In July 2024, there was a young Chinese entrepreneur—whom we'll get to know in a bit —who wasn’t even well-known within China itself. He was giving an interview to 36Kr, a media company specializing in financial and tech news. That same month, the American company OpenAI, one of the leading forces in machine learning and large language models, was experiencing some of its brightest days.
To put things in perspective, in July alone, approximately 1. 7 billion users visited its crown jewel ChatGPT (which we all know, of course). Take a moment to process that: 1.
7 billion visitors in just one month! Now, here’s the interesting part. At the height of OpenAI’s market valuation, this young Chinese entrepreneur made a rather unusual statement during his interview with 36Kr.
He said: “More investment does not necessarily lead to more innovation. . .
OpenAI is not a god, nor can it always stay ahead”. Fast forward to today—January 2025. Just last week, between the 20th and 24th, the World Economic Forum was held in Davos, Switzerland, as it is every year.
This time, the forum’s focus was on the importance of innovation and modern technology. According to Bloomberg, private discussions on the sidelines of the conference revealed that American tech giants had reached a consensus: the U. S.
and its allies must build more data centers and ensure that AI market regulations remain flexible, rather than rigid as they currently stand. Why? Because the U.
S. wants to stay ahead of its number one competitor: China. This sentiment was echoed in a statement by Ruth Porat, President, Chief Investment Officer, and Chief Financial Officer of Alphabet and Google.
She noted that while American companies might currently be more than a year ahead of their Chinese counterparts in AI models, it’s uncertain whether the U. S. will be able to maintain this lead.
However, despite her measured tone, Porat’s statement now seems overly optimistic. And like most investors, AI giants, and industry insiders, she was likely shocked by what happened in the very same days. So, what exactly happened?
On January 20, a Chinese AI startup called DeepSeek launched its latest open-source large language model, DeepSeek R1. This model has rivaled and even outperformed some of its American counterparts including ChatGPT. In just a few days, DeepSeek R1 dominated the global tech scene, catching U.
S. investors off guard with its astonishingly low cost and a model size less than one-tenth of OpenAI’s systems. This unexpected breakthrough triggered a massive market sell-off.
By Monday, American and European tech stocks collectively lost $1 trillion in value. Among the hardest hit was NVIDIA, which saw $589 billion wiped out —marking the largest single-day loss for any company in the history of the U. S.
stock market. What happened, in short? As the Wall Street Journal put it: a bloodbath.
Remember that young entrepreneur we mentioned at the start? Well, he’s the founder of DeepSeek! So now, the big questions are: - Who is this young man?
- What’s his story? - What exactly is DeepSeek? - Why are American tech giants so alarmed by it?
- And why is the U. S. so worried about China’s rapid advances in AI?
That’s part of what we’ll be discussing today in this highly anticipated episode, which many of you have been requesting over the past few days—so make sure you watch until the end. I’m Ashraf Ibrahim, and this is The Economic Informant. On January 20, at the very same moment that DeepSeek was launching its new R1 model, Donald Trump was being inaugurated as the new President of the United States.
Meanwhile, on the other side of the world, an unknown Chinese entrepreneur was sitting across from China’s second most powerful man, Premier Li Qiang, in a high-level business meeting with China’s top executives. This 40-something-year-old entrepreneur, whom you can see here speaking while the Premier listens intently, was the only AI industry representative invited to the meeting. Liang Wenfeng—the Chinese mathematics prodigy who has shaken the world in recent days through DeepSeek.
But before we discuss how his company stunned the Americans, we need to understand who he is and how he got here. Born in 1985 in Zhanjiang, a poor city in Guangdong province, Liang was the son of an elementary school teacher. This background shaped him into a top-performing student—so much so that he began studying calculus on his own in middle school.
Excelling through his education, he eventually earned a place at the prestigious Zhejiang University. As a student, Liang began developing AI algorithms capable of identifying stock patterns based on complex criteria. A math fanatic, he was convinced—years ahead of his time—that analyzing vast amounts of market data could uncover hidden trading patterns, paving the way for huge financial gains.
That’s why, he and two former university classmates decided to enter the Chinese stock market in 2008, in the midst of the global financial crisis, despite having zero prior trading experience. In 2013, a few years after graduating, Liang founded his first venture, an investment firm called Jacobi named after the German mathematician Carl Gustav Jacob Jacobi. Yet even as he built Jacobi, he continued his work with his two old friends, staying on his chosen path.
The trio spent years experimenting with different trading strategies before finally launching High-Flyer in 2015 —a Chinese quantitative trading fund built around a proprietary computing model that analyzed market data based on price and trading volume factors. Just one year later, they integrated machine learning into the model. This development allowed the system to delve deeper into financial and investment data, enabling it to identify optimal investment paths.
