Why Silicon Valley Isn’t Scared of DeepSeek Anymore | Digging into China

Hi, I'm Dong Xiong. Welcome to Digging into China, where we take a deep dive into what's really happening in China and share some unique insights. Today, let's take a closer look at the truth behind the U.S.-China technological competition, particularly in the field of artificial intelligence. Because artificial intelligence is a brand new technological domain, neither China nor the United States has much of a deep foundation to draw upon. Or to put it another way, the starting lines in this field don't have the kind of big gaps you see in other areas. Today, we are going to present and restore the brutal competition between China and the United States in artificial intelligence from five different angles. From these five aspects, you will see many very interesting phenomena. For example, China's AI sector didn't start late at all. There was also a time when China came very close to overtaking on the curve. Chinese new AI model products once scared Wall Street so badly they nearly wet themselves. It was even called a Sputnik moment, an epoch-making moment. Of course, I want to emphasize that it scared Wall Street, but it didn't immediately scare Silicon Valley. You will also see how, after nearly overtaking on the curve, China then took a magnificent detour, hesitating not moving forward, and then taking a very wide turn. This scene is pretty much a microcosm of U.S.-China competition across various fields, and it also holds significant lessons for the future competition between the two countries. First, let's understand Deep Seek, which Chinese public opinion regards as a proud symbol of national pride and even a great power instrument, and how it went from that Sputnik moment that shook the world to today, when it's been proven that there is a generational gap, a lag behind. We can understand the starting hand of Chinese artificial intelligence and the impact Deep Seek has brought to the world from four different angles and different standpoints. First, let's look at the report from China Central Television about Deep Seek and the undefined Chinese large model. This article is a comprehensive, overview-style verdict report that CCTV did after Deep Seek released its V4 model. The article opens with an ancient Chinese saying. I believe Chinese people will find it impressively profound. Do not worry about the price. Do not fear slander. Follow the true path and uphold integrity with dignity. These characters are the words Deep Seek attached when releasing V4. CCTV used this quote to set the tone for the entire piece. As CCTV puts it, steadiness is a kind of bearing, not backwardness. It has been five months since the last update to V3.2. During this period, America's anthropic rolled out new models that demonstrated unprecedented capabilities in network attacks. And a chat GPT also made netizens exclaim that the era of picks-or-it-didn't-happen has passed. In an era where mainstream large models update on average every 91 days, Deep Seek's silence was seen by many as equivalent to falling behind, even dropping out of the race. CCTV's first point is that our new model was released slower than others, and while others think we are behind, we actually are not. That's their first viewpoint. Second, CCTV believes this slower updating compared to others, this so-called silence, makes them victims of being defined by others. Because Deep Seek didn't provide external explanations, this lag behind others' updates was defined by foreign media as backwardness. Therefore, it is a victim of such definitions. We are not behind. CCTV's third viewpoint puts forward the of the article. We are behind because Deep Seek has been quietly doing something major, embracing domestic chips. After Deep Seek's V4 model was adopted to Huawei's Ascend chips, it achieved high-throughput, low-latency inference deployment. This means a domestically coordinated AI research ecosystem of software and hardware is gradually being perfected. Although China's current process technology is not dominant in a single core, the performance is limited. Through advantages in system design, cluster architecture, software coordination, and power consumption, it can explore new development solutions under existing conditions. So the reason we released it a bit later is to promote domestic chips. And why promote domestic chips? CCTV gives an even greater reason. Since 2015, the National Development and Reform Commission has rarely mentioned domestic computing power and has rarely explained specific development situations. The shift from few responses to positive responses is continuing to occur. In other words, we are actually playing a big game of chess. Our National Development and Reform Commission has been laying out domestic computing power since 2015, but we just didn't talk about it externally. Now that we have mostly got it done, we are starting to reveal it. At the end of the article, CCTV explains what not being defined means. When you say we are behind or slow, it's because you don't understand China. This big game of chess we've been playing since 2015 can now be made public. Our software-hardware coordination capabilities are all in place, just wait and see our good show. Therefore, the conclusion continues using this impressively profound high-level expression. When we discuss Chinese AI enterprises, what we are really discussing is not just the enterprises themselves, but an entire industry, and the development model it represents behind them. Wow, behind artificial intelligence, there is also a whole development model. See if that scares you or not. Now I have