

assistant professors.

17:55

And the other thing is um so Andrew Yao

17:59

is one person here who set up a couple

18:02

of departments the triple and the AI

18:04

college which more closely emulate the

18:08

US system where the assistant professors

18:10

are fully independent but most Chinese

18:12

universities have this system. So I'm in

18:15

the computer science department just

18:17

talked about the separate independent

18:19

colleges. So in the traditional

18:22

department such as computer science um

18:24

they have a they have a big professor

18:25

and then they have several professors

18:27

under him in the same lab.

18:29

>> Uh so for example my professor he's the

18:32

end of the lab and then under him there

18:34

are five other professors.

18:35

>> Got it.

18:35

>> Each do different directions.

18:37

>> Got it.

18:37

>> But even then because there's still so

18:39

many students and like one um because

18:41

our lab is now doing embodied AI and

18:43

this one professor has to manage around

18:45

30 students. So it's it's also uh not

18:47

much time to advise us.

18:49

>> This uh system, the traditional one

18:52

sounds a little bit more like the German

18:53

or Japanese system where there is often

18:55

a senior professor and then some junior

18:57

professors and some monster group

18:59

attached to those uh professors. But the

19:03

American system usually assistant

19:05

professors are pretty much independent

19:06

from day one um and generally would have

19:08

much smaller groups.

19:10

>> Yeah, I think that's true. They might

19:12

have taken the system directly from from

19:14

the Germans.

19:14

>> Yeah. So how do you how do you feel

19:17

about your fellow students? Like this is

19:20

many people would say the top university

19:22

in China. Undergraduates are highly

19:24

selected.

19:26

In the past I think it was true that the

19:28

top undergrads from Chinua would all

19:30

come to America or maybe to Oxford or

19:33

Cambridge or something for their PhDs,

19:35

but my understanding is a lot of them

19:37

now stay here. How do you feel about

19:39

your fellow grad students and how many

19:41

of them turned down an opportunity to

19:45

say go or or prefer to be here rather

19:47

than going to some US university for

19:49

their PhD? I would say now there's a

19:52

pretty decent amount because now the

19:55

conditions of China and China are quite

19:57

good for them. There are several top

19:59

undergrads in both YA class and in

20:02

computer science department who say they

20:04

don't want to go to the US even though

20:06

it's still the best because number one

20:08

it's too far they prefer to stay close

20:10

at home or like it's a new environment

20:12

it's uh too much of a change for them.

20:14

>> Yeah. So for example uh one of my

20:16

underassman he's a fourth year uh and he

20:20

uh wants to stay he wants to stay in

20:22

China for PhD and he he already got an

20:24

offer um and there's another one who

20:27

didn't get the offer from the lab so I

20:30

asked Alex to help him refer him to

20:31

another professor he got that offer but

20:33

at first the professor advised him to

20:35

apply to US universities and he really

20:37

didn't want to do he said it was yeah it

20:40

was too much of an ask for

20:42

>> okay some background for the listeners

20:44

So we're recording in the ST Yao

20:50

Mathematical Sciences Institute. So

20:52

that's Y AU. That Yao is a mathematician

20:55

who was a professor most of his career

20:57

at Harvard and then came to China and

20:59

now has set up several research

21:01

institutes here. And he created an

21:02

undergraduate college where the students

21:04

who are in that specially selected sub

21:07

college are typically pursue they're

21:10

going to pursue a PhD in math or

21:11

theoretical physics. There's a guy

21:13

called Andrew Yao who also was a

21:16

professor in the US I think for a long

21:18

time at Berkeley and Princeton um whose

21:20

main field is theoretical computer

21:22

science and he won touring prize. He

21:25

came back to China and has now

21:27

established a special Yao class YAO

21:30

class of some of the most talented math

21:33

and CS students in China. Um so we have

21:37

several very highly selected subpopuls

21:40

on campus. The overall population of

21:42

students here is highly selected, but

21:44

then you have even more highly selected

21:45

groups that like the kids in those

21:47

classes would have been like gold

21:49

medalists at the national level, math

21:51

olympiad or physics olympiad or CS

21:54

Olymp. So, it's a very select population

21:56

of students here. And the question I

21:59

want to ask Justin is if you take one of

22:01

those groups of kids and you say I'll

22:03

look at the top 20 kids graduating in a

22:05

particular year from one of those highly

22:07

selected groups. What fraction are going

22:09

to end up in the US and what fraction

22:11

are going to stay here?

22:13

>> Yeah. So for that STE class,

22:17

>> well they're they're signed up to do a

22:18

PhD here from the beginning. They have

22:20

the same.

22:20

>> Yeah. Let's do YAO class. So the and the

22:23

andreel class the class that class is

22:26

>> uh it it started off with 30 students I

22:29

forget what year back in 2007

22:33

>> but back then 100% of them like every

22:36

single one of them went to the US for a

22:37

PhD or or masters or just went there for

22:40

work and then eventually it expanded to

22:44

almost around 100 students now because

22:46

they combined three different classes

22:47

they combined they had they had three

22:49

directions they have theoretical

22:51

computer science

22:52

They have a quantum computing and have

22:54

an AI direction. Each of them were like

22:56

around 30 students per class. They just

22:57

combined it all. So they've expanded the

22:59

class to around 100 students now. And um

23:03

before co it was still um either 90% or

23:07

100% went to the US for PhD uh or work

23:11

and now it's around 50 50 to 60% go to

23:15

US and then the rest stay in China.

23:17

>> Okay. So that's that's an interesting

23:19

trend, right? and very pronounced of the

23:22

kids and those with that background what

23:24

fraction are actually going to get PhD

23:27

versus they just go and start a company

23:28

or do something in industry right away

23:31

>> yeah so right now or I guess based on

23:34

historical trends maybe we could say at

23:36

least 70% go to PhD maybe even 90%

23:41

>> I'd say 90

23:42

>> wow yeah I don't know if that's still

23:44

true in the US like I wonder what

23:45

fraction of the top say MIT CS CS grads

23:48

like Maybe a lot of them are just going

23:50

out and trying to start a company or

23:52

join a company right away.

23:54

>> Yeah.

23:54

>> Yeah. Or join one of the big AI labs.

23:56

>> The Y class kids don't um go to PhD

24:02

before they would just go to quant like

24:03

what called the MIT.

24:06

>> I think more recently there are some

24:07

that go to the top labs like deepseek or

24:10

bite dance but it's still the most

24:12

common route for them to do PhD in the

24:15

US or continue PhD in China. But it

24:19

seems to be they're they're starting to

24:21

change perception as well. Now many of

24:23

the the fourth year undergrads in Y

24:25

class are thinking that oh PhD is

24:27

probably there's no point to do it

24:29

anymore because they've already

24:30

published so much as undergrads. They

24:31

have a PhD they don't need another one.

24:33

>> Right. Just to elaborate on that for the

24:35

audience. So through you I've met a

24:36

bunch of these kids and a lot of these

24:38

kids they're a senior in college and

24:40

they published a handful of papers

24:41

already or they've been one of the first

24:43

or first few authors on a number of

24:46

papers already. So they might even be I

24:48

I don't know what the typical

24:49

expectation is for us PhD students how

24:52

many papers they should have published

24:53

but but these kids seem very

24:55

accomplished to me.

24:56

>> Yeah. If we were to put a number on how

24:57

many papers a PhD program would accept

25:00

it's around three papers at top

25:02

conferences and most of them they like

25:04

have way more than three papers.

25:05

>> You're saying a lot of them hit that

25:06

when they're undergrads.

25:07

>> Most of them both first authors as

25:10

>> All right let me jump to Alex. So Alex

25:12

you are the professor here. uh you work

25:15

on AI and robotics. Um tell us a little

25:18

bit about your academic background and

25:21

how you ended up here.

25:22

>> Yeah, I'm uh Alex. I did my PhD in

25:27

Montreal. I guess as I was graduating, I

25:30

was interested in both uh industry and

25:32

faculty positions.

25:34

>> Can you just say you have a famous PhD

25:36

advisor?

25:37

>> Yeah, it's Benjamin. Yeah. I was

25:38

interested in faculty positions and I

25:40

had a co-orker from Microsoft research

25:43

Asia so that's in Beijing um who uh took

25:48

a faculty position at Pain his name is

25:50

Dha and I told him I was you know

25:52

serious about becoming a professor in

25:54

China and he said if you want to do it

25:57

uh first you know use the connection to

26:00

establish that you're serious and then

26:02

he said if it's a department uh headed

26:04

by Andrew Yao um this is the place to

26:08

Sorry. And if that doesn't work out, you

26:09

know, we could look for something else.

26:11

And it did work out. And I ended up

26:14

coming here to College of AI at Chinua.

26:17

I've been here for about a year and I

26:20

really like it so far. So I have or I

26:22

guess like five incoming PhD students at

26:25

my lab. I think they're really great.

26:28

Um, as Justin I guess alluded to, when

26:31

they come in, they typically have mem

26:35

paper. So they have some other kind of

26:37

substantial accomplishments and uh I

26:40

also have a have a pool of interns here.