As a result, High-Flyer’s assets skyrocketed, reaching over 90 billion yuan ($12. 4 billion) by 2021. However, in the following years, the fund faced setbacks, losing about a third of its asset value, and today it manages approximately $8 billion.
High-Flyer’s rapid growth inspired Liang to embark on a side project one he considered the true future of his company and career. Pay close attention to what comes next. In 2019, Liang began purchasing thousands of Nvidia GPUs, leaving many wondering why he was doing it.
Even some investors interested in partnering with him at the time found it bizarre. One investor recounted to The Financial Times that when he first met Liang, alongside a group of other investors, he found himself face-to-face with a peculiar man sporting an even more peculiar hairstyle. But instead of discussing investment strategies and High-Flyer’s performance, Liang spoke at length about building a computing cluster with 10,000 chips to train AI models.
Although many didn’t understand what he was doing, by late 2022, when OpenAI launched ChatGPT, only a handful of Chinese companies had more than 10,000 advanced Nvidia chips —and High-Flyer was one of them. Just as Liang had planned. Liang’s relentless pursuit of Nvidia’s chips became even more evident when he explained his reasoning in an interview with 36Kr.
When asked why he was buying so many GPUs, he likened it to buying a piano, saying: 1. I can afford it. 2.
I have a group of people who desperately want to play it. As Liang stockpiled Nvidia’s chips, the Biden administration imposed tighter restrictions on selling high-end computing chips to China, particularly those used for training AI models. Under these restrictions, U.
S. companies wanting to sell chips to Chinese firms had to significantly reduce something called interconnect bandwidth— a term that refers to the speed at which data is transferred between chips. To comply with U.
S. export regulations Nvidia—the global leader in AI chip design— developed a China-specific AI chip in the same year: the H800. Although this chip technically adhered to Biden’s restrictions by reducing interconnect bandwidth, Nvidia found other ways to maintain high performance, ensuring that Chinese AI firms could still build powerful models.
Why Is This So Important? This created a months-long window of opportunity for Liang to purchase large quantities of H800 chips before the Biden administration caught on to what Nvidia had done and tightened regulations further—part of the broader U. S.
-China tech rivalry. Nvidia’s workaround became one of the key foundations of DeepSeek, the AI company that the obsessive Chinese billionaire Liang launched in 2023 in Hangzhou—the same tech hub that houses Alibaba. From the outset, Liang had a clear vision: to rely solely on Chinese talent.
In his rare media appearances, he described DeepSeek as a “one-of-a-kind local company, where we develop Chinese talent ourselves. ” Every employee and researcher at DeepSeek is Chinese, with PhD graduates from top institutions such as Peking University, Tsinghua University, and Beihang University instead of recruiting American experts. More importantly, most of them are fresh graduates or have just one or two years of experience.
These two aspects—Chinese talent and limited experience are extremely significant and demonstrate Liang’s sharp business acumen. Last year, MacroPolo, a U. S.
-based research institute affiliated with the Paulson Institute, published a study revealing that China had surpassed the U. S. for the first time as the world’s largest producer of AI talent.
Today, China accounts for nearly 50% of the world’s top AI researchers and engineers. This imbalance in tech talent between China and the U. S.
has been shifting for over a decade. A decade ago, the advantage was firmly in America's hands. Elite U.
S. universities attracted a significant number of China’s brightest minds, and many of these students remained in the U. S.
after graduation, contributing to the country’s technological and economic strength. In recent years, however, this trend has changed. Since 2018, China has heavily invested in AI education, adding over 2,000 AI-related university programs, including more than 300 at its top universities.
While many of these programs focus on AI applications in manufacturing, rather than generative AI and its tools, the shift remains highly significant. Liang recognized this transformation early on and began investing aggressively in these emerging talents from the moment DeepSeek was founded in 2023. So much so that DeepSeek has now become the highest-paying AI employer in China, rivaled only by ByteDance, the parent company of TikTok.
Beyond his focus on local talent, Liang had no issue with hiring inexperienced employees. When asked about this, he shared a statement worth learning from. Experience can sometimes be a problem or an obstacle.
If you hire experts, they will confidently tell you what they know in every situation, often without hesitation. They’ll insist that things must be done in a specific way, following a fixed method. ” In contrast, he explained, inexperienced individuals are constantly forced to explore, think critically, and develop practical solutions to challenges.
From the company’s inception—and at least until now—Liang has remained committed to his original vision: DeepSeek aims to break the monopoly of major tech firms, which is why he decided from the start to keep the company’s AI models open-source. And indeed, this has been DeepSeek’s approach since its first model launched in 2023 right up until the storm it stirred in December and beyond. So, what happened?
In late December, DeepSeek released its V3 model, boasting 671 billion parameters. (A parameter is a fixed value in an equation, and the more parameters an AI model has, the better it can recognize and process complex patterns. ) According to DeepSeek, V3 was trained in just two months using 2,048 computing chips, at a total cost of $5.
