May 20, 2025 • 18 mins
Despite the global hype around China’s DeepSeek, very little is known about the man behind it – Liang Wenfeng.
On today's Big Take Asia Podcast, host K. Oanh Ha talks to Bloomberg's Saritha Rai about the tech founder who led DeepSeek to the frontline of AI advances and what the company’s rise tells us about the battle for AI dominance.
Further listening: Why DeepSeek Sent Nvidia, Other Tech Stocks Tumbling
Watch, from Originals: How China’s DeepSeek Came for Big AI
See omnystudio.com/listener for privacy information.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Bloomberg Audio Studios, podcasts, radio news.
Speaker 2 (00:08):
Earlier this year, a new product from the Chinese AI
startup Deep Seek, shocked the world and rattled Wall Street.
China's deep Seek is freaking out the AI world right now.
Techtoks tumbled as It's absurd to the top of the
download charts. But despite the global attention, very little is
known about the man behind deep Seek. Chinese entrepreneur Liang Wu.
Speaker 1 (00:33):
Liang Wenfeng is certainly a mystery figure.
Speaker 2 (00:36):
Bloomberg crit the rye covers artificial intelligence in Asia.
Speaker 1 (00:40):
He's certainly one of the most inaccessible and low key
tech entrepreneurs that I've come across. Just to illustrate how
private he is, we weren't able to find any pictures
of him on the internet when we scoured through his
website and all of that. But finally appear in a
really high profile meeting with President Sheijeng Peing, and that
(01:05):
picture got out into the world and he was everywhere.
Speaker 2 (01:09):
What does this man of mystery look like?
Speaker 1 (01:11):
He is slim, wears glasses, but doesn't talk much. Baby faced, Yes,
I think we could describe him like that.
Speaker 2 (01:25):
Deep Seek rarely answers questions about Leang citing his privacy.
But Sarita and her colleagues were curious about this man
whose AI systems turned the tech world on its head.
So they spoke with dozens of people familiar with his work,
from former employees and fellow researchers to investors and insiders
in the industry.
Speaker 1 (01:44):
And what we found is that, yes, he is extraordinarily
low key, very shy, but extraordinarily driven and talented and passionate.
And I think he has somewhat taken on Deep Seek
as a sort of a mission to establish China in AI,
trying to make sure that China is the force to
(02:07):
reckon with in AI.
Speaker 2 (02:12):
Welcome to the Big Take Asia from Bloomberg News. I'm wanha.
Every week we take you inside some of the world's
biggest and most powerful economies and the markets, tycoons and
businesses that drive this ever shifting region. Today, on the show,
who is LNG One Fun, we learn about the mysterious
tech founder who led Deep Seek to the frontline of
(02:34):
AI advances. Plus what is the company's rapid rise? Tell
us about the US China artificial intelligence race. Sriita, thanks
for joining us. I'm fascinated by AI. You guys did
such an interesting job with a story. I wonder if
we can start with who is Lang one fung? What
(02:54):
do we know about his roots?
Speaker 1 (02:56):
So? Liang is about forty years old in a small
village called Middling in the Guangdong province. His parents were
school teachers, mainly taught primary school kids. He was extremely
bright and went on to study at Xijiang University and
(03:18):
then also did his master's there.
Speaker 2 (03:21):
At Jujiung University. Liang and his friends immersed themselves in
all things tech, machine learning, signal processing, electronic engineering. They
even developed programs to trade stocks during the financial crisis.
After graduating, Liang joined forces with two of his classmates
and set up a quantitative hedge fund called High Fire Management.
Speaker 1 (03:44):
So quant funds basically work with mathematical models and statistical
analysis to do stock trading. Humans are not involved in
taking the decisions. At its peak, High Flyer Management was
managing something like fourteen billion in assets, so it was
quite a sizeable fund, and in its most successful runs,
(04:07):
it was providing annualized returns averaging thirty five percent to
its investors, so I would say that it was doing
very well.
Speaker 2 (04:15):
Indeed, according to former employees. High Flyer had a geeky
startup culture. Its early job postings moosted of attracting top
talent from Google and Facebook and said they were looking
for math and coding geeks with quirky brilliants.