26:43

So I would say the the energy here is

26:46

really really good. Like it's you're

26:49

constantly getting people who like want

26:51

to work on things or want to start

26:53

projects. A lot of students will start

26:56

really ambitious research projects even

26:59

their freshman or sophomore year.

27:02

>> Yeah. My impression from being here a

27:05

little bit is it's a super high energy

27:06

place. It reminds me of places like

27:08

Caltech and MIT where people really want

27:10

to do stuff and there's just tons of

27:12

talent just kind of floating around. One

27:14

thing I that did surprise me a little

27:17

bit which I think is kind of interesting

27:19

is I feel like you have a lot of

27:21

students here who who could do just very

27:24

purely theoretical CS or physics

27:27

research but they do like empirical AI

27:32

or kind of like deep learning type

27:34

research. Uh whereas I feel like maybe

27:37

that's less common in the US unless it's

27:39

changed recently because I feel like in

27:41

the US at least when I went to school

27:44

maybe there was a little bit of a

27:46

perception that like if you could just

27:48

do purely theoretical research you

27:50

should do that to like use that

27:52

comparative advantage. I don't even know

27:54

what you think about that.

27:55

>> The parallel in physics would be some

27:57

kids are going to be theoreticians and

27:59

some kids are going to be

28:00

experimentalists working in the lab. And

28:02

the working in the lab is a little it's

28:03

it's obviously it's more empirical and

28:04

it's a little more like in the neural

28:06

yeah in the nitty-gritty details and yes

28:10

there is some kind of like

28:11

classification like some kids end up in

28:13

one bucket and the other

28:14

>> and but sometimes like the very best

28:18

this is just physics experience. The

28:20

very best experimentalists are kids who

28:22

have the chomps to do theory

28:24

>> but they also have the hands to go in

28:26

the lab and really do stuff. And those

28:28

people are the ones who really can push

28:30

a field forward. Yeah. But so so there

28:32

may be an analog to that in CS.

28:35

>> So yeah, I have a comment about theory

28:38

in China versus the US. So when I was in

28:40

the US, I was more involved on the

28:43

theory side on AI as well in the math

28:45

and stats departments

28:47

and through [clears throat] that I uh I

28:50

got to know a ton of Chinese students

28:52

because actually most math departments

28:54

and stats departments even CS

28:56

departments most of most of the nation

28:57

students are Chinese and there seemed to

29:00

be a common consensus that the

29:02

theoretical research in the US was much

29:05

more mature in China. So even if there

29:07

are students in China who wanted to do

29:09

theory um they would have to go to the

29:12

US to do theory to do frontier theory

29:14

research or if they wanted to stay in

29:15

China then they will work on more

29:17

empirical research or engineering

29:18

research.

29:19

>> So theory in the US is ahead of theory

29:21

in China. Is that what you said?

29:23

>> Much like uh by quite a lot.

29:25

>> Okay.

29:26

>> Which is a common consensus among all

29:27

the Chinese researchers and ST even said

29:31

like in a in a speech that China is

29:32

behind by 40 years on mathematical

29:36

research research.

29:38

>> Good. Alex, I wanted to drill down on

29:41

what why were you interested in becoming

29:42

a professor in China?

29:45

Well, I guess I just felt like okay,

29:48

objectively here I can get really strong

29:51

students. I also feel like uh the

29:55

overline sorry the underlying economic

29:58

strength in China like the industry is

30:00

very good and I feel like eventually you

30:03

know it will support like world class

30:06

academics and you know I think it's

30:07

already kind of starting to happen but I

30:10

think there's a lot of room for growth

30:11

in the academia in China. I mean, I

30:13

guess also even just as a little kid

30:15

living in China, working in China was

30:17

something I was interested in doing. So,

30:19

the fact that I got a good opportunity

30:21

to do it is pretty exciting.

30:24

>> I mean, I definitely feel like here

30:25

there's a can do spirit and a kind of an

30:27

upward trajectory to everything. Whereas

30:29

in the US, it's like, can we like

30:32

maintain our position? Are we just

30:34

declining a little bit? It's just very

30:36

different uh vibes in the two places.

30:39

How do you feel about like some of these

30:41

crazy college rankings like US News and

30:43

stuff they're already putting Chinua

30:45

like ahead of all the other like

30:48

engineering like I think like in the

30:49

engineering ranking for universities

30:51

like some of these crazy rankings have

30:53

Chinua number one in the world. Is that

30:55

crazy or is it is it is it reasonable?

30:58

Well, here's one way I might think about

31:00

it. So, you know, if you bracket people

31:03

by like their age or seniority, I think

31:06

if you take the people who are like,

31:07

let's say, 20 to 30, I think at Chinua,

31:10

they're as strong or maybe even as about

31:14

as strong or maybe even a little

31:16

stronger than the best US universities.

31:18

But then as you kind of go towards the

31:21

more senior cohort like the people who

31:24

have like 30 or 40 years of experience I

31:27

think if you compare that cohort in the

31:29

US to China I think we don't have as

31:32

many like extremely senior extremely

31:35

experienced people and I think that also

31:38

goes to what Justin was saying about our

31:41

theoretical research being a little bit

31:44

less mature. I think that's an area to

31:46

work on.

31:47

>> Yeah. In terms of what my students say

31:50

in terms of their preference rankings,

31:52

they usually say like the top five

31:55

schools in the US are like pretty

31:57

competitive with Shenua. So like I think

32:00

it's like Harvard, MIT, Princeton,

32:02

Berkeley, Stanford roughly that pool.

32:04

Yeah.

32:05

>> But I think below that it's pretty

32:07

widely agreed that generally

32:09

>> should want someone preferred.

32:11

>> Got it.

32:11

>> I think they swap out Harvard for CN.

32:15

They call it the big four. big four big

32:18

four CMU MIT Stanford Berkeley and then

32:21

now you add in

32:24

>> NLP you see for like five

32:26

>> I think it'll depend a little bit on the

32:28

area because in my opinion if you want

32:30

to do certain things like embodied AI I

32:32

think you could still make the case for

32:34

Shinguan even over those universities

32:37

just because I mean you have so much of

32:40

a robotics industry here compared to the

32:42

US

32:43

>> that's just my opinion

32:45

>> by the way my my experience

32:47

in having been a physics researcher for

32:49

a long time now is that yes the older

32:52

generation you would very seldom find a

32:55

truly world class guy here because most

32:58

of those guys would take if they could

32:59

get jobs in the US or elsewhere they or

33:02

you mean in Europe they would go there

33:03

but it's the younger group where you're

33:05

starting to see really world-class

33:07

talent that stays here

33:09

>> I think one of the tipping future

33:10

tipping points will be when they feel

33:12

confident that they can really fully

33:15

compete without relying on people who

33:18

went out to the US and got their PhD or

33:21

post-doal training and came back that

33:24

can fully rely on the people that are

33:25

just trained 100% within China. I think

33:28

famously like the of the deepseek

33:29

authors like in one of their early

33:31

papers that caused like the deepseat

33:33

moment. I think all of those people had

33:35

been like 100% trained in China or

33:37

something like that. So that was like

33:39

maybe one of the first indications of

33:40

that tipping point.

33:43

I would say for the professors who we

33:46

hire in our department, I think the

33:49

majority have done their PhDs overseas

33:53

often in the United States at good top

33:56

universities, but some of them also done

33:58

their PhD here in China.

34:00

>> Yeah. So, I think that's the that's the

34:02

tipping point that we're crossing now.

34:04

>> I think it's just a joke, but they

34:06

called the um the domestic PhD student

34:09

the tuber, right? about

34:14

the two in two is a

34:18

rough country bump.

34:19

>> Can you say it again? What was the

34:21

meaning?

34:22

>> Two means dirt. Two means dirt. Like a

34:24

farmer farmer and a young

34:29

international type sophisticated

34:32

which is

34:34

the redneck boss of China.

34:36

>> Yeah.

34:37

>> I think it's just a joke. Yes.

34:39

>> And it'll change over time. Yeah.

34:41

>> So this was before my time in

34:44

theoretical physics. But there was a

34:46

moment. So Oppenheimer was the first

34:48

American who really learned quantum

34:50

mechanics.

34:51

>> So at the time when Oppenheimer, if you

34:52

remember the biopic, which was really a

34:55

really good film,

34:56

>> he had to go to Europe actually to get

34:58

that education. And when he came back

35:00

from Europe, he was the only guy, he was

35:02

the only American professor who could

35:04

really teach quantum mechanics at the

35:05

frontier level. and he started the first

35:08

school of real really u mature school of

35:12

theoretical physics and he literally

35:13

split his time. He would spend half the

35:15

year at Berkeley and half the year at

35:17

Caltech because he was so in such

35:18

demand. They wanted him in both places

35:21

and that was so that was like just prior

35:23

to World War II and that was when

35:25

America was just like nowhere in terms

35:27

of cutting edge science. But then we

35:29

rapidly just one more generation we

35:32

caught up and went to the lead. Yeah.

