Hello listeners, welcome back to Ascent. So today
we'll be talking about another really interesting
company in Asia. What if I told you one of the
most exciting new players in the competitive
field of large -language models wasn't founded
by a Stanford or MIT AI PhD straight out of the
top lat, but by a quant trader? Was this quant
trader, well, I'll tell you the name, Liang Wenfeng,
the man who solved the Chinese market? To what
extent was his quant trading business developed
in China? Why did Liang Wenfeng pivot it from
quant trading to LLM development? And the most
mysterious of all, how was Liang Wenfeng, the
founder of DeepSeek, able to compete among all
the giants in and outside of China, while all
the other LLM giants are much better equipped
than him? In this episode of Ascent, we're diving
into DeepSeek. It is not just... any one LLM
story, but we'll be going through the founder's
journey, how he embarked on the first half of
the career as a quant trader and created a quant
trading firm, and how did that firm later transition
into an LLM company, which led to the product
that we all know today, DeepSeek. Join us as
we unpack this fascinating transition. Linda,
I have a question for you. So if you were to
have 80K RMB, that's roughly a little over 10K
US dollar at the age of 22, when you just graduated
from the college, what will you be doing? What
will you be using it for? Well, first of all,
I would love to have 80K when I graduate from
college. I didn't immediately have this, but
I remember exactly what I've done with... kind
of my first major bonus paycheck, which is to
spend it all on traveling in a, I think, one
or two week holiday to Peru. So definitely spend
it. What about you, Pan? Invested on the personal
discovery. Well, I think like yourself, the first
amount of money that I... get right out of the
college. First, wasn't a lot. And second, I also
did a lot of fair share of exploration of the
world. So, but I think in hindsight, I should
have invested in something, which I guess brings
us to the company, the founder that we're talking
about today, Liang Wenfeng. Well, not only is
he a really good investor himself, he also made
crazy amount of money. In between the age of
22 to 30. And I guess I'll give you a little
spoiler alert. Quick question for you. How many
times did your income increase from the age of
22 to 30? It's kind of a painful question. All
right. So listeners, please think about your
own income multiplication. Let's see. I think.
I'm not, not that much over the past 10 years.
I think probably a little bit, I would say a
little bit more than inflation, right? But not
that much. Yeah, definitely. It is a factor of
a few times, but it wasn't a factor of like tens
of times or a hundred times. For sure. Right.
And I think. To your earlier point, later on,
after spending my expenses on earth traveling,
later on, we eventually learn about investment.
So I would actually say my investment return
at some point, cumulatively, probably was more
than my day job income. So that kind of calls
into the importance of investing, especially
in the last 10 years when the market is just
doing so well. Right. Yeah. Which brings us to
the answer to your question earlier. How much
did Liang Wenfeng make in the last 10 years?
I guess apart from all the news coverage about
DeepSeek and how fast it expanded to different
parts of the world, Liang Wenfeng's personal
income from the age of 22 to 30 was 100x. We
will cover that later on in the stories, but
just think about that. And he did that purely
through his capability in mathematics and computer
science. Very interesting. Yeah. So how did he
start? So let's dive in. Chapter one, Liang Wenfeng's
early ages. Small village mass genius. So he
was born in 1985 and he was born in a very small
and peaceful and chill village by the ocean in
Guangdong province. And if you don't know where
Guangdong province is at, it is the province
that is basically attached to Hong Kong. It is
the northern part of Hong Kong. Both of Liang
Wenfeng's parents are elementary school teachers.
The city, or rather should I say the town that
he was in, had less than 1 million people, which
is considered very small in China. The city is
called Wuchuan. It is a hub for manufacturing
plastic slippers. which has nothing to do with
investing or, you know, like not even tourism.
I don't think anyone knows about that place.
So very, very modest beginnings. Very modest
beginnings. He is very talented. Léon Pond is
very talented in his elementary school, exhibiting
mathematics, talents. When he did his college
entrance examination, it was... It was no surprise
that he got number one in the town of Wuchuan.
And also with that, he made it into Zhejiang
University and he started studying electronic
information engineering, which sounds very much
like double E studies. Yeah. And for those who,
I guess, are less familiar with Chinese universities,
Zhejiang University is, I think no one would
disagree if I say among the top five. Definitely
tier one. Definitely tier one. Yeah. It's extremely
hard to get in. I don't have the exact numbers,
but I would say like one in a thousand or more.
Yeah. Yeah. In fact, my Google. His referral
was a graduate from Zhejiang University, and
she is a computer scientist as well. Do they
know each other? Different times, different times.
But, you know, from a tier one university is
a very good start for the journey that he got
himself into, like in terms of quant trading,
because that's when the time that not only did
he study, he also made his later partners in
business for quant trading. So throughout his
undergrad. Besides studying, he did also run
into a few interesting people. So in 2006, that
was the time when he's a senior of college. He
ran into someone. who you may have heard of his
brand, but the name Wu Tao was the person that
Liang Wanfeng had encountered. What's Wu Tao's
brand? Wu Tao's brand is the number one drone
brand in the world. Currently, it's called DJI.
So back in the days, Wu Tao is from Zhejiang,
and Wu Tao had invited Liang Wanfeng to join
the company. Had he started to join the company,
the DJI, back then, Liang Wanfeng would have
also been... equally financially free or had
achieved his own wealth accumulation by now,
but he did not. He has his own thing, I guess.
Yeah. Hardware may be not his thing as much.
For sure. So Liang Wenfeng seemed to have very
early and strong conviction to artificial intelligence.
And he believes that this will eventually be
the force that changes the world, not so much
as a drone, I guess. Even back in 20... With
his 2006. In 2006, he said no to Wu Tao. And
a few years later, the actual partner that he
had picked was a machine learning major. So I
think maybe something was in his mind that he
wanted someone who is, you know, doing mathematics
or in the computer science or in the machine
learning field. So that seed was planted early
on. Very interesting. Yeah. Yeah. He has his
ideas. Yeah. Yeah. So, OK, so at the end of his
undergrad, you know, like like any other student
in China, you would think of where do I go to
graduate school? There's no way out directly
from undergrad to job force. Yeah. So starting
in 2007, he once again enrolled in Zhejiang University
for his graduate school. And at that time, he
enrolled in information and communications engineering.
Yeah. But think about this. The second year of
grad school, which is 2008, the world global
financial crisis happened. Right. Financial crisis
happened. While he was doing grad school, he
became very interested in the financial system.
itself and also the crisis, he was thinking,
how do I leverage this into something that could
really benefit me? So rumor has it that he was
hiding in Chengdu and not so much spending time
in Zhejiang. And he had spent time in Chengdu
and learning how to code. And he had also written
a automatic trading system while he was in Chengdu.
And this program later on became the foundation
for the trading algorithm. that he had started
but this journey started as early as 2008 wait
so are you saying he skipped school for his own
startup you know what from all of the research
it is impossible to actually know whether he
attended school but but it but he was just so
intrigued and he kind of started his own company
like sort of the seed round of his company the
the very early stage of it in 2008 he just he
just couldn't contain the curiosity of you know
doing more in trading and doing more in writing
algorithm right that's very interesting i mean
without revealing my age i started college in
2008 and i remember do the math guys um so in
my first year of college all everyone could talk
about What's the financial crisis? I mean, spoiler,
I study economics. Obviously, my classes are
all about like, hey, if you graduated this year,
you might not be able to get a job. But great
that you're still a freshman. So I kind of. You
know the mindset. Why he, you know, all of a
sudden in 2008, like is trying to figure out
a way to combine what he's studying with the
financial market. Yeah. Also rumor has it that
in 2008 was the time that he had the initial
funding or the small principle of a couple 15K
US dollar. It's like 80K RMB. It's not a lot.