Speaker 1 (04:32):
The early job postings also referred to Sheldon, who is
this very awkward main character in the prominent American sitcom
called The Big Bang Pory.
Speaker 2 (04:46):
For example, I cry because others are stupid and it
makes me sad.
Speaker 1 (04:51):
Sheldon has a legend of fans and it's extraordinarily funny
without meaning to be. So, you know, the whole culture
of deep Seek in the early days revolved around recreating
some of that geeky nerdy culture. They were free snacks,
poker game nights. Everybody was dressed in T shirts and slippers.
Speaker 2 (05:13):
Sounds like a great place to work.
Speaker 1 (05:14):
Yeah. It was really a very unorthodox startup culture, unlike
what you'll probably see in the big tech companies in
China such as the Aliba Bay and the Tencent.
Speaker 2 (05:25):
And how did Liang transition from doing quant financials into
AI and building deep seak.
Speaker 1 (05:30):
Liang was always extraordinarily interested in machine learning and artificial intelligence,
and then a few months after Open Ai you know
launched chat GPT, the chatbot that became an overnight global success.
It was then the spring of twenty twenty three, a
(05:51):
few months had passed after the launch of chat GPT,
and Liang then announced that deep Seek would be set up.
In its early manifesto, deep Seek talked about shunning mediocrity
and tackling the big challenges in AI, and of course,
(06:12):
ultimately cracking artificial general intelligence.
Speaker 2 (06:16):
The manifesto also laid out deep Seek's ambition to position
China as a leader in cutting edge technologies.
Speaker 1 (06:24):
You Know, Liang has given two interviews, rare as they
may be. In both those interviews, he's talked about bringing
China's AI ecosystem to the forefront of where the world is.
You know, China has been accused constantly of being a copycat.
He wanted in AI China to chart a different path.
Speaker 2 (06:44):
Deep Seek worked fast since twenty twenty three, It's released
over half a dozen AI models and helped pioneer a
technique called sparsity, which enabled those models to train and
run more efficiently. Developers started to take note then earlier
this year. Back to that top.
Speaker 1 (07:02):
Story, now deep Seek shaking up global tech lower technology
co host when they released their reasoning model R one
that cost such an upheaval in the industry and coused
a trillion dollar stock market meltdown. That's when the world
really started paying attention to this secretive AI entrepreneur in China.
Speaker 2 (07:29):
And Trita. What is show groundbreaking about Deep seeks are
one model?
Speaker 1 (07:34):
The AI industry until recently was always about billions of
dollars spent in building the infrastructure, the data centers, the
graphics processing units in the data centers that would train
these models. But what Deepsek did was show that its
models could match or even outdoor on some benchmark measures
(07:57):
what the latest open AI or anthropic models were doing,
and with far less computational power, with far less resources,
and as Deep Sea claimed, with far less capital as well.
Speaker 2 (08:14):
So how did Liang and his team manage to achieve
true innovation at what it says is a fraction of
the cost? And what does deep seek success say about
the AI race between China and the US. That's after
the break. For much of the last decade, the US
(08:41):
has tried to restrict China's access to semiconductors. Tensions reached
a fever pitch in twenty twenty two and the following
year when Washington targeted Beijing with two rounds of chip
export controls in Vidia and shares.
Speaker 1 (08:55):
Some semiconductor companies have slumped today after the Biden administration
said it would tighten restrictions on exports of AI chips
to China.
Speaker 2 (09:05):
That limited sales from American firms like Nvidia, whose cutting
edge chips are used by tech companies to help train
their AI models. The move presented a significant challenge for
developers in China, but as Bloomberg's read the rises, it
also forced them to develop workarounds.
Speaker 1 (09:23):
Necessity is always the mother of innovation. This has been
proven by AI developers in China. Never mind the export cubs.
They've still gone on to build good models that have
benchmarked with the best around the world.
Speaker 2 (09:40):
And one of the most innovative approaches from Deepseek is
a sparsity technique we mentioned earlier.