three viewpoints to share with you about this CCTV article. First, the intent of this narrative framework is very obvious. It believes that this model's slower update speed compared to others is not backwardness. It's us doing big things and holding back a big move. It even suggests that being slow is our advantage, unlike your superficial excitement. This is a bit forced. Second, its emphasis on shifting to domestic chips does have some substantive content, because the United States will impose increasingly strict restrictions on exporting advanced chips to China. So, the determination to switch to Huawei chips is understandable and may be an inevitable path. Third, this is a kind of inexplicable joy that CCTV is venting. The West doesn't know what we are doing. While we are being slow, they got fooled. This kind of joy is a bit strange. I don't know whether Chinese netizens watching this kind of propaganda from CCTV can actually feel joy from it. Even if you are telling a story with a comedic, happy ending, you still need the process to show it. How we fell behind others. How the West became anxious and only then you feel happy. This still feels somewhat stiff, but that's how CCTV presented it. Look, the West has been waiting for us and couldn't get anything, waiting so anxiously. This is also our win. So I think the valuable information about the DeepSeek V4 is what CCTV mentioned about adopting Huawei chips. Of course, whether it's true or not is hard to say, because when DeepSeek first caused a global sensation, it claimed it didn't use Nvidia chips, but it was later confirmed that it used a large number of Nvidia chips. Second, let's share Reuters' report on how they introduced DeepSeek's new model release. Reuters' headline was, DeepSeek releases new artificial intelligence model. Market reaction lukewarm. The market's current reaction to DeepSeek releasing a preview of its next-generation AI model is quite lukewarm, forming a sharp contrast with the breakthrough performance last year when this Chinese innovation company shocked the world with its low-cost models. V3 and R1 triggered enormous repercussions overseas and even triggered a wave of selling in global tech stocks. That's what I mentioned earlier about scaring Wall Street early last year. Investors began questioning whether the logic of massively burning money on AI infrastructure was correct. Analysts generally regarded that moment as a black swan. However, this time, the reaction to DeepSeek V4 since its release has been lukewarm, highlighting how quickly those assumptions have changed over the past year. Why no surprise this time? The market and various industries have already become accustomed to developing low-cost, high-efficiency models under compute constraints. So the surprise element has been greatly reduced. This release followed a quite predictable path, and benchmark data also confirms this assessment. According to professional AI analysis data, the V4 model shows significant improvement over earlier versions, but in overall ranking, it is merely among the leading open-source models rather than clearly surpassing competitors. Where is the real significance? Huawei chips and geopolitics. The move to run the V4 model on Huawei chips aligns with the tightening of U.S. export controls. The real question now is whether China can continue advancing artificial intelligence development and achieve it with domestically produced chips. Third, let's share the comments from the U.S. National Institute of Standards and Technology. Their conclusion is that China's current strongest artificial intelligence models lag behind America's most frontier ones by 8 months. Their report claims that DeepSeek's own official tests suggest they are comparable to GPT-5.4, which was just released 2 months ago. But NIST's tests found that DeepSeek V4's actual level is roughly equivalent to GPT-5 released 8 months ago, not as advanced as they claimed. The so-called 8-month generational gap mainly comes from conclusions drawn using NIST's private test question bank. Why did Chinese tests show it is comparable to GPT-5.4? NIST wasn't very polite about it. They said DeepSeek used a lot of public leader board exam questions for training, basically cramming like Chinese high school students constantly practicing problems to push up the score. So when you switch to a new set of questions, it doesn't reach the claimed level. This is a very interesting phenomenon. NIST also pointed out that Chinese models generally use this method, training on some public test questions to answer quickly and accurately. At the same time, they noted that the current model iteration speed is 1 generation every 3 months. No matter how you explain it, updating only once every 8 months clearly can't keep up with this pace. Yet Chinese CCTV says our silence is about preparing a big move. Fourth, let's share economists' comments. It commented on DeepSeek and China's latest model from yet another angle. The title is something like DeepSeek's performance failed to impress. AI labs face increasingly fierce competition and government intervention. This article mainly makes three points. First, efficiency and technological convergence. DeepSeek's R1 success lay in extremely high cost efficiency, achieving inference capabilities comparable to open AI with only a few million dollars. But by 2026, reasoning models have become the industry standard, and DeepSeek still has a clear gap with top American models in automating complex tasks. Doing more with less money is impressive. But when compared on professional performance, there are still obvious gaps. Second, its view on computer bottlenecks