It is a very humble beginning, but that's sort
of the money that he started with. Anyways, we'll
come back to that number later. You just have
to remember it's a various amount of money. Small
amount of money. 15K USD. Exactly, yeah. So then
by 2010, time for graduation, he graduated from
Zhejiang University with a degree in information
and communications engineering, and specifically
with a bit of a touch on machine vision or computer
vision. And his thesis was on object tracking
with the PTZ camera, which is the pan -tilt -zoom,
which in layman's terms is just a surveillance
camera, right? So that's what he did. Let me
just add one more thing before we dive into a
very interesting event. So in 2013, three years
afterwards, he had started this company, his
first ever investment company with a Zhejiang
University PhD called Xu Jin in Hangzhou. And
then the two of them started sort of expanding
on the foundational algorithm that Liang Wenfeng
started in 2008. And then they were trading primarily
in the stock market within China. People would
say that Liang Wenfeng and Xu Jing, what are
they like? To give you a bit more context. So
Liang Wenfeng is like the very introverted Ilya
Soskever kind of tech bro. It's very quiet. And
Xu Jing, despite being a PhD in machine learning
and actual LM, he is more of the salesman like
Sam Altman. So not all PhD are introverted is
what you're saying? Well, I guess not. first
ever investment company is called a Hangzhou
Jacobi or Jacobi I don't know how exactly I can
pronounce that but the name is from a German
mathematician whose name Carl I guess Jacobi
J -A -C -O -B -I. Exactly, yes. And he was the
German mathematician who made fundamental contributions
to elliptic functions, dynamics, differential
equations, determinants, and number theory. Stuff
you learn if you major in math. Yeah, yeah, yeah.
And Jacobi's work particularly... The Hamilton
-Jacobi -Bellman equation, the HJB equation,
had a significant connection to quantitative
trading. So this mathematician's work is really
quite favored by all who are in quant trading.
And if you're in quant trading circle... I guess
mathematicians would be, this particular person
would be very well known. So they nerded out
on their company name. Yeah, nerded out on the
company name. That's really cool. Yeah. Also,
I guess a bit more on the partner, Xu Jin, like
XJ. Yeah, this is a very Chinese name. And I
do not blame you if you cannot reproduce the
name. Do not worry about that. is also a graduate
from Jersey University. And his major, when he
was doing that PhD, is in machine learning. So
he's actually a machine learning PhD and specializing
in the LLM back then in 2013. Very good foresight.
Very, very early on. He was also very lucky.
So in 2010, he had already been working and had
led one company public within China. And later
on, he himself joined Huawei's Shanghai Research
Institute. Very hardcore and very good at sales.
He's also very talented in the strategy making
in quant trading firms. And the annualized returns
with the product that he created sustained a
38 .63 % return on an annual basis. So that's
not bad for a quant trading firm product. This
is his... or this is Jacoby? This is the early
days of Huanfeng, which is Jacoby. Yeah, so it
was the two of them. But the IPO company is another
company that he left. So he was running two companies.
One IPO, one was Huanfeng, which is High Flyer.
Yeah. Wow. And he's also very young. And he is
in the special talent class within Zhejiang University,
like a crazy talent class, like Qinghua, Beta,
they all love it. Yeah, the special class. Yeah.
Anyways. Genius class. Genius class. Yeah. You
just have to let the genius use the talent that
they have and, you know, make things happen.
I think this is a sort of a deep six philosophy,
which we will go into later on. Yeah, we'll go
into that later on. So one thing that's really
worth mentioning that happened in 2012, which
is a paper that is published and it reads. ImageNet
classification with deep convolutional neural
network. Do you remember the name AlexNet project?
Yes. But for those of you who don't, it's a machine
learning competition, right? Right, exactly.
So it is a competition that is called ImageNet
competition. a sort of a robotic slash machine
learning scientist, Li Feifei in the Chinese
term, she started this competition in 2006. And
ImageNet is a project, it is a very large visual
database designed for visual object recognition.
And it's a software -based contest. So you basically
just tag a lot of visual objects and see which
one of the software can recognize in accuracy
which one of the object it is. And the database
itself is very large. Yeah, so I remember it
was... So the ImageNet competition basically
gave away a data set of images. And whoever gets
the most accurate answer... That's right. So
the implication of AlexNet or the ImageNet competition
is it revolutionized image recognition by demonstrating
the power of deep convolutional neural network,
CNN, which is sort of the original deep learning
neural network. Neural network. Yeah. Quick fun
fact about that. Machine learning has been in
existence for years, and the concept of neural
network actually is not new. It's something that
was actually, I think it started in the 70s when
there's papers published about it. But when it
was kind of first used in research purposes,
I think it didn't perform so well. So different
branches of machine learning kind of started
off. better than neural network right and so
it's been kind of in its winters for the last
30 40 years and a different branch which is more
based on teaching teaching the ai stuff and and
making it learn from like what we feed them rather
than using kind of neural network which is how
our brain functions true led uh most of the innovation
in the past 30, 40 years until this very moment,
which is AlexNet, which proved. that neural networks
are the way to go and that kind of shifted all
the research going forward exactly and another
fun fact which is do you know who are the three
authors that published the alex net project are
the three alums from your particular university
university of toronto yay toronto toronto it's
a alex frizhevsky if i mispronounce that name
i'm so sorry and ilya suskever which later worked
for open ai and jeffrey hinton which later worked
for google Yeah. Yes. I vividly remember the
email of Jeffrey quitting Google. Never met him
in U of T, but great professor. Great professor.
Exactly. So 20, all the way throughout Liang
Wanfeng's graduate school time, seems pretty
exciting. He finished his graduate study, found
a partner, and his partner and him both have
a lot of establishments and studies in machine
learning in this particular sector. And in 2012,
this very exciting competition happened, which
I think I can only assume that if you're in this
particular segment, you must know what had happened.
On to chapter two, quant fund years. how fast
can you really grow? Can you make money? It is
really the speed that how fast it grew. And looking
at the speed of a lot of different quant funds
and how they grew. To be honest, many quant funds
in the US, they are sort of an extension of an
existing hedge fund. So they are always starting
somewhere, I guess, besides Renaissance. That's
because of Jim Summers also started from nowhere.
But Liang Wenfeng, The speed that he was able
to accumulate wealth was really quite fast. And
let's take a look at what he had done between
the age of 22 after he graduated and 30. Yeah,
100 times, right? Yeah, 100 times. Yes, the end
result is 100 times AUM. It's quite fast. So
before we go into the numbers, let's do a quick
definition on what is a quant trading. hedge
fund yes teach us how to make money oh my god
actually you should be doing this we we have
about it after yeah let's let's talk about it
we We actually went through a trading software
and saw what's the current offering for Liang
Wenfeng's company. And it seems that it's performing
very well at this. Yeah, it's not a bad idea.
It is percent of market. But it is listed as
very high risk at this point. So we'll have to.
The most risky type of investment. But most,
I think, Chinese. hedge funds or chinese private
funds are listed in the r5 most risky category
right and you do have to have a minimum amount
of sort of capital to be able to even buy that
ticket it's not like you can just have 20k and
then enter no it's not that kind of deal it's
a game for the rich which we're not qualified
yet a quantitative fund commonly known as a quantitative
trading hedge fund It utilizes computer algorithms
and quantitative methods to identify and trade
most commonly stocks and commodities, etc. And
these funds, they typically use a combination
of mathematical and statistical models and along
with algorithmic strategies for trading opportunities,
identification and execution, a .k .a. You don't
need a person to be executing a trading strategy.