35:33

>> Yeah. Yeah. I I had heard the same thing

35:35

that for a while there was a perception

35:38

that like okay the US has good industry

35:40

they can do good appliance stuff but if

35:42

you want to do like theory or basic

35:44

research you got to be in Europe is 100%

35:47

analogous to the current situation so

35:49

the experimentalists in America were

35:50

good they could get stuff working we had

35:53

the industrial revolution we had become

35:55

by then the number one industrial power

35:57

but in terms of the highle theory we did

35:59

not have it and it almost it seems like

36:02

there's a very paralle whole thing going

36:04

on right now with China obviously the

36:06

rest of the world.

36:08

>> Yeah, I have a comment about the

36:09

rankings. So I think many people outside

36:12

of the academic system don't really

36:14

understand the rankings. It also has to

36:17

be split between like undergrad and

36:19

graduate. So so without a doubt

36:23

undergraduate students are the best in

36:24

the world. I would say on on average

36:26

much better than the best universities

36:29

in the US. But this doesn't actually

36:31

mean that the education quality itself

36:32

is higher. I just use this the filtering

36:35

so they can filter better students. But

36:37

then for graduate it's still without a

36:40

doubt that the front like most of the

36:41

frontier research is still in the US

36:44

although China is catching up very

36:45

quickly. That's why we see uh that most

36:48

of the PhD students in the US are

36:50

actually Chinese because they're high

36:52

quality students. Do you get the feeling

36:54

that we're turning back to Gabriel now

36:57

who is just finishing up his

36:59

undergraduate degree. Do you feel like

37:01

the competition you had to deal with in

37:03

the last four years here is pretty much

37:05

the toughest CS competition at any

37:07

university in the world average level?

37:09

>> Yes. Every single one of my classmates

37:11

are they're brilliant, right? Because

37:12

they had to go over the gal filter and

37:14

they were at the top in the province. Um

37:17

but you made a comment during lunch

37:18

about um what you think about once you

37:21

get past this gal filter, does this mean

37:23

that they're just going to be the top of

37:24

their class at? Relatively speaking, I

37:27

did the IV. I was good at the IV. I

37:30

wasn't top one in all of Hong Kong,

37:33

right? But that if if you go according

37:36

to your original theory, that would

37:38

imply, you know, last place in my

37:40

cohort. Honestly, that's what I was

37:42

expecting. But that's not what ended up

37:44

happening. As in my grade right now,

37:46

even after having to acclimate myself to

37:49

the Chinese environment, I'm like smack

37:50

in the middle, like 50%. My take on this

37:52

is yes, they're all brilliant students,

37:55

but a lot of them, they're like much

37:58

more acclimated to the testing

37:59

environment. And the main issue for them

38:01

is not that they can't get their heads

38:04

around class work, but rather it's like

38:07

social skills or just like really

38:10

self-disipline. The really really

38:12

impressive people uh Chinese people are

38:15

the ones who aren't just smart cuz they

38:17

all are, but they're like socially adept

38:19

as well. Like once you get both of those

38:21

Yeah. those are what you get as like the

38:23

top PhD students that say in the go run.

38:26

>> Yeah. I mean that some of these

38:28

superstar kids that Justin you

38:29

introduced me to last time I was here.

38:32

Those kids clearly like they're clearly

38:34

smart. They've done a bunch of research.

38:36

They've published a bunch of research as

38:38

undergrads and but when you interact

38:39

with them they're pretty polished. Like

38:41

you could tell like and some of those

38:42

people had already like raised money for

38:44

their startups even their they were like

38:46

seniors in college and they had already

38:47

raised money. Now, now part of that

38:49

ability to raise money here is I think

38:51

the investors here still have quite a

38:53

lot of respect for academia. And so like

38:55

they'll just like write a check. I think

38:56

it seems like they'll write a check

38:58

because you're some big professor at

39:00

Chinua or some big professor at Chinua

39:02

is saying this is my best student and

39:04

the the venture investors will just

39:06

write a check. It's a little bit

39:07

different in the US. Maybe that would

39:09

happen like around Stanford or

39:10

something. But but even then like the

39:12

VCs are thinking more about like can

39:14

this guy actually run a business? Are

39:16

they a hungry entrepreneur? I don't

39:17

really care what this old professor

39:19

says. It it seems like there's a slight

39:21

difference in the cultures. I don't know

39:23

that you would necessarily know about

39:24

that, but that that seems that's my

39:26

impression.

39:26

>> Yeah, I have some interesting comments

39:28

to say about that. So in particular

39:31

about um the respect for academia or

39:35

academics in China that's actually a

39:37

major reason why most of the students

39:39

even the top students they actually

39:40

pursue PhD instead of like going into

39:42

industry or doing a startup is because

39:44

of the prestige of uh doing a PhD purely

39:47

because of the social prestige. The

39:49

issue is whether um it's because of that

39:51

respect for academia that one of the

39:54

best routes here to get venture funding

39:57

is to distinguish yourself in academia

39:59

and have some big professor say hey this

40:01

is my most promising student give him

40:03

money for a robotics startup

40:05

>> I think that happens I think that sort

40:08

of happens a little bit like around

40:09

Stanford or something because you have a

40:11

confluence of one of the top CS

40:13

departments and like lots of bags of

40:15

money just around but I don't think it's

40:17

actually true at like Caltech or MIT or

40:19

some of these other places like you

40:20

could be like a really awesome student

40:21

but there isn't necessarily a bag of

40:23

money that's just like tossed your way

40:24

for being really good at academic stuff.

40:26

>> Okay. I I remember what I wanted to say

40:28

about that as well. So so in ch in

40:30

Chinese society among ordinary people

40:32

there's also like an explicit worship of

40:34

a chinua and ping university

40:37

undergraduates

40:39

um because of how hard it is to get in.

40:40

So like I I had a friend I made um in

40:43

the US. He's doing a math PhD. I met him

40:45

at a conference in the US um and he's

40:48

doing a math PhD at the University of

40:49

Bon and Jurian and he came back to

40:51

Beijing to visit and I invited him here

40:54

and when he came here he was like a how

40:56

do say he was praising the undergraduate

40:58

students of Chinua he's like so amazed

41:00

he's like oh like we treat these people

41:02

as gone in China

41:04

>> the social prestige of Chinua and Ping

41:07

University graduates is at the very top

41:09

>> yeah I I mean I would almost guess that

41:12

if your if your prestige that you derive

41:14

just from being an undergrad here is

41:16

enough. Then you wouldn't feel like you

41:17

have to get the PhD to pat it. Right?

41:19

Back in my day, this is totally

41:21

different era. But there was a time when

41:25

Harvard, MIT, and Caltech were by far

41:27

the most prestigious

41:29

undergrad degrees to have. And it used

41:31

to be set among people at those schools.

41:33

Like these are the only schools where

41:36

you just need that undergraduate degree.

41:38

If you just say, "Hey, BS Caltech or BS

41:40

SB MIT or you know, whatever." Harvard,

41:44

you know, suma at Harvard, that's it.

41:46

You don't need to go get your PhD

41:47

because people just assume you're

41:48

smarter than most PhDs, actually. I

41:50

think that's all gone now. That's like a

41:52

bygone world of 50 years ago or

41:54

something. But you could end up like

41:55

that in China, too, I think. So, so you

41:57

could have more and more kids who are

41:58

coming out of Chinuan. They're just

41:59

like, I'm just gonna start my company.

42:01

I'm not going to go to like live in

42:02

Pittsburgh at CMU for four years and you

42:05

know just to and write some more papers

42:07

just to get a PhD behind my name.

42:09

>> Yeah. So I have some more interesting

42:11

comments about that. So on on the

42:13

startup side instead of doing a

42:15

undergraduates

42:17

um what they do now more practically

42:19

speaking is they they just continue

42:21

doing PhD and then while they're doing

42:24

their PhD their PhD they can pull

42:26

funding by using the name of a PhD

42:29

student and the procedure of their

42:30

adviser to attract funding and do the

42:32

startup and secondly like oh why not

42:35

just quit why I still do PhD I think

42:38

that's I think that's something quite

42:40

common in Chinese culture They they like

42:42

to get the next best.

42:44

>> Yeah.

42:44

>> They continue to strive or

42:46

>> compete in the rat race.

42:48

>> Yeah.

42:48

>> Yes.

42:49

>> It's it's nature. It's evolution.

42:51

>> Like Yeah. Because um Yes. Even though

42:54

they have the team undergrad, but like

42:56

since year one as an undergrad, they

42:58

were just thinking, "Holy crap, we got

42:59

to get this. We got to go get the

43:00

masters." And to get the masters, you

43:02

have to be like top 60% of your

43:03

undergrad class. And that's all they

43:05

thought about like three years.

43:07

>> Wow. Wow. So it's like always there's

43:08

always one more hoop to jump through or

43:10

one more

43:11

>> and the reason is not even because of

43:12

like oh once I get the masters then I

43:14

can get a better job is because oh look

43:16

that's what everyone else everyone else

43:17

is doing mine doesn't look through as

43:19

well you know.