and export controls. Although DeepSeek is good at squeezing chip performance through algorithms, the disadvantage of large-parameter models like V4 lacking the most cutting-edge chips is gradually becoming apparent. It also has a premise that America has advanced chips while we are good at advanced algorithms, implying Americans aren't as good at utilizing advanced algorithms. But what if Americans have both advanced chips and advanced algorithms? How would you respond? Actually, there is still logical significance in Chinese propaganda here. At the same time, compute costs are no longer mythical. When model scale expands to a certain level, the dividends from algorithm optimization will be offset by massive compute demands. The third viewpoint is about government intervention and compliance costs. As a Chinese lab, DeepSeek must invest heavily in model safety requirements and ideological alignment. According to NIST testing, DeepSeek's models often show avoidance and dogmatic tendencies when dealing with politically sensitive issues, which affects its universality in the global market and user experience. This kind of government regulation still brings limitations to the development of such artificial intelligence. The second major aspect, let's understand what America's AI leading figures think about the current development of Chinese AI technology. This is also very interesting. From the perspective of knowing yourself and knowing the enemy, let's see how American AI leaders understand and view Chinese AI technology. First, let's introduce the founder of Anthropic, the company behind the cloud model that everyone often uses, Dario Amodei. In his book published this year, Technological Adolescence, he offers comments and analysis on Chinese AI. He knows China very well because he once worked for a Chinese company. We'll introduce that later. First, he believes Dario specifically names China as second only to the United States in AI capabilities and the country most likely to surpass the US. Combining an authoritarian government with existing high-tech surveillance infrastructure, the CCP has the clearest path to realizing the nightmare of AI empowered totalitarianism, bar none. This may even be the default outcome for China itself and other authoritarian countries that accept China's exported surveillance technology. He particularly emphasizes that he is not naming China out of hostility toward China. It's because China's current governance model is the most complete combination of the three elements, AI capabilities, authoritarian government, and high-tech surveillance state. His second view is the existing abuse of AI for surveillance and propaganda. The CCP has already begun using AI for monitoring, such as the repression of Uyghurs in Xinjiang and implementing algorithm propaganda through TikTok. He further warns that once powerful AI technology is in place, a personalized AI agent that deeply understands you for many years and uses that understanding to shape all your views will be far more powerful than the CCP's propaganda on children through TikTok. From this angle, the CCP's perverted propaganda activism will soon be realized. Third, his view on the authoritarian toolkit and the powerful AI. He judges that the CCP will use AI to strengthen its centralization in at least four areas, full-spectrum surveillance, AI propaganda, autonomous weapons, and strategic decision-making. His fourth view is that the Chinese people are the most likely victims. Dario clearly distinguishes the CCP regime from the Chinese people. If we are talking about the victims, the ones most likely to suffer from Chinese AI repression are precisely the Chinese people themselves, who have no say in government actions. He expresses admiration for the Chinese people and supports the struggle for freedom by many brave dissidents inside China. Fifth, his policy recommendations to the U.S. government include tightening chip export controls. He explicitly opposes selling advanced chips to China, saying it makes no sense to sell CCP tools to build an artificial totalitarian state that could potentially conquer us militarily. It's like selling nuclear weapons to North Korea and then boasting that the missile casing was made by Boeing. So America wins. This is mainly aimed at NVIDIA and Jensen Huang's views. Sixth, he talks about other authoritarian states also posing stimulating threats. It's not just the CCP using AI to threaten the Chinese people and the world. Other countries are worth watching too. He named North Korea. Now the second thing, why does Dario understand China and the CCP so well? This is related to his personal experience. He once worked for China's Baidu. Recently, Anthropik, where Claude is based, is preparing to go public. And in primary capital markets, the pursuit it has received has already surpassed open AI. Or you could also understand it this way. In the professional AI field, he is generally regarded as America's number one, the leader in American AI. So how did this founder, as a top American AI expert and even a leading figure, go work at Baidu for a period? This is exactly what we mentioned before the program about how Chinese AI did indeed have a period of overtaking on the curve by taking shortcuts, coming close to reaching or even surpassing American levels. That's the period being referred to. During Dario's time working at Baidu and the achievements created by him and his colleagues, as early as 2013, Baidu's Robin Li, many people feel he sometimes comes and goes with the spring winds, but he still has some strategic vision, invested $100 million personally to top American AI experts