You make the strategy first and then you execute.
In both phases, you use an algorithm to do that
decision making and then to do that execution.
And it heavily depends on quantitative analysis
for investment decisions and eliminating subjective
human judgments and emotional factors. I think
this is probably the hardest part in trading
because when you see that the price goes down,
that feeling... You want to click sell. You want
to... click sell but so the beauty of quant trading
is um it is a complete rational uh system uh
you program into the algorithm that when whatever
number hits you know goes up then you sell when
it when it drops again it right you you you can
buy in again so it is a less emotional of course
than human beings and these fund would use vast
amount of data to understand the market efficiencies
and understand the market trends. Typically,
a quantitative trading team would be composed
of... mathematicians, statisticians, computer
scientists, which is very different from the
traditional traders who are sort of working the
finance and hedge fund segment. Most commonly,
if you read any financial newspaper or, you know,
just read any stories about the con trading firms,
three strategies would show up very often, which
is the statistical arbitrage, the high frequency
trading and factory investing. Please explain.
I will try my best. Statistical arbitrage seeks
to profit from temporary price discrepancies
between assets that have statistical relationship.
This is... Kind of what I described before. So
buy high, sell low, but with some kind of relationship
between the stocks. Exactly. Typically and very
popularly used in commodities because it has
a very clear demand and supply relationship and
then you have a lot of historical data to refer
to when you make a decision. Therefore, you're
using the past historical data to foresee what's
going to happen in the future. the demand and
supply would happen. So if you see a relationship
between crude oil and natural gas, and one goes
up and the other doesn't, there's an opportunity
to profit from it. I guess in Barry Lehman's
term, yes, that would be an opportunity. And
high -frequency trading is just straightforward,
as in you're taking advantage on how fast you
can trade. Executing large volumes of order at
extremely high speed, as in milliseconds. And
I guess a little bit of a note here. What we
will discuss later in terms of the high frequency
trading in China, I don't think it will be exactly
what's high frequency in the States. There might
be a difference. Lower frequency, high frequency
trading. Lower high frequency trading. So honestly,
I'm not a trader myself, so I wouldn't know exactly
how many milliseconds like in China it would
be traded. But based on the like, first of all,
Chinese government and Chinese stock market just
doesn't like high frequency trading. If it's
commodity, then you can do high frequency trading.
But if it's stock market, then absolutely no
go zone. So arbitrage itself, this idea is not
very favored by the financial market here. So
the second one, high frequency trading is more
like trading frequently to benefit from increase
in. decrease during the milliseconds the more
volatile the better it is okay so if you were
if you were to be experiencing a very high high
or a low low then and if you can benefit off
of speed and you can predict um you know the
prices then yeah there might be a lot of opportunities
um so especially during financial crisis this
should be you know a relative combined with statistical
arbitrage right you'll be making a lot of money
this is why i think a lot of The high frequency
trading firm actually earns, like has a lot higher
return during the market volatility and downtimes.
Exactly, exactly. Yeah. So this relies on fast
technology and low latency. And finally, factor
investing, which I think is what a lot of Chinese
quant trading firms have applied in the past,
perhaps a combination with the previous two,
but this one is definitely most favored and most
pointed out, which is factor investing. You have
a lot of factors. You build portfolios based
on different quantitative factors or characters
that are historically been associated with high
returns. So for example, Crude oil is associated
with factor A, B, and C and you program your
algorithm to specifically be be related to your
factors A, B, and C? And should factor A, B,
and C were hit and you buy in or you sell? Oh,
so it's like building a relational diagram, like
a tree diagram of all the, whether it's stocks
or commodities that you want to trade and figure
out what is the leading factors if A, then B.
Exactly. And I track A. So if A changes, then
I buy or sell B. But here's the thing. I'm not
a quantitor. myself but i've had a conversation
with a real quant trader and what he had been
telling me was you always need to be finding
what's the next new factor factors actually change
throughout the years every factor can only be
useful for a certain period of time and if everyone
is using the same factor then the factor loses
its efficiency ah so it's like it's almost a
competition of which firm which fund finds the
newest factor because if you don't find new ones
you can't profit from the old ones or it's overcrowded
or your annualized return would start to decrease
okay so it's a race to see who finds the best
new factor fast constantly exactly constantly
it's a lot of stress it's a lot of stress okay
which is which is why you would need really a
group of statisticians mathematicians and computer
scientists to be able to do all of that you're
just analyzing a bunch of data every single day
and finding what's the new trend what's the new
factor and how can I be arranging all of these
in my trading algorithm it's very interesting
oh my god it sounds exhausting to me it sounds
like my day job Linda has a lot of potential
going into a trading firm without all the yeah
when's your birthday I'll be buying books let's
understand solve the market let's do that Okay,
coming back to Liang Wenfeng. So remember that
he had a very small principal money by the end
of his graduate school, right? And by 2015, this
is when he is 30 years old, the market has already
experienced seven years of bear and bull market
after the global financial crisis. And with that
fluctuation and volatility, oh my goodness. By
he is 30, he had made 100 million RMB. Obviously,
this is not only from the 80K that he started,
as we were saying earlier. probably other LPs
join later on. But all because of family and
friends, I guess. But that's all because he has
demonstrated that I am able to deliver such a
great annualized return. And it can be as high
as 160 % for the past seven to eight years. If
I see 160%, I'd be like... Cumulatively or annualized?
Annualized. Annualized basis. Annualized 160%.
I wouldn't last a year. I would be like, take
all of my money and I will shut up. Take my money.
Take 25 % of the returns. So his personal wealth
made it into the 100 million RMB bracket as of
2015. And that is declared in the news. The AUM
that he was managing by the year 2015 was 1 billion
RMB. Wow. yeah yeah 10 times what he has that's
a lot of money so by that time so 2015 he already
started you know buying a few gpus just you know
for fun for fun yeah for fun for so we yeah we
don't know for sure if he bought the gpus because
his fund required it or because his personal
interest of just I don't know, wrench. He just
needs it as a X informational. What's his major?
Like he, he just likes exploring these stuff
and he bought some GPUs and back then there was
an export control. So, um, how many did he get?
I think our research said different numbers.
Well, research has different numbers. I found,
so 10. nvidia geforce in 2015 i checked the version
it seems correct geforce seems correct but i
don't know whether it's 10 or more than 10 so
i i got 100 from a source in china but that said
we don't know a lot about this guy in 2015 yeah
i was in a so somewhere between 10 to 100 right
uh for 2015 it's a lot it's a lot yeah so but
but we all know that for someone who is in the
year of 2015 already started buying gpus very
actively and knowing what they were doing with
gpu that falls in a very small group of people
definitely yeah yeah this is what like 2017 is
when bite dance and tiktok really kicked off
so this is they're only starting though yeah
And Zhang Yiming, I don't think he had any idea
that this would turn into... Yeah, they definitely
didn't have GPUs. So he was ahead of the game.
Yeah. We'll do an episode of ByteDance maybe
later on. And we'll find out maybe in 2015, like
how exactly how prepared Zhang Yiming was at
the time. So then two years later, remember the
money that he had, right? So two years later
in 2017, Lan Wangfeng's personal wealth, five
times, five X'd. It was 500 million RMB. And
the AUM that he was managing was 2 billion RMB.