Speaker 1 (09:47):
Now. Sparsity is something to do with building a model
without having the high end computational power. It's when a
large language model doesn't have to be entirely harnessed to
give a nan to a query. Instead, Liang and his
fellow developers tried to apportion the expertise of the model
(10:10):
into smaller expert groups and then only harness those groups
that required to be used. So in doing that, they
made it much more computation efficient and also much more
cost efficient.
Speaker 2 (10:23):
Is it basically, instead of using your whole brain, are
you using just certain parts of your brain to do
that computation.
Speaker 1 (10:31):
That's exactly right on? You know, instead of entirely using
every little gray cell in your brain, it only fires
up those neurons or little portions in your brain that
contained that particular field of expertise and then bring that
to you know, respond to a query or give an
answer to a particular question, whether it's a command or
(10:53):
a coding question.
Speaker 2 (10:56):
The sparsity breaks through impressed Deep seeks competitors, but its
price point is what ultimately made headlines. Deep Seek said
it cost them just five point six million dollars to
train its V three model. That's far less than they
estimated one hundred million dollars Open Ai spent on its
most advanced version of chat GPT.
Speaker 1 (11:18):
Now, there is definitely a whole lot of skepticism around
that number because just the intrastructure, the training of the model,
the talent, and the time it takes all of it
adds to quite a sizable sum of money, so the
skepticism is warranted. People have estimated that there was no
(11:39):
way Deep Seak could have pulled that off without at
least a billion dollars or more.
Speaker 2 (11:44):
Also in Deep Seek's favor is that AI startups like
it have a staunch ally in China's government and present.
She Jinkin Sriita says she sees generative AI, robotics and
other high tech ambitions as beneficial to the state's agenda,
part of a larger push for self reliance in key technologies,
and Deep seax success has spurred much bigger rivals such
(12:07):
as Ali, Baba, ten Cent, and byte Edance to release
their own AI models. Srida deep six model is entirely
open sourced at this point. That means any individual or
company could incorporate deep Six's algorithms into their own programs.
Why did the company choose this approach and why is
that important open source?
Speaker 1 (12:29):
On one level, you could say that it is democratizing
AI and taking it out into the world, But let's
not forget that China's AI models would have otherwise found
fewer takers around the world if they were proprietary models
and were on par in terms of what they cost
(12:51):
with Western companies such as open ai. By making it
cheap and by making it open source, China allowed people
around the world to quickly take a look at the
models and begin using them, allowing them to be adopted
much faster in the business and AI ecosystem, thereby outdoing
(13:13):
the likes of OpenAI. Now that's huge. It's not only
about democratizing models, it's strategically about making sure that you
cut out your competitor by making things so cheap that
the world adopts it quickly and then it becomes mainstream.
Speaker 2 (13:33):
As a result, Microsoft and Amazon both offer deep seek
on their cloud services, and deep seeks models have been
incorporated into Perplexity, an AI powered search engine that also
offers models from open Ai and Anthropic.
Speaker 1 (13:47):
There is definitely a question about, you know, how fast
AI is advancing, and there's a fear around the world
about having all of the controls rest only with one
or two companies in the world. I think that was
what jep Seek and others were trying to put out
a message in the world, saying that all of the
controls cannot be left to one or two companies and
(14:09):
the proprietary models that they are building, it should be
much more democratic. Therefore, I think the open source philosophy
is about de risking concentration and allowing more people to
build with technologies that are much more available.
Speaker 2 (14:29):
Is there potentially a kind of clash of cultures or
a clash of values as well when it comes to
building AI between the Wesk's approach and the Chinese approach.
Speaker 1 (14:38):
Very clearly, because if you look at early models of
deep Seek or even the you know, not tweaked or
fine tune models of deep Seek, they are very much
working within the boundaries of China's censorship rules. For instance,
you cannot ask questions about Taiwan or Sheishingping without it
(14:59):
giving a very bland official answer. Whereas if you take
that same model and you can train it with other
data and make it culturally suitable to different geographies. That's
one of the things that deep Seek learned early on
that by open sourcing the model and by giving developers
(15:23):
and users a chance to customize to their own cultural context,
deep Seak could find much more quicker and faster adoption
around the world than by controlling a lot of it
and controlling it in a way that it could only
give China friendly answers around the world.