Andrew Ng, a Chinese-American who researched AI technology. They established Baidu's AI team in Silicon Valley, called the Baidu Silicon Valley AI Lab. After Andrew Ng received this investment, the first person he thought of was Dario, and he recruited Dario. Dario studied biology but is a computer genius. The code he wrote at Stanford became textbook-level examples. What did Dario do at Baidu? He helped Baidu develop an advanced speech recognition system. This technology was later named by MIT as one of the top 10 technological breakthroughs in the world in 2016. They improved the efficiency and accuracy of English and Chinese speech recognition by optimizing neural network architectures. During his time at Baidu, Dario realized a path for the development of AI technology. While doing the work at hand, he was thinking about the future development of this emerging industry. This is what later formed his famous scaling law. He discovered while working at Baidu with a group of top AI scientists that as compute power and data increases, AI performance shows a very smooth improvement curve. So how to change the progress of this AI technology? It was through his famous scaling law. Increase compute, data, and model scale to bring predictable performance improvements. Don't let the curve be too flat. Today's development of American AI technology is based on this law. Why did Dario leave Baidu? This is very interesting Chinese corporate culture. After Baidu's Silicon Valley AI lab achieved remarkable results, internal territorial walls broke out. Whose credit is this? Whose power is that? In that situation, Dario couldn't so he directly jumped from Baidu's Silicon Valley lab to Google, then from Google to OpenAI, and in 2021 founded Anthropic himself. Therefore, Dario has a profound understanding of the development of Chinese AI because he was a direct participant, and he also deeply knows why Baidu's AI technology development later stagnated and fell behind, precisely because of this corporate culture constraint. Third, let's understand Andrew Ng we just mentioned. Andrew Ng remains to this day a first-class AI expert recognized in the United States. He officially joined Baidu in 2014 as the chief scientist. He built Baidu's AI team from nothing and eventually expanded to more than 1,300 people. In the early days of AI companies, this scale was leading in the world. Dario left Baidu in 2015 and Andrew Ng announced his resignation in March 2017. After Ng announced his resignation, Baidu's stock fell 3% within hours, which shows Ng's influence on Baidu's AI. Did Andrew Ng or Dario really go to China to work for Baidu? No, they never lived in China. They all worked remotely for Baidu. Baidu's AI lab was set up in Silicon Valley. The reasons Ng left and Dario left are exactly the same. They couldn't stand the internal fighting at Baidu over fame, profit, and credit after achievements. You might think, as the boss, shouldn't you stop this? What's there to fight about? All the credit belongs to Baidu company. Just call a stop and it's done, right? Actually, a company's culture is an instinctive force that's very hard to correct just by one boss. So what is Andrew Ng's attitude today towards U.S.-China AI competition? It is completely opposite to Dario's. He believes we should cooperate with China and not restrict China. This is Ng's current attitude. The third viewpoint to share with you. From Baidu establishing an AI lab in Silicon Valley and its AI team starting early with a high starting point, can we say that China's AI start was no later than America's? Bloomberg has a report on this that confirms this view. China's start in AI technology development was not later than America's or roughly the same. It lists a timeline. In 2012, Geoffrey Hinton's team in the U.S. dramatically surpassed competitors in competitions with deep learning, igniting the starting point of the modern AI revolution. That same year, Google started its AI project, marking the first wave of strategic layout by American tech giants. The leader of Google's AI project in 2012 was Andrew Ng. By 2013, Baidu began investing, and in 2013, it recruited Andrew Ng. So in terms of timing and even bringing people into technology, Baidu's starting point was roughly the same as when these big American companies got involved. And the starting point was also very high. In 2014, Baidu's investment in Silicon Valley increased from $100 million to $300 million. At the time Baidu's AI lab was established, OpenAI had not yet been founded. OpenAI was founded in 2015, and Anthropic appeared even later in 2021. So China's AI technology started relatively early and with a very high starting point. However, after achieving brilliant results in 2016, it messed itself up. So when Chinese people write the history of artificial intelligence, this period of history should not be erased. China is not like in other fields where it lagged far behind and is now catching up. In the AI field, China started relatively early and the start was also relatively smooth. But in Bloomberg's analysis of China's AI development process, they found a problem. The start wasn't late and the starting point wasn't low. But there was a key structural problem behind it that no official Chinese institution is willing to acknowledge. The research done by Baidu's Silicon Valley Institute was still a rapid following, learning the technology from America's Hinton team. They were not wave makers. This is very similar to DeepSea's current state of closely following the frontier. Moreover, in Baidu's later AI development, the teams were almost entirely Chinese or ethnic Chinese, which is completely