So that doubled again. Yeah, exactly. And in
2019, he started... developing this special department
within Huanfeng that specializes in large language
model. It wasn't a large language model, but
it was more of a processing cluster that he started,
which is called the Firefly. And by Firefly,
by 2019, that's another time where he got more
cars, right? Yeah, so it was said that Huanfeng's
Hi -Fi fund used... 1100 gpus for the high flyer
one which is the ai the gpu cluster and supposedly
this 1100 gpu is all all used for the fund itself
but we don't know i mean i highly i think he's
running his personal projects i don't know yeah
yeah he just seems always to be working on something
else right it's it's a lot of gpus for for quantfund
that is the size that he was managing there's
i feel like there's definitely some that are
used for r d for other projects and for sure
there might be not It might not be the direction
where Deep Seek is heading today, but there seems
to be some exploration beyond just using it to
make money. Right, right, right. So yeah, so
that was 2019. What also happened in 2019 is
he was doing so well in quant fund that he was
given an award for being a top leading quant
trading firm in China. Was the firm how big is
it? It's the top, one of the top. It was one
of the top 10 firms in 2019. And in 2021, he
became the top five firm. But the 2021 was primarily
because of the size of the firm. The size of
AUM grew even larger. The 2021 was the highest
time. We'll come back to 2021. So 2019, sorry.
2019, he got an award as part of the top 10.
Right, exactly. In that award, he was managing
about 10 billion RMB in 2019. That's already
10x when he started in 2015, right? And the speech
that he delivered was called... the outlook of
China's quant trading, a perspective from a software
engineer. He's such an engineer. Oh, so he's
positioning himself as a software engineer. Yeah,
yeah, yeah. So he likes being a, you know, a
technocrat. We will get to this in a bit. Let's
once again look at how much money he was accumulating
in the next couple of years. So in 2020, you
know, Firefly One was well put into use and Firefly
One also created... In two years, 10x, he's 10
billion. So from 2019, he went from 10 billion
to 2021, 100 billion. When I look at the number,
it was the first time for me to count the zeros.
I wasn't sure, am I reading this number right?
But it was 100 billion by 2021. That's how fast
he grew. in one year with an already high base.
Yeah, that's true. That's a high base for a quant
trading firm developing at that speed. Because
usually if you were to be going on to a different
asset level, then you have to be shifting a lot
of your strategies. So another callback to an
earlier comment you made. This 10X, so 2020 is
the year of COVID where a lot of people are putting
money into investments. So this 10X partially
is because of return, partially is because of
new LPs joining. We don't have that exact breakdown,
but it is still very, very significant and very
impressive. Yeah, it is very impressive. You
know, 10X, I think I absolutely agree with the
reason that you just provided. 2021 was kind
of a... No one knows what's the best way to make
money. So putting money into a fund that obviously
can deliver results is a good idea. So 100 billion
RMB roughly translate into 14 billion US dollar,
which is a reasonably sized quant trading firm.
Oh, in the US? 14 billion US dollar. That's a
lot. That's a lot. And how well did he perform
in 2021? He was targeting the Chinese CSI 500
stock index, so index fund. He outperformed the
index by 50%, posting an annual return of 71%,
thanks to the AI -powered prediction model, Firefly
1, that forecasted which stocks would perform
better. This made Liang Wenfeng temporarily one
of the richest men in China, just the year 2021.
But very briefly, but very briefly. With 2 %
management fee, and if you're making that much
money, you're generating an estimated 200 million
management fee just for the firm. That's 200
million. He's still charging only 2%, which is
actually pretty cheap. 2021 was 2%. I don't know
for now. Maybe it had gone up. Then I feel like
that's pretty cheap. For this 75, if it's 71
% return. Yeah, it was 2 % management fee and
20 % carry. Okay. Yeah. But think about this.
Maybe he will be charging more management fee
for now, given that he has a deep seek that he
has to, you know, foster, like being the dad
of a very young kid that spends a lot of money.
Maybe, yeah. Maybe, yeah. How did Liang Wangfeng
spend the, you know, 2 % management fee? out
of the 200 million he spent 155 million to buy
10 000 pieces of nvidia a100 chips for firefly
2 that's going to come up he also spent 60 the
research fund on its ai lab which i think later
on became what's deep seek today but in 2021
he's already actively spending a lot of money
on a the card and b what else what other infrastructure
can be put onto the research lab so the management
fee and carry got reinvested into the future
business exactly well fun too and future business
yeah didn't take much now that i'm looking at
the numbers 71 percent means he 10 times right
but 71 is the return so actually a lot of people
put money in the fund for sure for sure for sure
a lot of lps join the fund later on yeah are
you seriously thinking about putting money i'm
not i can't afford it because yeah it's uh there's
the minimum threshold to enter is true true true
yeah this is not investment advice friends so
please invest yes very rationally if you're outside
of china you can't even yeah get in but yeah
but just something in very Interesting to look
at the historical returns for this fund. Yeah,
but remember the year 2021 because we're going
to come back to that. So let's do a quick summary,
right? In between Liang Longfeng's 30th birthday
to his 36th birthday, how much money did he make?
He delivered five times AUAM in three years,
10 times AUAM in four years, and finally... A
hundred times AUM in six years. Let's just say
this guy is constantly improving things and things
were upward trending despite like COVID happened
and a lot of competitors then joined the market.
As you said, a lot of Wall Street bros came back
and started. quant trading firms. He still is
very much in the game and delivering the ground.
Delivering more than most funds. Yeah, delivering
a lot of funds. So, okay, remember the 2019 speech
that he had very emotionally, sort of, I don't
know how emotional. As a software engineer. As
a software engineer that he gave on the outlook
of China's quant trading future. Yeah. So, I
looked through everything. A few things I can
tell. First, he likes Jim Simons a lot. Oh, Jim
Simons, the renter. Renaissance guy, the US equivalent
of Liang Wenfeng. Exactly. Liang Wenfeng is such
a fan of Jim Simons that he wrote the foreword
for the Chinese translation of The Man Who Solved
the Market. Jim Simons' book? Yes. Oh, wow. Well,
Jim Simons, he's a... Autobiography about him.
Right, right, right, right, right. That was written
by Gregory Zuckerman, a Wall Street Journal writer.
Oh. I can't believe you remember that. I recently
finished the book. So the core insights of the
speech that he had delivered in 2019, I can sum
up, I guess, three points, which is... He thinks
that there is going to be massive growth potential
for quant trading inside of China. It's kind
of true. Yeah, it's kind of true. This is only
the beginning of the industry. And Liang Wenfeng
highlights that the quantitative trading is becoming
mainstream globally. And he was comparing the
Chinese market with what's happening in the US,
like Bridgewater and AQR, Rentech, managing huge
assets. And he points out that in China, there
is just so much headroom with the Chinese quant
market. And comparing to the current AUM that
is being held in the Chinese quant firms versus
the trillions of dollars that is being managed
by the US firms. I think the number is that half
of the U .S., all trades in the U .S. are coming
from hedge funds, and China is much lower. So
by benchmarking, he thinks that there's a lot
of room for growth. Exactly, a lot of room for
growth. And second of all... a quick comment
on the efficiency of the models or the algorithms
that's being used. So Liang Mengfeng observed
that the industry in China is improving very
fast and it's roughly doubling every 18 months
due to Moore's law. But the average profitability
hasn't increased because of market efficiency
is also rising at the same similar pace. So this
means that the strategy that worked before is
not going to work as well in the future or over
time. So he thinks that there must be a shift
that is moving from traditional multi -factor
models, which is the one that we talk about,
to AI and then to more integrated frameworks.
So he thinks that innovation in this sector with
the algorithm will continue to go on and it will
be more complex and more robust to support a
more complicated and more efficient trading in
the market. So like more competition among his
peers are actually driving the market to more
sophistication. Going forward. Exactly. Exactly.