43:20

>> Wow

43:21

Alex you're

43:22

>> I also see a lot of students who I feel

43:24

like can kind of uh hustle around the

43:27

noun. Is that how you say?

43:29

>> Yeah.

43:30

>> Yeah. So like even if they did badly on

43:32

the GA and they went to a lower ranked

43:34

undergrad, they still work hard, get

43:36

some good papers, go to a top PhD or go

43:40

to a good company. So I don't know. I

43:43

don't feel like it's the end of the

43:44

world for everyone.

43:47

>> And I feel that's healthy. Like, you

43:48

know, back in the day, the way we used

43:50

to say it in the US is you could be a

43:52

total [ __ ] in high school, but you

43:54

could still go to a community college

43:56

for a couple years and learn, you know,

43:59

calculus and physics and stuff and then

44:01

transfer to a pretty good state

44:03

university, transfer to the University

44:04

of Illinois, which is actually really

44:05

good engineering school, and then like,

44:07

so there's no there's no point at which

44:09

you're totally out of it, you know? or

44:11

even if you didn't have a good

44:12

undergraduate degree, you're like, "Oh,

44:14

but let me go work at Loheed Martin, and

44:16

if I'm really good at my job, I can

44:17

still make my way up." So, I think it's

44:19

healthy to give people many ways to

44:23

succeed, even if at one particular stage

44:26

at age 18, they were not up to snuff.

44:28

They were playing too many video games,

44:30

but you can you can still make it up at

44:32

some other stage. I think that's just

44:33

healthy. I would be alarmed if like

44:35

there was no way to the top except by

44:38

jumping through all the perfect hoops,

44:40

you know, at every stage of your life.

44:41

That would that would probably that's

44:43

was related to my question at lunch

44:44

today that I was asking is like is like

44:47

it are there kids who were not quite as

44:50

distinguished when they finished high

44:51

school but managed to get in here but

44:53

they still turn out to be the top kid

44:55

when they're actually allowed to do

44:57

research or something like that. So that

44:59

it's healthier if that's possible.

45:01

>> Yeah. So that that's definitely

45:02

possible. My my lab mate that we met

45:04

today is literally one of them. Yeah.

45:06

>> He's like the top he's the top

45:07

researcher

45:08

>> in our lab as a first year and also

45:10

published so many papers as an undergrad

45:11

and he didn't go to Chinua or Pi.

45:13

>> Yeah.

45:14

>> Yeah. But but actually the perfect coup

45:17

right now in Chinese society is not like

45:19

Chihuahua undergrad to Chihuahua PhD.

45:21

It's it's do Chinua undergrad and then

45:23

you go to MIT you stand for Berkeley for

45:25

PhD.

45:26

>> Yeah.

45:26

>> And then and then you stay there

45:30

>> and then eventually come back. or not.

45:33

>> Yeah, I think most of them don't want to

45:35

come back. So, so it's like in China,

45:36

the perfect group isn't to go from like

45:39

uh like a you do you perform really bad

45:41

in high school and then you go to a bad

45:43

university and then you go to Chinua.

45:45

It's like they actually prefer you go

45:46

like to the US at the end. That's that's

45:48

still like a perception Chinese society

45:51

that's the optimal path.

45:53

So I my purpose of having this

45:55

discussion with you with you guys here

45:57

is that like for most people in the US

45:59

they're even people who are in technical

46:01

subjects or academia they have this

46:03

sense that like US is competing with

46:04

China and it's it's a serious

46:06

competition the Chinese have their

46:08

strengths and the Americans have their

46:09

strength and maybe they are starting to

46:11

beat us in some important ways but very

46:14

few Americans understand like what is a

46:16

Chinese university like what is the

46:18

talent selection system in China what is

46:21

the preference stack back of a

46:23

22-year-old very bright Chinese kid.

46:26

Like I think most Americans don't

46:27

understand any of that and that's what I

46:29

was trying to elaborate. Is there some

46:32

aspect of our discussion that I didn't

46:34

cover that you think the audience the

46:37

manifold audience would you know be

46:39

informed by if we discussed it? Any

46:41

anything that you think uh we didn't

46:44

cover in that bundle of stuff that we

46:46

should talk about? I think we should

46:49

emphasize more just the hoops like

46:50

firstly how to get into undergraduate

46:52

but actually before that how to get into

46:54

high that's a good point that I never

46:56

really thought about in Hong Kong. So,

46:58

first of all, to get into high school in

47:00

China, you have to take the phone call.

47:02

So, the middle school test.

47:02

>> Yes.

47:03

>> And if you [ __ ] that up, 50% of people

47:06

[ __ ] that up. Not [ __ ] it up, but like

47:07

just don't do that.

47:08

>> They just don't get into a top high

47:09

school.

47:09

>> Not even know high school. They don't

47:11

get into high school. They go to a

47:12

vocational training school. And when you

47:14

get into vacation training school, you

47:15

don't even take the dog and you just go

47:17

directly to what they call a dun where

47:19

you learn like practical skills like

47:21

factory work and stuff. So once you're

47:24

on that track, it's kind of impossible

47:26

for you to jump back into academia.

47:28

>> Yeah.

47:29

>> Let's say you do get into high school,

47:30

then you take the G call and went to G

47:32

call. Um yeah, you still have to be good

47:35

enough to even get to a university, not

47:37

even. So they separate them based off.

47:40

So there's vocational schools, there's

47:42

barb, there's even, and then if you're

47:44

in the top 5% that's a 211 and then if

47:47

you're in the top one, two% you're 95.

47:51

And then 95 to 30 something of them. And

47:54

then the top two are they covered up.

47:56

>> Okay. Just to elaborate. So nine they

47:58

have these are classifications

48:01

of universities.

48:02

>> Yes. Four year

48:03

>> right. And four year universities. And

48:04

if you score top 5% on the gao

48:07

>> around yeah depending on how

48:09

>> roughly you can get into one layer.

48:11

>> You can get to the 211s.

48:13

>> Okay.

48:13

>> Lesser than a 95 when it's still

48:15

considered a like that good school.

48:17

>> Okay. But the next layer which is how

48:19

many schools are in the next layer? 95

48:21

is like 30 something.

48:22

>> Okay. So, top 30ish universities in

48:25

China.

48:25

>> Yes.

48:26

>> Roughly speaking, all the kids getting

48:27

into those are top 1 to 2% on Galo.

48:30

>> Which is actually already crazy because

48:33

on the US exams like the ceilings are

48:36

usually only like

48:38

>> well you the abs if you actually get a

48:40

perfect score on the SAT it's like few

48:41

per thousand kids or one per thousand

48:44

kids. But you're you're getting toward

48:45

the ceiling of what the American system

48:47

can resolve just to get into one of

48:49

these top 30 schools in China or at

48:52

least to get into Beijing or uh Chinua

48:55

University. Right. So this the system

48:57

here is very very elitist, right?

49:00

Elitist and meritocratic based on one

49:02

test.

49:03

>> Yes. Exactly. So most people get into

49:05

university based on alcohol. Yeah.

49:07

>> Yeah. And then I think for grad school

49:10

applications it's so if you're at a

49:12

great university already um there's

49:14

actually two systems. One is based off

49:16

your undergraduate GPA if you're good

49:19

enough you get to B yet so stay in the

49:21

school or or actually you can v to other

49:24

schools. So let's say you're like some

49:25

other 95 if you're really good at top

49:27

one of your school and you get to go to

49:29

bed. um or if you're already in that

49:32

good enough and the other system is

49:34

called and so that's more the social

49:36

mobility part of it and that's basically

49:39

we don't even look at your undergrad

49:40

like GPA you can think of it like

49:42

another call for after your undergrad

49:45

degree but that's like that's extremely

49:48

hard as well but because less people

49:50

take it they consider that's why they

49:52

consider master degrees like less

49:54

prestigious than undergrad degrees but

49:56

yeah if you're not from like your top

49:59

university for chin boiling to the best

50:00

universities you can technically take

50:02

bats but even better ought to be like

50:04

top five% to even be considered

50:07

>> okay so there's a GPA based and also a

50:10

further exam based way to get into the

50:12

graduate programs here

50:14

>> maybe that's enough about like this not

50:16

everybody's actually that interested in

50:17

like what the hell the minutia of like

50:19

the Chinese academic system

50:20

>> let's talk a little bit about the about

50:23

competitiveness

50:25

between China and the US right which is

50:28

a topic that's often we often discuss on

50:31

this podcast, but from what you guys can

50:34

see within what you could argue is a top

50:38

university in China or at least the top

50:39

technological university in China and

50:41

the companies around it here at Beijing.

50:44

Any observations about where you think

50:47

US China competition is going like the

50:50

technology and AI anything? Yeah. One

50:52

thing is I think the US is really being

50:57

held back by its lack of highquality

50:59

infrastructure because I think you have

51:02

a lot of universities in the United

51:04

States and they're held back by the fact

51:07

that they're not located in a city with

51:10

like top tier industry. So like you have

51:13

Carnegie Melon top computer science

51:15

school but it's in Pittsburgh.

