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