Speaker 2 (15:45):
And while some applied China's innovations in AI, many of
the US suspect darker reasons for the success. An April
report from a US House of Representatives committee alleged significant
ties between Deep Seek and the Chinese got It concluded
that the company unlawfully stole data from Open Ai. The
(16:05):
Chinese embassy rejects those claims as groundless. Meanwhile, Deep Seek
and Liang haven't commented on the House report. Srita, it
seems like there is very much of an arms race
of sorts when it comes to AI, certainly between the
US and China.
Speaker 1 (16:20):
At the moment, it's very much a race, and I
think it would be too early to call a winner.
All I can say is that a year ago I
would not have called it as a close race. It's
a marathon, but you have to go at a sprinting pace.
We are really at the very beginning of it, and
(16:42):
for whichever country cracks the race, there is a lot
of economic gains to be had. So every country, particularly
the US and China, do not want to let up
in AI.
Speaker 2 (16:55):
And what are the challenges ahead right now for Deep
Seek that you see.
Speaker 1 (16:59):
I think one of the main challenges is what next,
What can they do that outdes what they've already done.
But there's also, I think for Deep Seak competition within
its own home ground, a bunch of China companies such
as Ali Baba and Paita and Tencent building models that
are outdoing Deep Seek's last flagship model. So there's this
(17:25):
pressure on Deep Seek to do better. But also I
think there is also the question around commercializing these models.
How are companies like deep Seak going to make money?
There is no clear answer yet whether Deep Seak wants
to make money and if it does, how will it
make money.
Speaker 2 (17:49):
This is the Big Take Asia from Bloomberg News. I'm
wan Ha. This episode was produced by Young Young and Naomi.
It was edited by Patty Hirsh and Joshua Brustin. It
was fact check by Bloomberg's editorial team and mixed and
sound designed by Taka Yasuzawa. Our senior producer is named Lushaven.
Our senior editor is Elizabeth Ponso. Our deputy executive producer
(18:10):
is Julia Weaver. Our executive producer is Nicole Beemster Bower.
Sage Bauman is Bloomberg's head of podcasts. If you liked
this episode, make sure to subscribe and review The Big
Tack Asia wherever you listen to podcasts. It really helps
people find the show. Thanks for listening, See you next time.
Bloomberg Audio Studios, podcasts, radio news.
Speaker 2 (00:08):
Earlier this year, a new product from the Chinese AI
startup Deep Seek, shocked the world and rattled Wall Street.
China's deep Seek is freaking out the AI world right now.
Techtoks tumbled as It's absurd to the top of the
download charts. But despite the global attention, very little is
known about the man behind deep Seek. Chinese entrepreneur Liang Wu.
Speaker 1 (00:33):
Liang Wenfeng is certainly a mystery figure.
Speaker 2 (00:36):
Bloomberg crit the rye covers artificial intelligence in Asia.
Speaker 1 (00:40):
He's certainly one of the most inaccessible and low key
tech entrepreneurs that I've come across. Just to illustrate how
private he is, we weren't able to find any pictures
of him on the internet when we scoured through his
website and all of that. But finally appear in a
really high profile meeting with President Sheijeng Peing, and that
(01:05):
picture got out into the world and he was everywhere.
Speaker 2 (01:09):
What does this man of mystery look like?
Speaker 1 (01:11):
He is slim, wears glasses, but doesn't talk much. Baby faced, Yes,
I think we could describe him like that.
Speaker 2 (01:25):
Deep Seek rarely answers questions about Leang citing his privacy.
But Sarita and her colleagues were curious about this man
whose AI systems turned the tech world on its head.
So they spoke with dozens of people familiar with his work,
from former employees and fellow researchers to investors and insiders
in the industry.
Speaker 1 (01:44):
And what we found is that, yes, he is extraordinarily
low key, very shy, but extraordinarily driven and talented and passionate.
And I think he has somewhat taken on Deep Seek
as a sort of a mission to establish China in AI,
trying to make sure that China is the force to
(02:07):
reckon with in AI.