51:17

um UIC,

51:19

Illinois,

51:21

um uh I guess Georgia Tech is in

51:24

Atlanta. And I feel like, you know, in

51:27

China because you have the highspeed

51:29

rail, it feels like you can get to any

51:31

city within a day and just saying like,

51:34

"Hey, go to Shanghai or go to another

51:36

city," that's like nothing for me. But

51:38

if I have to, you know, take a flight,

51:41

that's kind of like a big trip. So I

51:43

feel like you have a lot more like

51:45

interc city collaborations in here and I

51:48

feel like more of that talent is

51:50

unlocked. So I do think in the US

51:54

there's a risk that um it's really going

51:58

to become overly dependent on like just

52:00

the Bay Area and maybe maybe just New

52:04

York or Boston. Yeah. there's a chance

52:06

that if that Bay Area companies lose the

52:10

really competitive edge, the US might be

52:12

in serious trouble,

52:14

>> right? So, let's break that down a

52:15

little bit. So, here they have a

52:18

high-speed rail system,

52:19

>> which is awesome. And I don't think

52:21

people really appreciate in the States

52:23

what that means. So, like, you know,

52:24

like every hour or every 30 minutes,

52:28

like there's probably a train,

52:29

high-speed train between here and

52:30

Shanghai.

52:31

>> And you get on the train, it's not

52:34

stressful. Well, it's not like the

52:35

airport where you got to go through. I

52:36

mean, there's a little bit of security

52:37

like they are. You do put your bags on a

52:39

conveyor belt at one point,

52:41

>> but it's you you get to the train

52:43

station like not you don't have to get

52:45

there hours ahead, right? You can kind

52:46

of cut it a little tighter.

52:48

>> You get on the train, it's super

52:50

comfortable. There's even a business

52:51

class car where you can lie flat if you

52:53

want, take a nap.

52:55

>> You get there, you're arriving at the

52:57

city center of the other city. So just

53:00

doortodoor

53:01

um you have access to many other cities

53:04

with five million plus people within

53:06

like a few hours of here and you could

53:09

do it as a day trip. You could go there

53:10

and come back and that just makes it

53:13

easier for you to collaborate amongst

53:15

cities. And do you think though that

53:18

means there's less of a concentration

53:20

cuz I still feel like even in China like

53:22

most of the tech is okay around here

53:26

where we are

53:27

>> a lot of the big a lot of the best most

53:29

promising companies are here there's a

53:31

bunch in Shenzen

53:33

>> there's some in Hanzo because Alibaba is

53:35

there and Deepseek is there and then

53:37

Shanghai I guess Shanghai is SMIC but it

53:39

is still pretty localized. Is there a

53:41

sense of like these other these cities

53:43

that most Americans wouldn't be that

53:45

familiar with? There's there's plenty of

53:46

high-tech activity there going because

53:48

of this infrastructure.

53:51

>> That's an interesting question. I mean,

53:52

I wouldn't say Hung Joe is actually

53:55

decently far from Shanghai.

53:56

>> It is. Yeah. Well, about an hour on the

53:58

highspeed rail. Yeah.

53:59

>> Yeah.

54:00

>> Yeah.

54:00

>> So, I have some comments on that. So in

54:02

particular to to promote more even

54:06

tempor technological development across

54:08

China's geography the government has a

54:10

national plan called uh east data west

54:14

compute where they build data centers

54:15

out in the western

54:16

>> provin where there's lots of solar as

54:18

well

54:18

>> a lot of solar a lot of cheap land cheap

54:20

electricity bunch of subsidies there to

54:22

provide more jobs and development

54:24

>> and and also for national security

54:27

reasons um uh because ship manufacturing

54:30

is such a capital intensive industry

54:32

before it was mostly concentrated in

54:34

Shanghai, the government actually

54:36

forcefully moved some to the central

54:38

regions in China such as Shandu um to

54:41

develop word chip uh chip industry

54:44

there. So Shandu is also considered a

54:47

develop tech hub there although not in

54:49

AI but in chip manufacturing military

54:51

technology. So I mean aside from

54:54

infrastructure I think that the

54:55

government because a government things

54:57

are more stateled here than they are in

55:00

the states in the US. If it's stateled

55:03

then they might say like yeah let's

55:04

encourage this industry to be in Chandu

55:07

or let's encourage this industry to be

55:09

in Chongqing or something like that. So

55:11

it does get spread out more than just

55:12

letting like everything like the free

55:14

the market maybe just concentrates

55:16

everything in the Bay Area and the

55:17

government doesn't try to do anything to

55:19

counter that. here that would maybe try

55:21

to do something to spread it out more

55:23

and maybe that's the thing that's

55:25

manifested here.

55:27

>> Yeah.

55:28

>> Yeah.

55:29

>> There's another thing which really

55:32

confused me before I came to China but

55:35

which I just started to understand which

55:37

is if you look at like the total market

55:40

cap of let's say the Chinese companies

55:44

it's so much smaller than the market cap

55:46

of the American

55:48

>> companies tiny. Yeah. But then if you

55:49

look at like measures of productivity

55:52

like what they actually produce the

55:55

Chinese companies seem to produce more

55:57

in the aggregate depending on how you

55:59

measure it but they definitely do.

56:01

>> So I think what's going on you guys can

56:04

let me know if you agree but the market

56:06

cap it's kind of like an integral of the

56:10

future profits like the future dividends

56:13

they return. So if you have a situation

56:15

of low competition, like you have a few

56:18

monopolies, they're very profitable,

56:21

maybe they don't produce as much, but

56:23

they they return huge dividends and they

56:25

have a big market cap. Whereas I feel

56:27

like in China, they try to induce a high

56:30

level of competition and they help new

56:34

companies to keep entering. So you have

56:36

a huge number of companies, you have

56:38

relatively low profit margins, but you

56:41

have a lot of production. So let's let's

56:43

hear from up to in economics terms what

56:45

China doing is flattening the supply

56:49

curve more and more supply in there so

56:52

that the supply curve is almost

56:54

horizontal and when it looks like that

56:56

then you know if you taken um you know

56:58

beginning micro economics uh what it is

57:02

is the supplier surplus that little

57:06

triangle in the supply looks nearly zero

57:09

>> it just goes away everything is that hu

57:12

triangle that is you know above the

57:14

price line which will be the consumers

57:16

of us. So that's what you have in China.

57:19

In the US system, uh what where you know

57:23

you don't have a very flat supply, we

57:26

have a few participants in the market.

57:28

>> So the consumer surplus is less and the

57:32

supplier surplus is a lot more. So like

57:34

an outlet market

57:36

>> and the other benefits cost and benefits

57:38

of both systems which to me is just uh

57:42

whatever surplus you you just maximize

57:44

surpluses uh however you do it and u it

57:49

appears to be you know when you have

57:52

Tesla is worth 10 times more than by

57:57

uh you know the electric vehicle

57:59

penetration in the US is like whatever

58:01

5% margin when it's like 60% of the

58:04

market in China. That that's a failure.

58:06

You know, that's pretty much a failure

58:08

of um of the EV industry in the US. It's

58:11

not a success that pesa is worth 10

58:14

times what is in terms of how an

58:18

economist would look at how business

58:20

would look at it. They would say, well,

58:24

>> yeah, I I agree with you, Alex. It's a

58:27

question of what the society is

58:28

optimizing for. And if the society says,

58:31

"We want consumers to benefit, so we

58:33

want to maximize competition between

58:35

companies.

58:36

>> We're not going to allow monopolies to

58:38

extract monopoly rents from the system."

58:41

Well, then there aren't as many great

58:42

stock investments cuz what's my best

58:44

stock investment? Get into some company

58:46

that's going to become a monopoly that's

58:48

going to generate wild um earnings

58:50

numbers year after year after year in a

58:53

predictable way. Then yeah, that thing

58:54

suddenly becomes a trillion dollar

58:56

company. But that's not necessarily

58:58

greater for the consumers, right? So

59:00

it's it's basically that conflict. The

59:02

question though is like if you're racing

59:03

to get to AGI,

59:05

maybe you want to be the system that

59:08

will allow the AGI monopoly to whoever

59:10

wins the race and then that guarantees

59:12

your companies win the race because so

59:13

much capital ends up pursuing those

59:16

opportunities in the US and so little

59:18

capital is pursuing the AI opportunities

59:20

in China by comparison. I mean certainly

59:22

compared to the rest of the world it's a

59:24

lot of resources but compared to America

59:26

the amount of resources flowing toward

59:27

AI is like onetenth here as in the US

59:30

and yet they're still able to kind of

59:32

kind of keep up.

59:33

>> I believe that's something what Nick

59:34

Land argues.