Makes sense. Yeah. And finally, and this is the
one that we sort of discussed very briefly before
we started recording, which is he then outlined
a short -term and a long -term strategy. So the
short -term strategy he thinks moving forward
is called the multi -strategy integration, which
is starting from now in the next one or two years,
you can't just be using one strategy to be able
to gain all of your profits. You need to be combining.
And the example that I said that you said was
not useful was combining high frequency with
some of the multi -factor trading. And you're
like, the Chinese market doesn't like high frequency
at all. So there's no way of integrating that.
Yeah, so I guess context for everyone who has
not traded in the Chinese stock market. The Chinese
government or regulators just allowed same -day
trading in the stock market. This is only the
stock market, so you can't trade anything. You
can't buy anything and sell it on the same day.
There's other ways to get around it, but it just
makes high -frequency trading in the stock market
very difficult. But commodities is fine. Commodity
is fine, futures and options are fine, but those
are very limited to non -retail traders and heavily
regulated because there's been a couple of incidences
in the past that created a lot of commotion to
make the regulators more strict. And the government's
crackdown on the within day or T0 trading in
the stock market, I think, is also a way to protect
the people who are in the stock market. Yeah,
the emotional retail investors of China, i .e.
my family. The volume is really large. And so
very much unlike the U .S. scenario where I guess
80 % of the stock market is consist of agencies
and institutions and 20 % are just individuals,
the Chinese is kind of the reverse. So you need
to be protecting the people, the majority of
the people who are in the market and that you
cannot encourage such behavior to dominate, right?
So that's kind of where that crackdown policy
is coming from. And OK, coming back to 2019.
So long term, he thinks that the industry is
going to move towards quantify fundamental analysis
and primarily tackling the inefficiencies that
is still in the in the market. And he says that
it's feasible and necessary as technical strategies
will become less profitable, to be honest. Yeah,
like just going back to fundamental analysis.
Sorry, it's almost like saying. uh high frequency
trading or quant based trading will need to make
way for like a mix strategy yeah i'm blurring
the lines between long -term quantum and long
-term fundamental trading yeah sounds like very
interesting very interesting yeah for it's i
guess unique to china yeah yeah um yeah i guess
very unique to china he's suggesting that long
-term trading or like i guess intrinsic value
value -based trading should definitely still
be there but in the meantime like quant trading
should still serve its purpose and should be
improving in its strategies to improve efficiencies
of the overall market anyways so the conclusion
that he had by the end of the award ceremony
is as a hedge fund, our mission, aka as HuanfengHF's
mission, is to improve Chinese secondary markets
efficiency. Which kind of, he did that. He has
to say that in a public setting. What is he going
to say? Like, we're not going to help the market
liquidity. True, true, true, true, true. That's
very true. So I think he did what he could, I
guess, from a market participant's point of view
or like as a leading quant firm's point of view
and also as a software engineer. I think for
anyone who's trading in 2019. Would have a positive
overview of the market. Because it's positive.
Yeah, exactly. Because it's positive. It's very
much up for trending. All right. So, all right.
So, moving on to the next chapter. sort of a
midlife crisis kind of chapter, right? Like we
all hate that chapter, but it's coming up. He
turned 35. 35. Yeah. So that's not old guys.
It's not old. 35 is completely not midlife. It's
a perfect time to take a challenge. Let's just
say. So 2021, remember 2021 was the best year
that he had, right? He was managing 100 billion
RMB in 2021. Firefly One was working just fine.
And in 2021, he became the leading quant strategy
hedge funds in China, top four to be specific.
But in the meantime, later on in 2021 was a serious
challenge and 2021 became the worst year for
Liang Wenpeng as well. So officially, what happened?
Officially, in November 2021, there was an incident
where the fund experienced continuous max drawdown.
And I'll explain that in a bit. The return started
to decrease. over and over again. And starting
from November, in one and a half months, the
asset that he was managing decreased from $100
billion to $80 billion. And it was still continuing
to decrease. It was so bad that Huanfang HF had
to issue a public apology to the investors about
the max drop -down. It was apologizing. and made
a public claim that it will continue to reduce
in size. It seems that, you know, better model
is needed. They cannot be managing such a large
amount of fund continuously. And, you know, a
lot of things went wrong. And the apology was
the only thing that was disclosed. Some other
funds also did equally bad. And then there were
guesses that they were using the same factor.
And, you know, if you're using the same factor,
then maybe it's not efficient. So everyone's
using the same strategy, but they're using the
same wrong strategy at 2021. Well, I cannot know
like what. would be the exact reason, but the
return for a lot of the funds at the end of 2021
were bad. And the only assumption that was fair
to make was maybe people were banking on the
same factor and then the factor did not yield
as good of a return. Yeah. So I guess what's
the max drawdown in the quant trading and why
is it very bad for quant funds? A drawdown is
a decline in value of an investment or portfolio
from its peak value. it sounds bad but it's also
i think there's two factors one is the return
actually declining yeah the other one is i can
actually as an lp take out the money true um
so a mix of both like it kind of it's in a circle
so the return is not good then i'm gonna take
my money out exactly i think there's specific
times where you can take your money out like
you can't just take it out every it's not like
stock where you can sell it every day right right
right um but during one month 20 that's a lot
that's a lot yeah it's also a very rapid loss
of i think credibility yeah you you just you
know went up to 100 billion and But you were
not able to sustain that. So I think that's a
question for a lot of the con trading firms that's
up and running these days. Like how long can
you be sustaining your sort of level of asset?
Exactly. Yeah. I mean, it's bad enough that he
has to make a PR statement. Yeah. The challenge
begins 2022. If you were Liang Wenfeng, you'd
be thinking of, so, you know, how do I continue?
How do I proceed from here? Are there ways to
pivot? So here is the actual circumstances that's
in the year 2022. Externally, there is continuous
government crackdown, the one that we just talked
about. The government does not support or like
any type of financial arbitrage in the stock
market. And Liang Wenfeng, he's an initial part
of the strategy. We're all related to the stock
market. But obviously, there is the commodity
bit. But a part of his strategy... probably will
have to retreat because of the government crackdown.
Yeah, so the Chinese government has... I guess
you can say a bigger hand. A big invisible hand.
A big invisible hand on the market. Yeah. And
if you look at Bloomberg News, you will find
out that maybe as early as 2020, there are continuous
news that's been published about how tight the
control will be for Chinese quant trading firms.
It goes on and on and on. The more profitable
that the quant traders find, I guess the more
regulations there must be. until today i think
commodity is still very much welcomed in the
market but it's just stock market there's a lot
of rules and regulations yeah yeah it kind of
makes sense you have to protect the retail investors
a bit more the retail investors tonight yes um
okay so also what happened externally is uh by
the end of 2022 november 30th if you can remember
what happened on that day is uh the very first
version of chat gpt uh oh Published by OpenAI.
Yeah, that is the end of 2022. It's been three
years? Yeah, it has been three years. But the
November 2022 Chachaputi wasn't the... chat to
beauty later on like it wasn't it was 3 .5 it's
what the one that made all the news right yeah
yeah yeah 3 .5 exactly and so that was the external
circumstances right so we were still locked down
back then we were still locked down we were not
even we were what were we doing we were we were
none not november but i think november december
is when uh everything kind of went haywire and
yeah yeah yeah country opened up exactly so uh
so while we were going into A lot of chaos. ChatDVD
is being published. And also, the fund itself
has not been performing very well throughout
the second half of 2021. Performers were declining.
And according to a lot of financial news, they
word it as partly due to its AI mistiming trades
on the market. Not sure how to understand that.