Speaker 2 (02:12):
Welcome to the Big Take Asia from Bloomberg News. I'm wanha.
Every week we take you inside some of the world's
biggest and most powerful economies and the markets, tycoons and
businesses that drive this ever shifting region. Today, on the show,
who is LNG One Fun, we learn about the mysterious
tech founder who led Deep Seek to the frontline of
(02:34):
AI advances. Plus what is the company's rapid rise? Tell
us about the US China artificial intelligence race. Sriita, thanks
for joining us. I'm fascinated by AI. You guys did
such an interesting job with a story. I wonder if
we can start with who is Lang one fung? What
(02:54):
do we know about his roots?
Speaker 1 (02:56):
So? Liang is about forty years old in a small
village called Middling in the Guangdong province. His parents were
school teachers, mainly taught primary school kids. He was extremely
bright and went on to study at Xijiang University and
(03:18):
then also did his master's there.
Speaker 2 (03:21):
At Jujiung University. Liang and his friends immersed themselves in
all things tech, machine learning, signal processing, electronic engineering. They
even developed programs to trade stocks during the financial crisis.
After graduating, Liang joined forces with two of his classmates
and set up a quantitative hedge fund called High Fire Management.
Speaker 1 (03:44):
So quant funds basically work with mathematical models and statistical
analysis to do stock trading. Humans are not involved in
taking the decisions. At its peak, High Flyer Management was
managing something like fourteen billion in assets, so it was
quite a sizeable fund, and in its most successful runs,
(04:07):
it was providing annualized returns averaging thirty five percent to
its investors, so I would say that it was doing
very well.
Speaker 2 (04:15):
Indeed, according to former employees. High Flyer had a geeky
startup culture. Its early job postings moosted of attracting top
talent from Google and Facebook and said they were looking
for math and coding geeks with quirky brilliants.
Speaker 1 (04:32):
The early job postings also referred to Sheldon, who is
this very awkward main character in the prominent American sitcom
called The Big Bang Pory.
Speaker 2 (04:46):
For example, I cry because others are stupid and it
makes me sad.
Speaker 1 (04:51):
Sheldon has a legend of fans and it's extraordinarily funny
without meaning to be. So, you know, the whole culture
of deep Seek in the early days revolved around recreating
some of that geeky nerdy culture. They were free snacks,
poker game nights. Everybody was dressed in T shirts and slippers.
Speaker 2 (05:13):
Sounds like a great place to work.
Speaker 1 (05:14):
Yeah. It was really a very unorthodox startup culture, unlike
what you'll probably see in the big tech companies in
China such as the Aliba Bay and the Tencent.
Speaker 2 (05:25):
And how did Liang transition from doing quant financials into
AI and building deep seak.
Speaker 1 (05:30):
Liang was always extraordinarily interested in machine learning and artificial intelligence,
and then a few months after Open Ai you know
launched chat GPT, the chatbot that became an overnight global success.
It was then the spring of twenty twenty three, a
(05:51):
few months had passed after the launch of chat GPT,
and Liang then announced that deep Seek would be set up.
In its early manifesto, deep Seek talked about shunning mediocrity
and tackling the big challenges in AI, and of course,
(06:12):
ultimately cracking artificial general intelligence.
Speaker 2 (06:16):
The manifesto also laid out deep Seek's ambition to position
China as a leader in cutting edge technologies.
Speaker 1 (06:24):
You Know, Liang has given two interviews, rare as they
may be. In both those interviews, he's talked about bringing
China's AI ecosystem to the forefront of where the world is.
You know, China has been accused constantly of being a copycat.
He wanted in AI China to chart a different path.
Speaker 2 (06:44):
Deep Seek worked fast since twenty twenty three, It's released
over half a dozen AI models and helped pioneer a
technique called sparsity, which enabled those models to train and
run more efficiently. Developers started to take note then earlier
this year. Back to that top.
Speaker 1 (07:02):
Story, now deep Seek shaking up global tech lower technology
co host when they released their reasoning model R one
that cost such an upheaval in the industry and coused
a trillion dollar stock market meltdown. That's when the world
really started paying attention to this secretive AI entrepreneur in China.