6 million. The model can perform many of the core functions of GPT-4. For perspective: - GPT-4 has 1.
8 trillion parameters—three times larger than DeepSeek’s V3. - However, OpenAI trained GPT-4 using over 16,000 chips, at a staggering cost of $100 million. - In contrast, DeepSeek’s AI was trained with far fewer resources, at a fraction of the cost.
Then, on January 20, DeepSeek launched R1, a logical reasoning model that the company claimed rivals OpenAI’s latest and most advanced model, Q (O1), which currently costs $200 per month. DeepSeek also asserted that R1 outperforms ChatGPT’s existing models—and it’s completely free. Exactly one week later, on Monday, DeepSeek unveiled Janus Pro—a compact AI model that can run on any laptop and generate images.
The company even claimed that Janus Pro outperforms OpenAI’s DALL·E 3. The combination of these powerful, open-source, and free AI models —along with DeepSeek’s unconventional approach—stunned the industry. Why?
Because for years, the AI industry operated under one fundamental belief: Developing AI requires massive amounts of advanced, incredibly expensive computing chips. This is precisely why no one expected serious competition from China, especially since Chinese companies have struggled to purchase high-end chips due to U. S.
export restrictions. But DeepSeek took an unexpectedly brilliant approach. Linart Heim, an AI researcher at the RAND Corporation, explained AI models using an analogy: Imagine ChatGPT as a librarian who has read every book in the library.
When you ask it a question, it gives you an answer based on the knowledge it has gathered from all those books. The problem with this approach? It’s long, expensive, and resource-intensive—requiring massive computing power, enormous amounts of data, and a huge electricity bill to train an AI model at that scale.
DeepSeek, however, said: Stop. We’re not taking the hard road. They neither had the resources nor the computing power for a traditional AI training approach—so they played it smart.
What did they do? Lennart explained that instead of making the AI model—akin to a librarian—read every single book, DeepSeek made it a highly skilled librarian capable of efficiently searching for the most relevant book to answer a given question. In other words, rather than reading all the books, it only reads the one that contains the key information.
Alongside this smart approach, DeepSeek also employed another technique as part of its solution: the Mixture of Experts model. What does that mean? Instead of having a single librarian capable of searching across every topic, they created a team of specialized librarians.
One librarian is dedicated to cooking, another to physics, another to specific types of literature, and so on. This method reduced the strain on computing chips, as they no longer had to process everything simultaneously, preventing system overload. By applying these strategies with remarkable intelligence, DeepSeek managed to train its AI models at just a fraction of the cost of its American competitors.
The shockwave DeepSeek sent through the U. S. AI industry has fueled a lot of speculation over the past few days without any concrete evidence to back it up.
Some American experts have claimed that this was a message from the Chinese government to the Trump administration, proving that U. S. technological sanctions against China are ineffective.
Others have taken the theory even further, suggesting that DeepSeek is part of a Chinese conspiracy to dismantle the American tech industry. In reality, things are often much simpler than conspiracy theories suggest. And, of course, DeepSeek’s models are not without their flaws.
After returning from their Chinese New Year break, Liang and his team announced that they would immediately begin work on the next version of DeepSeek. Meanwhile, one key question is now being asked by some of the top AI experts: Can DeepSeek sustain its competitive edge as the industry advances and spending increases? This question is crucial because we shouldn’t jump to the conclusion that major U.
S. tech companies are simply wasting money or that DeepSeek is on the verge of overtaking them. So why might that be an exaggerated scenario?
Because operating advanced AI models will continue to be extremely expensive even after training. Furthermore, major companies like Google and OpenAI will keep investing heavily in AI development to maintain their dominance in the market. The key takeaway here is that DeepSeek has exposed the flaws in the “bigger is better” doctrine.
It has demonstrated, in practice, that smaller AI models—when trained correctly and with innovative, smart techniques like the ones the Chinese team employed—can rival or even surpass the performance of much larger models. This means that AI companies can achieve significant advancements with much lower investments than what was previously common in the industry. As a result, investors may be encouraged to fund emerging AI startups, fostering greater competition among companies rather than allowing Silicon Valley giants to dominate cutting-edge technologies simply because they can afford massive spending.
I won’t keep you much longer—AI is a vast topic that would take multiple episodes, not just one. In fact, we might do another episode soon on the intense competition between China and the U. S.
for AI dominance, as both see it as the key to shaping the future of global technology. If you’d like to see that episode, let us know in the comments! And before we wrap up, I have a question for you: After seeing how a group of young developers in China, with minimal resources, managed to outperform American companies backed by the world’s largest financial and technological powerhouses do you think there's hope for Arab companies to rise and compete globally?
Or is that simply too difficult for us as Arabs? And why? I’ll be reading all your answers in the comments!
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