But, yeah, what do you want to say? So, I think
the government published, there was also, like,
stock -wise, the retail investors are also not
so happy about their return. I remember the government
kind of blamed it on the hedge funds. Yeah. So,
yeah, that was during that time. Yeah, yeah,
yeah. I can imagine. It would make sense. Yeah,
it's a lot of money lost. Right, right, right.
A lot of money that's being misused, mistimed.
Yeah, but it's really, if you look at the historical
fund return, it was pretty, it was briefly. It's
decent. Yeah. It's decent. It was briefly down
in 2021. I think people did panic and withdraw
their money. But later on, the fund went on and
it was doing decent returns. I did look at some
of the numbers of the annualized return all the
way from 2016 to 2021. In between 2016 and 2018
for HF, the neutral strategies annualized return
was in between 20 % and 30%. It was great. Pretty
good, yeah. And 2019 to 2020, because of... flyer
flyer one the annualized return and for the csi
500 equivalent etf it was a it was between 30
and 40 but in 2021 that return dropped down to
15 oh so it's still positive i thought people
are like it's still positive but that may be
the best performing product uh if you were to
be looking at all of the products that's from
hf uh i'm i can maybe it's also there are funds
that are not making money and this fund the the
top money making fund is 15 exactly which is
decent by any means yeah which is decent even
with an etf i guess so we are moving back to
2022 So I think by year 2022, because of all
the internal and external reason, and also I
think with Liang Wenfeng's personal interest
development, he was finally able to decide that
I think it's time to make my LLM company an official
thing, a .k .a. the AI lab that was within HF
fund. Moving on to... 2023. 2023. 2023 was the
year that the artificial intelligence research
branch and the hedge fund split. Exactly. Or
he created a new company that is focused on.
artificial intelligence. Exactly. Take us from
here. All right. So the AI Liang Wenfeng was
born. So 2023 May is when he registered the company,
which is the parent company of DeepSeek. 2023
may is when the company was officially announced
deep seek with the announcement actually the
statement was they're focused are on agi artificial
general intelligence it was never about application
it was always about infrastructure this is from
day one when they established the company that
was the goal very far -sighted they're very very
similar to sam oldman exactly so that's actually
very different from most companies in China.
Most companies never claim that I'm going to
have a vision of AGI. Right, exactly. Like I'm
going to establish a company that is going to
be benchmarked against open AI, against anthropic.
I'm not about applications. I'm not about short
-term ROI. This is very, I would say like non
-Chinese company mindset in general. non -conventional
non -conventional exactly um because over the
past 10 years with the rise of tech companies
in china it's always about short -term roi it's
always about can you the the p firms are so spoiled
that they want ipo in like within seven years
right that's uh so that was a pretty bold statement
to make and he actually stuck true to the statement
to this day. So they published their first LLM
model six months later in November and it's called
DeepSeek V1. That made zero slash. Zero slash.
Zero splash. I mean, nobody heard about them.
Maybe people in the industry within China. Well,
no, that is a boutique, cute little model that
the Chinese has made, but nothing threatening.
Right, exactly. And the first wave they really
made was mid -2024, which is really one year
after they established a company. Still pretty
fast. And that's when they published DeepSeek
V2. and uh there's kind of two innovations in
this model that created some splashed in the
research market so it's still not uh general
yet like we haven't heard about it at that point
right as a market as users of of uh language
models so the first innovation is called mixture
of experts moe and it's basically an idea that
was That started in the 80s or 90s. Nothing new.
It's nothing new, but they used it, whereas a
lot of the other models didn't. And to put it
into application is something that a lot of people
applauded them for. And the idea is, usually
in an LLM model, you need a... billions of parameters
right and that requires a lot of computation
power so moe architecture actually only calls
upon the experts so only if you have so right
now their their model has six billion parameters
i only call maybe a fraction of that that are
my experts to this kind of questions and whatnot
and so that reduced the cost by a lot yeah right
um so that made some splash in the in the research
within the industry right the other innovation
was called not really innovation but also something
that has used before is called mla but this one
they kind of created them i think that they're
the first one to really use this in llm models
and this is basically a librarian i'm trying
to put this into like simple term but like if
you rather than keeping a library at your disposal
with all the books you kind of condense everything
into this book of glossaries but then you're
allowed to zoom into any item and still extract
the necessary information so another cost -saving
effort to reduce training costs and a combination
of those two and some of the other innovations
kind of made some splash in May of 2024. Okay.
And... Oh, I can't believe it's a year ago. It's
only a year ago. Oh, wow. It's exactly a year
ago. Yeah. Wow. Okay. The reason why all of their
efforts so far has been around cost saving. It's
not because they don't have money. We just talked
about how much money they have. Right? Make them
rain. Yeah. It's not because they don't have
money. It's because they can't access... The
top GPUs. The top performance GPUs. Because as
of 2022, you can't get any of the A100 cards
and video cards into China. Welcome to the band
world. Yeah. So that's why he actually, the team,
DeepSeek team, actually made an effort. to reduce
costs and that's what later shocked the market
start to process the problem from a different
perspective if i can't buy more how about i just
use the the downgraded version but use them more
efficiently and use less computing power exactly
yeah so it's ironic that the ban of high -performing
gpus pushed them forced deep sea to be innovative
which later shocked the entire us if not global
generative ai industry exactly yeah prior to
them i think all the other major um ai or aj
or llm companies in the us they were competing
on the size whoever has more gpus or has a larger
data set or essentially have more money can compete
further that is not wrong But the fact that Deepsea
can do that made people realize that, oh, you
don't need to have the most groundbreaking GPUs
to be creating a very efficient and useful LLM.
Yeah. Cool. Exactly. So then this is V2, then
half a year later again. So every half a year
they come up with another version. is the one
that everyone kind of caught the world's attention,
which is two things, actually, a combination.
So they published V3, which is an extension of
V2, which is their general AI model, in December
of 2024. So about seven months later than V2.
In January... they published, again, another
model called R1, which is the reasoning model.
Both together is what really made the news, but
what I think that actually caught everyone's
attention is because both Vivo and V2 are not
mass releases. They were not on the app stores.
There was no website that you can access to ask
the LLM questions. True. Right? Users that are
not in the industry that doesn't know how to
use GitHub to clone the model itself don't have
access to it until December. Right. And that's
why it blew up. I think most people talk about
the shock factor as, oh, it appeared as the top
ranking app in terms of download in the App Store.
And that's how we know about it. Right. Right.
And that's how I knew about it. And that's, that's,
that's what we, the, what we experienced during
the Chinese New Year time. I know. The news published,
it was January 20th or something or 25th. Yeah.
And it came all of a sudden and the next day
everyone is talking about it and it show up in
the app store. It's literally when everyone is
on vacation too. Yeah. Yeah. Like you don't do
that to people who are on vacation. Like everyone
then has to go, like if people who are, you know,
trading in, in the u .s market they have to like
the chinese trader who are trading in u .s market
they woke up that day thinking that they're on
chinese new year break oh man have to go back
have to go back to work and sort out the nvidia
chip exactly exactly so that that was one other
shocker um obviously There's the price factor,
right? So I think you might remember the $6 million
training cost is what was published everywhere.
How did the $6 million came about? Because it's
an open source model, they actually publish a
lot of stuff. So along with V3, they published
an article in their blog saying a cluster of
about 2000 Nvidia H800. So H800 is the China
version of 8800 after the export bans. And it
used a total of 2 .788 million GPU hours. And
they estimated if you rent this GPU in the market,
it's about $2. So 2 .788 million times $2 per
GPU hour, that's 5 .6 million USD. That's the
number that was quoted. That was the number that
every single article. ended up publishing. Everyone
thought they trained a model for $6 million.