Speaker 2 (07:29):
And Trita. What is show groundbreaking about Deep seeks are
one model?
Speaker 1 (07:34):
The AI industry until recently was always about billions of
dollars spent in building the infrastructure, the data centers, the
graphics processing units in the data centers that would train
these models. But what Deepsek did was show that its
models could match or even outdoor on some benchmark measures
(07:57):
what the latest open AI or anthropic models were doing,
and with far less computational power, with far less resources,
and as Deep Sea claimed, with far less capital as well.
Speaker 2 (08:14):
So how did Liang and his team manage to achieve
true innovation at what it says is a fraction of
the cost? And what does deep seek success say about
the AI race between China and the US. That's after
the break. For much of the last decade, the US
(08:41):
has tried to restrict China's access to semiconductors. Tensions reached
a fever pitch in twenty twenty two and the following
year when Washington targeted Beijing with two rounds of chip
export controls in Vidia and shares.
Speaker 1 (08:55):
Some semiconductor companies have slumped today after the Biden administration
said it would tighten restrictions on exports of AI chips
to China.
Speaker 2 (09:05):
That limited sales from American firms like Nvidia, whose cutting
edge chips are used by tech companies to help train
their AI models. The move presented a significant challenge for
developers in China, but as Bloomberg's read the rises, it
also forced them to develop workarounds.
Speaker 1 (09:23):
Necessity is always the mother of innovation. This has been
proven by AI developers in China. Never mind the export cubs.
They've still gone on to build good models that have
benchmarked with the best around the world.
Speaker 2 (09:40):
And one of the most innovative approaches from Deepseek is
a sparsity technique we mentioned earlier.
Speaker 1 (09:47):
Now. Sparsity is something to do with building a model
without having the high end computational power. It's when a
large language model doesn't have to be entirely harnessed to
give a nan to a query. Instead, Liang and his
fellow developers tried to apportion the expertise of the model
(10:10):
into smaller expert groups and then only harness those groups
that required to be used. So in doing that, they
made it much more computation efficient and also much more
cost efficient.
Speaker 2 (10:23):
Is it basically, instead of using your whole brain, are
you using just certain parts of your brain to do
that computation.
Speaker 1 (10:31):
That's exactly right on? You know, instead of entirely using
every little gray cell in your brain, it only fires
up those neurons or little portions in your brain that
contained that particular field of expertise and then bring that
to you know, respond to a query or give an
answer to a particular question, whether it's a command or
(10:53):
a coding question.
Speaker 2 (10:56):
The sparsity breaks through impressed Deep seeks competitors, but its
price point is what ultimately made headlines. Deep Seek said
it cost them just five point six million dollars to
train its V three model. That's far less than they
estimated one hundred million dollars Open Ai spent on its
most advanced version of chat GPT.
Speaker 1 (11:18):
Now, there is definitely a whole lot of skepticism around
that number because just the intrastructure, the training of the model,
the talent, and the time it takes all of it
adds to quite a sizable sum of money, so the
skepticism is warranted. People have estimated that there was no
(11:39):
way Deep Seak could have pulled that off without at
least a billion dollars or more.
Speaker 2 (11:44):
Also in Deep Seek's favor is that AI startups like
it have a staunch ally in China's government and present.
She Jinkin Sriita says she sees generative AI, robotics and
other high tech ambitions as beneficial to the state's agenda,
part of a larger push for self reliance in key technologies,
and Deep seax success has spurred much bigger rivals such
(12:07):
as Ali, Baba, ten Cent, and byte Edance to release
their own AI models. Srida deep six model is entirely
open sourced at this point. That means any individual or
company could incorporate deep Six's algorithms into their own programs.
Why did the company choose this approach and why is
that important open source?
Speaker 1 (12:29):
On one level, you could say that it is democratizing
AI and taking it out into the world, But let's
not forget that China's AI models would have otherwise found
fewer takers around the world if they were proprietary models
and were on par in terms of what they cost
(12:51):
with Western companies such as open ai. By making it
cheap and by making it open source, China allowed people
around the world to quickly take a look at the
models and begin using them, allowing them to be adopted
much faster in the business and AI ecosystem, thereby outdoing
(13:13):
the likes of OpenAI. Now that's huge. It's not only
about democratizing models, it's strategically about making sure that you
cut out your competitor by making things so cheap that
the world adopts it quickly and then it becomes mainstream.