59:36

So yeah, so Nick land would he has a

59:39

term techno capitalism which is that you

59:42

know capital produces awesome technology

59:45

um that technology makes a lot of money

59:47

which produces more capital and they

59:49

just have this feedback loop that's

59:50

running out of control and part of it

59:52

his observation is it's out of the

59:54

control there's no like it may seem like

59:56

Elon is the genius or you know this guy

59:59

is the Sam Alman is the but actually

1:00:00

what's happening is this machine is just

1:00:02

like is just like pushing capital toward

1:00:05

more tech devel velopment and then more

1:00:07

tech development creates more capital

1:00:08

and the thing just works on its own and

1:00:10

the people are kind of irrelevant. The

1:00:11

individual people it's just that the

1:00:13

dynamics will eventually then lead to

1:00:15

like super intelligence or something.

1:00:17

>> I guess one thing is I feel like the

1:00:19

Chinese startups because they're a lot

1:00:21

smaller like the Alibaba they're a

1:00:24

little bit more afraid to pursue like a

1:00:27

novel like product market set like

1:00:30

something where they don't know if

1:00:31

there's a market yet. So like one

1:00:33

example is like Anthropic kind of took

1:00:35

the lead in making like a a complete

1:00:38

software program to help you with

1:00:40

coding. So even though like the Quen

1:00:43

model for example, you know, the

1:00:45

intelligence level is like roughly the

1:00:48

same, the coding ability is roughly the

1:00:50

same because they didn't have like the

1:00:52

fully fleshed product. People don't want

1:00:55

to use it as much. But I kind of feel

1:00:57

like once they observe that like this is

1:01:00

something people will pay for. Yeah.

1:01:02

They'll build the fully fleshed out

1:01:03

product and I think they'll catch up.

1:01:05

>> I agree. I think we're seeing that right

1:01:07

now. There'd be a fast following by

1:01:09

Moonshot with Kimmy and with Quen and a

1:01:13

coding rig for Quinn. That fast

1:01:16

following could lead to a competition

1:01:19

where the a lot of the profit margins

1:01:21

are competed away for anthropic. Yeah,

1:01:24

>> I feel like most investors, global

1:01:26

investors, there's still a huge US side

1:01:29

bias where they just don't want to put

1:01:31

money behind the Chinese companies

1:01:33

>> for whatever reason. I mean, it could be

1:01:34

like, oh, the Chinese government will

1:01:35

never let these guys make as much money

1:01:37

as could be something that equilibrates

1:01:39

out like in the next 10 or 20 years or

1:01:41

could just be like just stuck like that

1:01:43

for a long time.

1:01:44

>> I think one major component would be

1:01:46

China China trying to develop it

1:01:48

semiconductor supply chain. That's

1:01:51

probably the most important piece in

1:01:52

China's AI alone.

1:01:54

>> Yeah.

1:01:54

>> At any point in time, if they continue

1:01:56

to rely on Nvidia, it's not a reliable

1:01:58

source of

1:02:00

>> Yeah. Now, we we've discussed there was

1:02:02

a group of American AI researchers and

1:02:04

journalists who came through here, came

1:02:06

through Beijing. I think you you met

1:02:07

with them, right, Justin?

1:02:08

>> Oh, yeah.

1:02:09

>> Yeah. And I think I I I looked at all

1:02:12

the reports that they read that they

1:02:13

wrote based on their trip here. And you

1:02:16

know a lot of them I think thought that

1:02:18

the GPU sanctions uh or controls weren't

1:02:23

good because they kept reporting that

1:02:26

the Chinese companies the number one

1:02:28

thing they heard from the Chinese IDI

1:02:29

researchers is we we wish we could have

1:02:31

more Nvidia GPUs. Do do you want to

1:02:34

comment on that? Is that is that a fair

1:02:36

assessment of the situation?

1:02:37

>> Yeah, I would say that's pretty fair.

1:02:39

Basically all Chinese would want more uh

1:02:42

Nvidia GPUs. Yeah. is is the issue the

1:02:45

money like so so there's two things

1:02:47

going on here one is the Chinese

1:02:49

companies don't have as much money which

1:02:51

we just discussed so even if they had

1:02:53

the GPUs they might not be able to do

1:02:55

the monster training runs that openai

1:02:57

andropic can do the other issue is just

1:03:00

if you have the money you still can't

1:03:02

buy the GPUs because the US government

1:03:04

is not allowing it like which which of

1:03:06

those two factors is actually more

1:03:07

decisive

1:03:08

>> it it depends on the company so for the

1:03:10

startups it's definitely the money and

1:03:12

for the large companies then then it

1:03:14

might be the supply although uh it seems

1:03:17

they are still able to get a ton of

1:03:19

GPUs.

1:03:19

>> Yeah. See, one of my one of the guys

1:03:21

that I've had on the podcast, a guy

1:03:23

called TP Wong, who's a software

1:03:26

developer himself, but also very close

1:03:27

observer of AI and military competition

1:03:31

between US and China. He he said his

1:03:34

reaction to Nathan Lambert and all these

1:03:37

guys, Jasmine Sun, Jasmine Sun, I think

1:03:40

I introduced her to you. was one of the

1:03:41

reporters who was here, one of the

1:03:43

writer. So his response is like look

1:03:45

they only interviewed basically startup

1:03:48

companies they didn't actually did they

1:03:51

meet with like Alibaba people did they

1:03:53

meet with bite dance people because TB

1:03:55

Wong would say those guys have very deep

1:03:57

much deeper pockets and it's unclear

1:04:00

whether they really are GPU poor so that

1:04:02

that that was TB Wong's reaction. I

1:04:04

don't know the answer myself actually.

1:04:06

>> Yeah. So, so for my lab who's currently

1:04:08

working at BES, he's able to get like

1:04:10

any GPU he wants. Yeah.

1:04:12

>> So, so it doesn't seem like they're GPU,

1:04:14

>> right? I mean, one of the secondary

1:04:15

things I would invite people to study is

1:04:17

the amount of Nvidia sales to Taiwan,

1:04:23

Malaysia, Singapore, all of these

1:04:26

countries which don't produce any models

1:04:29

also as far as I can tell don't like

1:04:30

produce a lot of inference tokens,

1:04:32

right? So, what what are these GPUs

1:04:34

doing in these countries? Even Taiwan.

1:04:36

So Taiwan is is one of the biggest in

1:04:38

terms of numerical purchases of GPUs

1:04:41

from Nvidia. But where do those where do

1:04:44

those GPUs go? I think they get put in a

1:04:46

suitcase and the guy gets on a plane for

1:04:48

Shanghai. And that's where I think the

1:04:49

GPU actually ends up because I can't go

1:04:51

to Taiwan and find anybody training a

1:04:53

monster model. I can't go to Taiwan and

1:04:56

find anybody running a monster model,

1:04:58

providing tokens. So, what the hell are

1:05:00

these GPU, you know, billions of dollars

1:05:02

of GPU sales to Taiwan, to Malaysia, to

1:05:05

Singapore? Where are these things?

1:05:06

Singapore does have some uh data centers

1:05:09

and but like the data centers in

1:05:11

Malaysia, like I wonder if they're just

1:05:13

doing computations for white dance

1:05:16

people or your friend or or whatever.

1:05:18

So, so I I actually I actually don't

1:05:20

know for the people that have the money

1:05:23

in China enough to pay for the compute,

1:05:27

do they have trouble getting it? Do they

1:05:28

have trouble getting the latest Nvidia

1:05:31

GPUs whether in in country or like

1:05:35

virtually by by running the jobs in

1:05:36

Malaysia or running the jobs in Taiwan

1:05:38

or something like that? I don't know the

1:05:40

answer to that question. I don't think

1:05:41

that group that came here really got to

1:05:43

the bottom of that.

1:05:43

>> I'll give I'll give an anecdote from

1:05:46

from the founder of Zai who's also a

1:05:49

professor at Chima. His name is Tia.

1:05:52

And so I did ask him, oh why don't you

1:05:54

use B200s? and basically said, "Oh, you

1:05:57

couldn't get."

1:05:58

>> Yeah, but they did have H100 stockpile

1:06:00

from several years staff, so they could

1:06:02

use that to train their moms.

1:06:03

>> So, yeah. So, I just don't know the

1:06:04

answer. So, maybe that in his case, he

1:06:06

can't get like he would like to get the

1:06:08

B200, but he can't.

1:06:10

>> Yeah.

1:06:10

>> Yeah.

1:06:11

>> One thing I can add is, you know, in

1:06:12

model training, it's kind of a high-risk

1:06:15

thing. So I think even if like the

1:06:17

Ascent GPU is of equally good quality,

1:06:21

people are afraid of switching to

1:06:23

something that's less established and

1:06:25

less tested.

1:06:27

>> I think another thing we might see in

1:06:29

this computing catchup process is like

1:06:33

maybe the first thing to really catch up

1:06:35

will be like the gaming GPU.