And I think some article in the immediate aftermath,
the first two days, people were comparing this
number to the billions of dollars that OpenAI
spent. And I was like... What are we doing, USA?
The tens of years that Liang Wanfeng spent in
hoarding the cards and doing the trainings himself
and, you know, in the past 10 years and hiring
the talents and hiring the people, none of that
was counted. Yeah, none of that was counted.
That's not fair. Yeah, and I think a day or two,
I mean, immediately people start to kind of realize
and publish articles, but... it was too late
for the stock market. Just poor financial reporting.
Yeah. That caused the stock market to react.
So the stock market reacted. Nvidia saw its worst.
I think the stock market overall saw its single
stock worst return over one day, which is 17
% down. And then obviously people started to
realize, or they realized, but people started
to publish articles saying that, you know, they...
this is just the last run of that model, right?
There's so many other costs, like human resources,
for example, research and development, everything
that went on before counts. Yeah. So if you add
that up, I think there are some numbers that
puts this whole project between 500 million to
1 .6 billion, which... Overall, it's still cheaper
than U .S. companies working on the same things.
But the shock factor was late. No one expected
a Chinese company that is not ByteDance, not
Alibaba, not Tencent to come up with a model
that on performance base matches that of ChatGPT's
latest, probably better than Lama, and is open
source. And it's free. And it's free. It's not
free for API, but it's free for everyone to use.
Even the reasoning model is free for everyone
to use. Stanford published an article that says,
we thought China was way behind on all the generative
AI models that are coming up. Effectively, China
is behind by three to four months with a lot
less chips. Be alert. Yeah, all of these developments
are because of... Liao Wenfeng couldn't find
better resources. There's no chips, and... If
you don't want American educated PhDs, we'll
just find homegrown ones. Yeah, that's actually
a really good point. Let's touch on that. He
doesn't want... No, we're not saying that. He
doesn't want American PhDs. Okay, sorry. I said
that. He never said that. I don't think he ever
disclosed that we don't want international talents.
I think it's because he wanted, but he couldn't
find. Or there's just not as many in the market.
He's not by dance, right? So it's hard to find.
Hard to attract talents. Yeah. It's still a small
lab. Yeah. and he has a lot of options because
it's a small lab like they're quite selective
about talent there's someone uh complaining online
about like i'm a huawei engineer top of the top
of my school eight years of working experience
deep seek never gave me an interview like hey
um so there is a deep seek hiring philosophy,
which is super interesting, which is, and not
speaking for them that but this is from coming
from stuff we gather from the headhunters that
are talking about their hiring strategy, which
is, they tend to hire folks that are new graduates,
or folks that are still in the last two, three
years of their PhD program, who studied domestically
in china which is very well i guess counterintuitive
because most of the other companies are actually
trying to find talent from open ai from uh anthropic
from from google and meta right and try to get
them to come back to china the oversee students
uh and to help the Chinese AI industry grow and
offer them probably two times of their salary
that they're making in the US. And they all went
completely a different approach. And it worked.
So it's kind of ageism, but they actually refuse
to hire people that has more than five years
or eight years of experience because the philosophy
is that I'd rather hire someone who... are more
creative or who doesn't have necessary experience
but are smart and think about you know how to
solve problems rather than someone who is who's
good at what they're doing but have a specific
way or is used to doing things a certain way
because this person is experienced so it's very
interesting yeah yeah willing to hire new and
innovative people without any experience yeah
yeah And so they currently have, I think, about
150 or so employees. And I think a majority of
their employees are 20 -something -year -olds,
right? So it's very interesting. And I look at
their top maybe 20 or so. Most of them are from
Beida or his Zhejiang University. So Peking University,
Beida, or Zhejiang University. And a lot of them
has PhD degrees. and they had their PhD degrees
domestically. Yeah. Yeah. Gives a lot of hope
for the LLM industry within China. But I think,
you know, before recording, we both shared this
very critical view about DeepSeek, right? Obviously,
you know, great company, made a hit, has been
working on this for a very long time, have their
way of accumulating wealth, have their way of
hiring talents. But the question that we both
had was how long they can... or how long they
can remain in this industry. What are your thoughts
on that? This race will not end in the next two
years. It will be in a while. And obviously,
he can still have a group of talents and he can
still be... Huanfang HF Fund, as of this point,
is still up and running, but it's just much smaller
in size. What do we think of the future of DeepSeek?
How is that going to go? Personally, I'm very
concerned. But there's a few factors. Let's run
through some of them. So first of all, there's
a whole cost issue, right? He managed to keep
the cost down. And he cut down the API cost so
much that ByteDance, Alibaba, OpenAI has to respond.
And it's free for everyone. So I'm not sure if
they're actually making money. I don't think
they are starting to. making perhaps a little
but right yeah they're not on the you know the
extremely aggressive end of commercialization
yet yeah and they're definitely on the cost reduction
front like they could have priced their api a
little bit higher but they chose not to because
the i think the end goal is agi and so this is
kind of all the path toward it but he doesn't
have funding he uses his own funding or he uses
funding from from uh hf fund an article i read
that is that he actually at the beginning maybe
2023 2022 when he wanted to establish deep seek
He did go talk to investors to think about, you
know, maybe I shouldn't fund 100 % myself. It
should be, you know, us with the investors. We
can maybe tap into their resource, et cetera,
et cetera, which is most startups, AI startup
path in China. DeepSeek didn't end up doing it
because with money comes with obligations. And
usually these obligations means commercialization
early on. And return. And return. And that's
not the intention. for a company that is looking
at AGI. That's not what OpenAI has achieved.
That's not the fundamental goal of Liang Wenfeng
at this point. Liang Wenfeng's company feels
very much like a university lab. Exactly. It
doesn't feel like that he wants to be delivering
anything commercial just yet. He's just hoarding
a lot of really talented students and say, do
whatever you want. Yeah, it's a research lab.
It's a research lab funded by a really rich head
fund investor. Interesting. Okay, I like that.
Yeah. You just don't call it deep sea. You just
call it a research lab. I think that's a more
accurate depiction of what it is. Exactly. But
the question is, this business model will only
work when... You have enough belief in the AGI,
in achieving AGI through LLMs. And you're going
to continue to make money on your fund to supply
this or you find another investor. But like you
said, with investor come with obligations and
can you take on these obligations without disrupting
your research idealism? So, I mean, research
idealism meaning that the employees of DeepSeek
apparently can run any model without pre -approval.
They're just like, I have this idea. Full freedom.