different from Silicon Valley's lab strategy of attracting the most top talent. There is now a view that most AI scientists worldwide are Chinese, but that's actually not the case. There are people of all races and skin colors. Without an open approach that truly integrates into the broadest global top talent pool, it is very difficult to compete in such cutting-edge fields. Bloomberg also did a deep analysis of why Chinese AI technology later fell behind. They started developing together with high starting points. So why did they fall behind? First, differences in the depth of the talent ecosystem. It ultimately focused on Chinese people or domestically trained people. Second, chip constraints. After 2019, the United States restricted exports of advanced chips. Third, asymmetric academic openness. Almost all American AI breakthroughs are published openly, so Chinese researchers can learn them synchronously. But China's core technologies are often not fully disclosed for commercial or security reasons, thinking they are smarter than others, which actually affected overall development. Fourth, different path choices between application and basic research. In Chinese AI, they are willing to invest in AI products that can bring returns, especially AI technologies that can support commercial applications like search, advertising, recommendation, algorithms, payments, and risk control. China's Alibaba, Baidu, and Tencent are all willing to invest in those. But unlike deep mind and open AI, which deeply cultivate the foundational model architectures, Chinese companies were unwilling to invest until around 2022 when they began catching up in this area. Look, they were nearly overtaking on the curve at the most advanced level, but then they took another magnificent detour and now have become laggards again who need to catch up desperately. So this kind of lag from the perspective of AI competition is a lag manufactured by the Chinese themselves. They were originally walking together, but then took another detour on purpose and now have to chase again. Five, let's understand exactly how big the gap is in US-China AI field. Recently, many people have been making such comparisons. I chose one that is relatively acceptable from all sides, Stanford University's AI Index report. It provides a Stanford-version commentary on the gap between China and the US in AI field. First, on model capabilities, the gap is 2.7%, but America still holds the top end. According to Stanford University's evaluation in March 2026, Anthropx Cloud 4.6 leads the world with 1,503 points, followed closely by Bytedance in Beijing with 1,464 points. The gap is only 39 points, so they consider the gap to be 2.7%. Stanford's report also reaches a conclusion that America's private investment in AI is 23 times that of China's. Yet, it only maintains a 3-percentage point performance advantage. They consider this very fair. The second comment from Stanford's report is on capital investment. America crushes, but China has hidden funds. In 2025, US private AI investment reached $286 billion, while China had only $12.4 billion, a difference of about 23 times. But the Chinese government, through guidance funds, has deployed $184 billion to AI enterprises since 2000. These funds do not enter private investment statistics. Chinese CCTV boasted that only in 2015 did the National Development and Reform Commission start playing a big game of chess and secretly investing in AI. But the Stanford University's report says the CCP started laying out AI investments back in 2000. The report's third area of analysis is that chips and compute are America's strongest moat. The US and its partners advanced AI processing chips manufacturing capabilities after quality adjustment are 35-38 times China's. This is currently the most durable structural advantages. Fourth, on research papers and patents, China is leading comprehensively. China holds 74% of global AI patents and accounts for 18% of global AI papers. However, quantity does not equal quality. American patents' forward citations exceed half of those cited, reflecting that influence still centers on the US. It is worth noting that in 2025, China's number of research papers in AI surpassed US for the first time, showing a structural shift in research focus towards explainability and governance. Its fifth comment is on the open-source ecosystem. China is competing for global infrastructure status. Alibaba's models had accumulated 942 million downloads by March 2026, exceeding total of the next eight competitors combined, becoming the default open-source base model for developers in Asia and the Global South. ByteDance and Tencent's open-source releases increased 89 times in 2025. Baidu jumped from zero releases in 2024 to more than 100 in 2025. Sixth, talent flow is America's soft underbelly. America still has an advantage in attracting AI researchers. But the number of AI researchers immigrating to the US has declined 89% since 2017. If researchers building frontier models increasingly choose not to go to the US, massive investment can only exchange for hardware and infrastructure rather than the capital that turns compute into capability. Seventh is robotics and industrialization, China's other front. In 2024, China accounted for 54% of global robot installations. The humanoid robot field is scaling at consumer electronics speed. This means China's AI advantage is not only at the software level but is extending to automation and the physical world. So the final conclusion from the report is that in the AI field, America has advantages in three areas, frontier model capabilities, private investment, and chip manufacturing, while China leads in the other four areas, patent quantity, research paper quantity, open-source ecosystem, and robot installations. This is Stanford's report. Viewers can also judge the state of U.S.