1:06:38

Then you'll see the inference GPUs catch

1:06:41

up. So more people will use the Chinese

1:06:44

the ascent for ging or sorry for

1:06:46

inference and then maybe post training

1:06:50

fine-tuning will catch up and then

1:06:52

pre-training might be the very last

1:06:54

thing to catch up. I think one of the

1:06:56

important things is that now that

1:06:58

deepseek is fully optimized for the

1:07:00

Huawei architectures at least for

1:07:02

inference like that is a big development

1:07:06

and my understanding is the pre-training

1:07:10

which requires the really excellent

1:07:12

networking that Nvidia provides is

1:07:16

increasingly a smaller and smaller

1:07:18

portion of the total compute that's

1:07:20

involved in AI because there's a bunch

1:07:22

of inference which is generating tokens

1:07:24

and there's a bunch of stuff like in

1:07:25

post training where it isn't really

1:07:27

you're not really updating all the

1:07:29

weights you're just updating small

1:07:30

subsets of the weights and so the

1:07:32

bandwidth uh aspects of the hardware are

1:07:34

not as important

1:07:35

>> so I think the the place where Nvidia

1:07:37

has its biggest advantage is like

1:07:39

shrinking

1:07:40

>> fraction of the total amount of compute

1:07:41

involved in AI that that's my impression

1:07:44

so any other uh observations you want to

1:07:48

make about competitiveness between US

1:07:50

and China it doesn't have to be about AI

1:07:51

or tech it could be about just like the

1:07:53

day-to-day when Han Fetto when you were

1:07:55

on the podcast earlier, we talked about

1:07:57

the convenience of how how nice it is to

1:08:01

live in China, like the food delivery,

1:08:02

the good food, everything's so

1:08:04

inexpensive. Any anybody want to comment

1:08:06

on that aspect of it? What what your

1:08:08

daily life is like here compared to what

1:08:10

it would be like in Montreal or

1:08:12

somewhere else? Oh, I guess before we go

1:08:14

on about the life, I think I think one

1:08:16

province or one university to definitely

1:08:18

watch out for in China is this

1:08:20

university in Amway called USC,

1:08:23

University of Science and Technology of

1:08:25

China. This university is uh producing

1:08:28

some of the worldass like uh chip

1:08:30

engineers and um one of the top memory

1:08:34

companies in China called Ching Memory

1:08:36

Technology CXMT is just based out of

1:08:38

there as well. Yeah, I I think for

1:08:40

people in physics, we've been aware of

1:08:42

USC for a long time because although

1:08:45

it's out in the middle of nowhere, it's

1:08:47

been one of the most excellent Chinese

1:08:50

universes for a long time. In fact, in

1:08:51

fact, I would actually say, you know,

1:08:54

Bay Chima and the USC, at least from a

1:08:57

physics perspective, are probably the

1:08:59

the top universities. USC has been good

1:09:02

for a long time, and they also had a

1:09:04

genius program for a long time. So they

1:09:06

were admitting kids to USDC at age 15 or

1:09:10

16

1:09:11

>> for for a long time. And so like a lot

1:09:13

of the best people that we would see in

1:09:15

the US were not Chinua Bay kids. They

1:09:18

were actually USC kids who had gone to

1:09:20

college when they were 15. So that

1:09:22

that's that's been around for 30 years

1:09:24

or more.

1:09:25

>> Yeah, that definitely makes sense on the

1:09:27

physics. I think even the electrical

1:09:28

engineer side. Yeah. And then on the AI

1:09:30

side, it would be Chinua Piking and then

1:09:32

Shanghai gel. Yeah. Which has their

1:09:36

AC and glass. Yeah.

1:09:37

>> And there's also Zaha is supposed to be

1:09:39

very inj

1:09:41

supposed to be quite good. So

1:09:42

>> So the urban layout and the way the

1:09:44

roads work is super weird if you're from

1:09:48

the US for example. So a lot of people

1:09:51

like to ride these little two wheel

1:09:53

scooters around. Like they're always

1:09:55

electric. They're super cheap. But it's

1:09:58

just funny to see a whole group of

1:09:59

people riding around in these things.

1:10:01

But to make it work, you know, they have

1:10:03

big bike lanes and then they have really

1:10:06

wide sidewalks and then people park

1:10:08

their two wheelers in the sidewalk

1:10:11

>> on the sidewalk.

1:10:12

>> And then also the urban layout of

1:10:14

Beijing is super weird because they

1:10:16

basically stuffed all the universities

1:10:19

into the top left corner of the city. So

1:10:22

you got like Shinua, Chinese Academy of

1:10:25

Sciences, Pecking, Renman. So this I

1:10:29

don't know. It's very unusual to just

1:10:31

have all the universities stacked.

1:10:33

>> It's a little bit like Boston, Cambridge

1:10:35

in the US where it's like there's so

1:10:37

many colleges in that town and this

1:10:39

Haidan this this northwest is it

1:10:42

northwest part of Beijing is like that.

1:10:44

It's like that's where all the

1:10:45

universities are and they're pretty

1:10:46

close to each other.

1:10:48

>> Yeah. But not just only the universities

1:10:51

but the political centers here as well

1:10:52

>> and the tech companies. Yeah. Yeah. You

1:10:55

know, I was going to say that um

1:10:58

this electric bike culture is very

1:11:00

unique here. I I was talking to Kaiser

1:11:03

Gua Kaiser Qu. Um and he says he gets

1:11:06

the best way to actually get around

1:11:07

Beijing is just to ride around in his I

1:11:09

forgot the name of it's like electric

1:11:11

turtle or there's some name for what he

1:11:13

has. It's just some classic like

1:11:14

electric bike and he just rides it

1:11:16

around Beijing and you avoid the traffic

1:11:18

that way.

1:11:18

>> Yeah. Yeah. cuz you can kind of go

1:11:21

anywhere with the electric scooter cuz

1:11:23

you can do sidewalks, lightly or the

1:11:25

main roads,

1:11:26

>> whereas cars and pedestrians, you're

1:11:30

kind of limited to just one of three.

1:11:32

>> So, China's very safe. It's very safe.

1:11:34

There's low crime rate and everything.

1:11:36

But the the most dangerous thing I

1:11:38

always tell my friends, if you heard

1:11:39

that I was killed in an accident in

1:11:41

China. I was killed I was killed by an

1:11:44

like a delivery guy on an electric

1:11:45

scooter who just hit me while I was on

1:11:47

the sidewalk. And that's like the one

1:11:50

dangerous aspect I find.

1:11:51

>> It's it's also funny. Most people don't

1:11:54

don't wear helmets.

1:11:56

>> Yeah.

1:11:56

>> Well, the the delivery guys wear Yeah.

1:12:00

>> I think they just they just changed the

1:12:02

log to like force it like this week.

1:12:04

>> Yeah. I just someone just told me you

1:12:06

have to wear a helmet down below.

1:12:07

>> That's one of my friends that took it.

1:12:09

>> Yeah,

1:12:09

>> I'm interested.

1:12:11

>> So I think that electric bike culture is

1:12:13

coming to the US and it's an example of

1:12:15

something that actually although most

1:12:16

Americans don't realize it, it

1:12:18

originated in China and now it's coming

1:12:20

to the US. So now you can buy inex

1:12:22

relatively inexpensive electric bikes in

1:12:25

the US. They're all made in China and

1:12:27

it's becoming a thing where like people

1:12:29

who have a slightly longer commute would

1:12:32

get an electric bike. Yeah.

1:12:33

>> And ride it, you know, and so that

1:12:35

that's like coming to the US now.

1:12:37

>> Yeah. Yeah.

1:12:38

>> Yeah.

1:12:39

>> I I wanted to imagine like the car

1:12:41

culture is so ingrained in America that

1:12:42

they just want to change it,

1:12:44

>> you know. I think it it's only going to

1:12:46

come to certain like cities probably or

1:12:48

campuses where people But I see more and

1:12:50

more electric bikes now. I mean in

1:12:52

Berkeley I saw a lot of electric bikes.

1:12:54

>> Oh yeah. So the universities, they're

1:12:56

starting to have these electric bikes

1:12:57

because of the ch the Chinese students

1:12:59

there.

1:13:00

>> Oh, that could be it. That could be the

1:13:01

vector. That can be like balling. Yeah.

1:13:03

>> Wait, weather has got to be a factor,

1:13:05

too. Cuz if you have ice on the road,

1:13:07

electric scooter be so scary.

1:13:09

>> Yeah,

1:13:11

>> they somehow they somehow manage it.

1:13:13

>> No, Beijing has really dry winter, so

1:13:16

there's usually not ice on the road.

1:13:18

>> Yeah, it may not work in Montreal.

1:13:20

[laughter] I've done it.

1:13:23

>> Okay. Anything else?

1:13:24

>> I don't want just just a practical thing

1:13:26

for Gabriel. If you are a high school

1:13:30

student in the US, um what what kind of

1:13:33

student would you recommend apply to

1:13:35

Chinese universities and what

1:13:37

preparation do you think you recommend

1:13:39

they do say a couple years out?