Yeah, we have enough chips back then. Try it
out, you know. Can this continue, right? Especially
yesterday, NVIDIA, again, their report, their
Q1 report came out and said, hey we can't deliver
h20 anymore to china because of a recent restriction
the u .s government has put on us again so context
china ordered about i think a million unit or
so of h20 which is the chip that nvidia created
especially for china to bypass the recent ban
on gpu and china already ordered I think last
year China already got one million or so, and
then they ordered another million or so. NVIDIA
already created some, right, in their inventory,
about to ship to, some already shipped, some
didn't ship. U .S. government, like April 9th
said, hey, we're going to, you're going to need
a license to ship that. We might not give you
the license. I don't know, indefinitely. So one
month later, NVIDIA's financial report. says,
we got all of these inventory, we can't ship
them, we're going to write them off. China is
not getting those H20s. China is not getting
those H20s, which means DeepSeek is not getting
those H20s. So there's a lot of roadblock to
the continual success of DeepSeek. But going
back to January of this year, When they made
the news, besides what we discussed, which is
hitting the App Store top of the chart and just
getting tractions, the $6 million costs that
shocked everyone for training models, the other
thing that really shocked the community that
is actually working on generative AI models is
R10 and also distal models. let me talk about
r10 first so r10 is so the r1 is the reasoning
model that if you go on deepsea .com you can
use it if you click on the kind of i think it's
called reasoning or whatever button right that's
the equivalent of the o1 model published by chat
gpt that one has a dash zero model which uses
a technique that differs from every other model
out there that's doing reasoning. And the best
way to think about this is the R1 model or the
most reasoning models out there are actually
brilliant thinkers and mathematicians who have
gone through formal education, who have been
taught how to think through a heavily human -evolved
post -training process where people like us,
Tell the model, I think this is good, I think
this is not. Human reinforcement. Exactly. Human
feedback. Human feedback to make the model perform
well. The R10 is unlike someone who went through
a formal education, someone who taught themselves
math and physics and everything else. There was
no human involved. the model itself actually
solves complex questions by asking themselves,
is this the right way? How do I go about this?
So you kind of reward yourself as the model every
step of the way. So that was kind of a different
way that was always discussed in the research
community and DeepSeek proved that it works.
In terms of... Well -performing Chinese models.
Actually, Alibaba published one that's called
Quinn. That was pretty good. But it didn't make
the same splash as DeepSeek because it really
was going along the same lines as most US models.
DeepSeek actually really made a different innovation
and folks in the US was looking at how this works
and they're like, wow, hey, we should try that.
So that was one thing that they did. kind of
really shocked everyone. The other thing was,
it's the first time that you can distill a reasoning
model. And what distilling means is you have
this big model that sits in the server somewhere
that as a user, you can kind of, if you go on
deepsea .com, if you use any of the apps, you
can call on the API, right? That big model requires
It's the 6 billion one. So it requires a lot
of resources to train and to use. But as an individual
hobbyist, you can kind of distill that model
and make your own mini model using instead of
600 billion parameters, you can train yours on
7 billion parameters, for example. And you can
recreate that model by kind of distilling it
and putting it on your MacBook. Not our MacBook
Air, but like a better MacBook. But essentially,
it shows that how open source and how accessible
it is. Exactly. So that more people can enter
into this ecosystem. And then the more developer
it is, the bigger the community is, the less
that people will switch to other closed sourced
LLMs. Right. Which is very smart. Yeah. And the
fact that they went with open source actually.
which kind of also shocks everyone um so so people
can then use this and distill the reasoning model
and the only other comparable reasoning model
if you remember is no no not the open source
one the only comparable one is 04. right which
is closed source closed source is harder to distill
the model because you don't have the parameters
right so you actually have to create a billion
times to really get a good distillation, which
is very costly by any means. So there's no really
good way. So DeepSeek R1 was the first distillable
reasoning model for the community. And imagine
all the copyists just going like, yeah, cheap
model. Let's get it on my laptop. So it ended
up, and I think the CEO of Hugging Face actually
quoted this. After the DeepSeek models are once
published, over 500 derivative models were created
on Hugging Face. And these 500 or so models,
and this was two months ago, so the number probably
went up, racked up a total of 2 .5 million downloads.
Wow. Yeah. This is the developer community. This
is the developer. 2 .5 million for the developer
community. Right. Wow. Yeah. And guess who? open
source deep six model who is the one and only
so uh perplexity oh really yeah so perplexity
actually uh used open so if you go to perplexity
you can actually find deep seek oh yeah okay
And they actually did it not for completely free
because they have to train it, but it's pretty
free and DeepSeat didn't get a cent. And that's
the beauty of open source. So what Perplexi did,
which is super smart, is that they... I guess
this is called retrained. So they open sourced
a version of the DeepSeek R1 model that's been
post -trained to provide unbiased, accurate,
and factual information. And by unbiased, accurate,
and factual, it means they've created prompts
that would trigger censored responses. Censored
meaning party censored. And they generate a response
to these prompts to make the model answer these
questions as if they were not censored. Interesting.
So they created an uncensored version of DeepSeek
hosted in the U .S. on Perplexity servers and
marketed that successfully. Oh, that's very smart.
It's a great way. And all they have to put up
in terms of cost is the post -training and server.
All in all, I do think this is a very successful
attempt for a Chinese LLN company. Definitely.
I like that Liang Wanfeng is running the company
like a college lab. It's giving the freedom to
the researchers. And I like that he open sourced.
He even embraced with an open mind, I think.
He's not making money off of all the copies that's
being made, but he is letting people to use it
and doing their own creativities. So I think
that takes a lot of courage. Yeah. But I also
think this is a bit calculated. For sure. Because
you are not the incumbent. Definitely. You're
trying to... become the incumbent and the best
way to do it is to do it with a community and
to build a community you open source right yeah
so it's actually quite smart yeah but i guess
the the question is um deep seek i think is very
far far away from um you know being a public
company i think it still takes a lot of years
and i don't even know whether that's the way
for them to go but with the information that
we were able to find online we can only paint
you a very faint picture of what it is and what
it is not. And I would highly recommend that
we just wait it out and see what could be the
future hold for them. I really look forward to
what they will be doing. But I do think this
is a highly competitive arena. And what Liang
Feng is doing, he's definitely trying his best
to, you know, finding talents and, you know,
bring up the most interesting, I guess, algorithms
or the models that he can create. I think it
did a really good job to stir up the market.
And it's sending positive momentum to the research
industry, to the research community. And I hope
that despite all the GPU bans, they can continue
or other companies in China can pop up and challenge
the incumbents to create more competition, to
create more innovation at this space. Thanks
to DeepSeek, actually OpenAI's API price dropped
a lot. right and thanks to deep seek now we have
a lot more options when it comes to chinese llms
so i i don't know about you but i like i know
dc has his performance limitations but i I tend
to ask a lot of Chinese questions to DeepSeek,
and I find that you get better answers than asking,
say, chat GPT. Right. Yeah. Well, I take all
of my AI tools with a grain of salt. DeepSeek
does well in some things. It can do some very
basic secretary work for me. Well, maybe I'm
just asking too simple of a question, but I think
it still hallucinates a lot at this point. Yes.
But I would hope that it will continue to improve
in the quality of answers that it can provide.
And I really do enjoy the thinking process. I
always open that thinking bar. It's like it responses
quite well to different questions and different
contexts. So it kind of shocks me sometimes that
the type of responses the thinking process would
label. So today I asked a question and somehow
from my tone. I think the thinking process that,
you know, this question seems quite urgent. What
were you asking? I mean, it was just a really
like brief. I didn't type as much. It misunderstood
me. But the fact that, you know, because my question
was very brief, it somehow concluded that maybe
I have a sense of urgency. It's quite interesting,
right? It's so akin to a human that it's... a
little bit scary at this point it's trying to
understand like what what else you're trying
to convey to the machine yeah it's been very
fun researching for deep seek super fun yeah
super frustrating but because it changes all
the time it's like two people that have no quant
trading background no limited llm background
we're llm users doesn't mean we're Developers,
right? I think we have to thank the folks that
are behind our research that helped us with fact
-checking. Well, thank you for all the QuantTrader
friends that I've reached out for your continuous
support and not hating me as a friend who is
constantly asking financial questions. And thank
you for all the friends that I reached out that
was helping me. answering my questions with a
12 -hour time zone difference. And if you have
any questions or comments, please do leave us
a message. And if there's any other rising companies
that you're interested in hearing us talk about,
please do leave a comment as well. We'll see
you in the next episode. Bye. Bye.