-China AI competition based on the comparison targets listed by Stanford. Finally, let's understand anthropic founder Dario's comparison of the current state of U.S.-China AI technology. Earlier, we shared how in his newly published book, Technological Adolescence, he introduced and critiqued Chinese AI and the CCP's AI policies. So how does he evaluate today's U.S.-China AI competition? Because he is currently a leading figure in American AI and was once a leading figure in Chinese AI, he has a deeper understanding of the development of AI technology in both countries than others. Earlier this year in Davos, he made a very harsh comment that shocked international public opinion. While most public opinion is watching the U.S.-China AI competition, Dario talked about a different logic. He said that to build AI that is smarter than humans in almost everything requires millions of chips that at least tens of billions of dollars. This is most likely to be achieved in 2026 to 2027. The release of China's DeepSeek did not change this calculation because its cost reduction is still within the originally expected curve. This means that in 2026 to 2027, we may face two completely different worlds. In the United States, multiple companies will definitely be able to obtain the required millions of chips. The key question is whether China can also obtain millions of chips. If it can, we will live in a bipolar world. This is the essential difference of zero and one, not who leads by 3% or 17%. He is also mocking Stanford's report here. It's about whether China can cross the threshold of millions of chips to enter the level where powerful AI exists. Crossing it means a bipolar world. Not crossing it means U.S. unipolar dominance. This is a binary judgment, not a spectrum scale issue. You all talk about who leads by how much. But if you don't even have this infrastructure, what are you competing for? But if China obtains so many advanced chips, then we can talk about bipolar competition. Dario told Bloomberg's Editor-in-Chief that the U.S. leads China by many years in chip manufacturing capability, and he thinks exporting these chips is a major mistake. Future AI represents essential cognition and essential intelligence. He compares it to a nation of genius in a data center. Imagine 100 million people smarter than any Nobel Prize winner, all under the control of a certain country. His view is a firm opposition to exporting advanced chips to China. Once general AI is achieved and mastered by the CCP, it would be devastating for the world. He believes China is still behind in building high-level AI, and the chip embargo is one of the main reasons. Once lifted, they will catch up quickly. Then let's understand the second American leading figure, the one everyone is very familiar with, Google's key AI leading figure, Demis Hassabis. When Hassabis accepted an interview with CNBC, he said something quite heavy. He said the Chinese are very good at going from 1 to N, taking existing things and making them faster, cheaper, and more efficient. But from zero to one breakthroughs, creating fundamentally new architectures that didn't exist before, China has not yet proven it can do so. He said things like what DeepSeek is doing are things others have already done. They can make them cheaper, better, and faster. But things that don't exist yet, they can't create. But what Americans are doing now is zero to one. Of course, America also does 1 to N, but Chinese do 1 to N better. I noticed that some Chinese tech media have also reported on Demis Hassabis' comments, and it caused quite a reaction in China. Regarding Demis Hassabis' comments, Dario chimed in again. He said even 1 to N is enough to constitute a threat, because he understands China better. He said, don't underestimate it. Even if China is doing 1 to N things, as long as it can cross the compute threshold of millions of chips, it is enough to enter the level where powerful AI exists. So combining the views of Hassabis and Dario may give us some judgment on America's assessment of China's AI competitive capabilities. There is a gap. America is leading. But China's current main gap is in compute. Once the compute gap is breached, that may truly usher in an era of full-scale U.S.-China confrontation and competition. So from the above five aspects, through our comparative analysis of U.S.-China technology competition, we can more clearly understand the cold and rigid reality of U.S.-China technological competition. And in this competition, it is full of drama. China once started early with a high starting point. But later, due to its own corporate culture, some development concepts and government regulation, the gap widened again. Now it's catching up. Americans believe the issue is mainly the compute gap. In fact, China's AI community also faces a more difficult environment than America, the problem of government regulation. So from the competition in the AI field, everyone can also feel that in other areas of U.S.-China competition, there generally exist such respective advantages and the respective disadvantages. I welcome you to share your own views on comparisons between China and the U.S. in this area. Thank you so much for watching. If you find this video helpful, please like it, drop a comment below, and hit the subscribe button right here. It really helps YouTube suggest my video to others. I'll be back with more content soon. Until then, be well.