1:13:42

>> Oh, that's a that's an interesting

1:13:43

question. I think um they should

1:13:47

definitely at least be interested in

1:13:50

China. That's prere regarding Chinese

1:13:52

language. I think it's definitely better

1:13:55

to have prep. I wouldn't say it's like

1:13:57

completely uh bad to have any prep at

1:13:59

all as I don't think allows foreign

1:14:02

students to do like a one year language

1:14:04

course like before you go on to start

1:14:06

your actual university degree. So T1

1:14:08

actually actively recruiting a lot of

1:14:10

just like foreign foreign people. So

1:14:12

they're that's one of their goals

1:14:14

because they seem to have a lot of like

1:14:16

Chinese people with foreign passports

1:14:18

try to just come back. But yeah, sort of

1:14:20

actively trying to recruit these

1:14:21

actually for more on people.

1:14:24

>> I I guess the main thing is Oh, my my

1:14:27

friend did recommend this. Hopefully,

1:14:29

you're interested in STEM and you're

1:14:30

actually interested in research. If

1:14:32

you're here for just like a humanities

1:14:34

major, um you won't get nearly as much

1:14:36

out of it. I think

1:14:38

>> my my wife's a professor of literature

1:14:40

and film and she she's going to be on

1:14:43

sbatical at Chinua this fall. So, so

1:14:46

hopefully there is some humanities here.

1:14:48

Um, she's been actually a visitor at

1:14:50

Beijing University and now at Chinua.

1:14:53

So, we'll see how they compare to the

1:14:55

humanities.

1:14:55

>> Does she do like I don't know oriental

1:14:58

literature or is

1:15:00

>> her her focus is mainly Yeah. modern

1:15:01

Chinese literature.

1:15:02

>> Oh, that would get a lot. [laughter]

1:15:04

>> Yeah. Yeah.

1:15:05

>> I guess I have some extra advice for

1:15:07

that for those high school students. So,

1:15:09

make sure you really know how to speak

1:15:11

Chinese and you can make friends with

1:15:12

Chinese people.

1:15:14

>> Yes. You got to be social. I think I

1:15:15

think you're a good example.

1:15:16

>> So, so there's a very clear split in

1:15:19

Chinua

1:15:20

among the internationals. Even among

1:15:22

many of the um so-called uh overseas

1:15:27

Chinese who can speak a little bit of

1:15:28

Chinese, they still don't interact much

1:15:32

or at all with uh domestic Chinese

1:15:35

students. And I feel like I can even see

1:15:38

this case among Malaysian students who

1:15:40

are they they grow up in a Chinese

1:15:42

schooling system. they see Chinese all

1:15:44

their lives, but even then they have

1:15:46

some trouble like u interacting with the

1:15:48

Chinese domestic students. So, so you

1:15:51

can imagine how hard it is for people

1:15:52

who don't speak Chinese more at all. But

1:15:55

I think I think Chinese students are

1:15:56

very friendly and they're very happy to

1:15:58

be friends with the outside people. So,

1:16:00

I I would encourage people to really

1:16:01

just reach out to Chinese people and try

1:16:03

to try to be friends. And and and a very

1:16:05

interesting person I met here is he's

1:16:08

like a just a pure pure-blooded

1:16:10

American, white American from Tennessee.

1:16:13

And uh he was working in the battery

1:16:16

industry in the US for a few years. And

1:16:19

one day his Chinese coworker was like,

1:16:21

"You teach me English, I'll teach you

1:16:22

Chinese." He's like, "Okay, yeah, let's

1:16:24

do that." And he he studied Chinese for

1:16:26

like a year and then like very intensely

1:16:29

for a year and then he came to Chinua

1:16:32

for his masters in material science and

1:16:34

he only exclusively hangs out with

1:16:35

Chinese people. His Chinese are amazing.

1:16:37

Like I was I would say on some some ways

1:16:40

better than my Chinese which is so

1:16:42

impressive. Yeah. And and very

1:16:43

embarrassing for me at all. [laughter]

1:16:45

>> That's wild. I I think though the hard

1:16:47

part, at least for me, would be becoming

1:16:50

fully fluent in the written language

1:16:51

because like for me it's really hard to

1:16:53

memorize the characters and stuff like

1:16:55

that. I just I completely amazed by

1:16:57

people who could come here in a year and

1:16:59

suddenly learn how to read.

1:17:00

>> Yeah, I can't do that as well as to

1:17:02

this. I'm struggling a lot with this. So

1:17:04

So texting,

1:17:05

>> you're going to have to wear your AI

1:17:06

smart goggles where

1:17:07

>> Yeah. Yeah. Exactly. I need that.

1:17:09

Luckily, there's AI now. So I just click

1:17:11

a button and say everything.

1:17:14

>> All right, we're almost out of time. In

1:17:15

fact, we only booked this room until 3.

1:17:17

So we're going to have to get out here.

1:17:18

But let me let me ask you two AI guys,

1:17:22

do you have any predictions that you

1:17:24

might think would be surprising to the

1:17:26

US listeners or the non-Chinese

1:17:28

listeners? Any any thing about like

1:17:31

robots, robots in the home, or who's

1:17:34

going to be leading the model race, LLM

1:17:37

model race a year from now? any anything

1:17:40

that you think is sort of a little bit

1:17:41

non-obvious for the listeners?

1:17:44

>> Maybe the most nonobvious one although

1:17:46

it's also the quite it might be the most

1:17:48

uncertain one um is probably the the

1:17:52

phase transitioning where they start

1:17:54

mass producing machines like that is a

1:17:57

very politically sensitive issue in

1:17:59

China as well. So you can almost get no

1:18:01

like high quality information about that

1:18:03

but but it seems like they're going to

1:18:05

start massproducing that soon. Okay, I

1:18:07

want to Okay, I want to drill down on

1:18:08

this because I I I'm intensely

1:18:09

interested in this question and I even

1:18:11

like

1:18:12

>> when I was in Shanghai actually right

1:18:14

after I taped with you, Hu last time I

1:18:16

went to Shanghai and I another Manifold

1:18:19

listener who's Taiwanese Chinese brought

1:18:23

me to dinner and I met the top

1:18:25

leadership of SMIC who were all

1:18:27

Taiwanese. It was wild. They and their

1:18:29

wives were like all Taiwanese people.

1:18:32

Even they like we wouldn't really talk

1:18:35

about EUV because it was so sensitive.

1:18:37

>> You are asserting that we might see

1:18:40

actual EU Chinesemade EUV machines in

1:18:44

production soon. Is that what you're

1:18:46

saying?

1:18:47

>> But this is based on information that

1:18:48

people can rumors that people other

1:18:51

people can also find on the internet. So

1:18:53

this is like this is like most funny

1:18:55

information. But there's no there's no

1:18:56

like actual chin wa rumors like oh my my

1:19:00

labmate went off to this huge facility

1:19:03

that Huawei runs in near Shanghai where

1:19:06

they're actually maybe he's working on

1:19:08

EUV like that. You're you're not hearing

1:19:10

rumors like that.

1:19:10

>> I can Okay, I may be able to ask around.

1:19:13

I I I do know many of the the physics

1:19:16

engineering students and many of them

1:19:18

>> they go to the the SN manufacturers like

1:19:21

CMXT YTMC side carrier Huawei as well.

1:19:26

>> When I met with uh Taylor Ogen this

1:19:29

hedge fund guy in Shenza

1:19:32

>> he has he was tracking this uh green

1:19:35

field the site that Huawei and he has a

1:19:38

lot of inside access to rivers and

1:19:40

stuff. So he claims there's this huge

1:19:43

site where they're developing EUV stuff.

1:19:46

Huawei is doing it. And so but the

1:19:48

question of like how far they are from

1:19:50

actually shipping a machine that's used

1:19:52

to make chips, I I have no idea. But

1:19:54

there is definitely intense effort.

1:19:56

>> Yeah.

1:19:56

>> And there must be people going to staff

1:19:58

that facility, right? So So in

1:20:01

particular, Huawei does have a campus of

1:20:03

like

1:20:04

>> like maybe one to two million R&D

1:20:07

people.

1:20:08

>> Yeah.

1:20:08

>> Yeah.

1:20:08

>> Yeah. somewhere in the outskirts of

1:20:11

Shanghai.

1:20:11

>> Yeah.

1:20:12

>> Yeah. But but but we wouldn't be able to

1:20:14

get any like real information on this

1:20:16

because there there there were like

1:20:19

research groups who tried to dig into it

1:20:20

and [music] they were raided by the

1:20:22

government.

1:20:22

>> Oh, I see. So the government's

1:20:24

specifically trying to keep a lid on.

1:20:25

>> Yeah. So any anyone who actually wants

1:20:27

to do like rigorous analysis and

1:20:29

research on this

1:20:30

>> Yeah.

1:20:31

>> they they don't want to do it. They want

1:20:32

to stay away from because it's a red

1:20:33

line.

1:20:34

>> Interesting.

1:20:36

>> Okay. Well, maybe now is a good time to

1:20:38

call it. Awesome. Thanks to you guys. I

1:20:40

hope for our listeners this has been

1:20:42

informative and now you know more about

1:20:44

what life is like at China's top [music]

1:20:46

technological university. Thanks for

1:20:48

listening. Bye.

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