Speaker 2 (13:33):
As a result, Microsoft and Amazon both offer deep seek
on their cloud services, and deep seeks models have been
incorporated into Perplexity, an AI powered search engine that also
offers models from open Ai and Anthropic.
Speaker 1 (13:47):
There is definitely a question about, you know, how fast
AI is advancing, and there's a fear around the world
about having all of the controls rest only with one
or two companies in the world. I think that was
what jep Seek and others were trying to put out
a message in the world, saying that all of the
controls cannot be left to one or two companies and
(14:09):
the proprietary models that they are building, it should be
much more democratic. Therefore, I think the open source philosophy
is about de risking concentration and allowing more people to
build with technologies that are much more available.
Speaker 2 (14:29):
Is there potentially a kind of clash of cultures or
a clash of values as well when it comes to
building AI between the Wesk's approach and the Chinese approach.
Speaker 1 (14:38):
Very clearly, because if you look at early models of
deep Seek or even the you know, not tweaked or
fine tune models of deep Seek, they are very much
working within the boundaries of China's censorship rules. For instance,
you cannot ask questions about Taiwan or Sheishingping without it
(14:59):
giving a very bland official answer. Whereas if you take
that same model and you can train it with other
data and make it culturally suitable to different geographies. That's
one of the things that deep Seek learned early on
that by open sourcing the model and by giving developers
(15:23):
and users a chance to customize to their own cultural context,
deep Seak could find much more quicker and faster adoption
around the world than by controlling a lot of it
and controlling it in a way that it could only
give China friendly answers around the world.
Speaker 2 (15:45):
And while some applied China's innovations in AI, many of
the US suspect darker reasons for the success. An April
report from a US House of Representatives committee alleged significant
ties between Deep Seek and the Chinese got It concluded
that the company unlawfully stole data from Open Ai. The
(16:05):
Chinese embassy rejects those claims as groundless. Meanwhile, Deep Seek
and Liang haven't commented on the House report. Srita, it
seems like there is very much of an arms race
of sorts when it comes to AI, certainly between the
US and China.
Speaker 1 (16:20):
At the moment, it's very much a race, and I
think it would be too early to call a winner.
All I can say is that a year ago I
would not have called it as a close race. It's
a marathon, but you have to go at a sprinting pace.
We are really at the very beginning of it, and
(16:42):
for whichever country cracks the race, there is a lot
of economic gains to be had. So every country, particularly
the US and China, do not want to let up
in AI.
Speaker 2 (16:55):
And what are the challenges ahead right now for Deep
Seek that you see.
Speaker 1 (16:59):
I think one of the main challenges is what next,
What can they do that outdes what they've already done.
But there's also, I think for Deep Seak competition within
its own home ground, a bunch of China companies such
as Ali Baba and Paita and Tencent building models that
are outdoing Deep Seek's last flagship model. So there's this
(17:25):
pressure on Deep Seek to do better. But also I
think there is also the question around commercializing these models.
How are companies like deep Seak going to make money?
There is no clear answer yet whether Deep Seak wants
to make money and if it does, how will it
make money.
Speaker 2 (17:49):
This is the Big Take Asia from Bloomberg News. I'm
wan Ha. This episode was produced by Young Young and Naomi.
It was edited by Patty Hirsh and Joshua Brustin. It
was fact check by Bloomberg's editorial team and mixed and
sound designed by Taka Yasuzawa. Our senior producer is named Lushaven.
Our senior editor is Elizabeth Ponso. Our deputy executive producer
(18:10):
is Julia Weaver. Our executive producer is Nicole Beemster Bower.
Sage Bauman is Bloomberg's head of podcasts. If you liked
this episode, make sure to subscribe and review The Big
Tack Asia wherever you listen to podcasts. It really helps
people find the show. Thanks for listening, See you next time.
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