Latest news with #AngelaZhang
South China Morning Post
4 days ago
- Business
- South China Morning Post
US tech curbs ‘incentivise' China, Trump-Xi talks may ‘happen soon': SCMP daily highlights
Catch up on some of SCMP's biggest China stories of the day. If you would like to see more of our reporting, please consider subscribing A senior member of US President Donald Trump's economic team said on Sunday that Trump may speak with China's President Xi Jinping 'very soon', adding that any such call could help break the impasse in trade talks between the world's two biggest economies. US law professor Angela Zhang says regulating AI is 'like buying insurance' and precautions must be taken, especially by pioneers in the field. Adding aerial refuelling to pilot training is seen as a significant step for China's air force. Photo: Xinhua China's air force has introduced aerial refuelling to its pilot training programme as the People's Liberation Army tries to step up combat readiness and long-range capabilities.
South China Morning Post
4 days ago
- Business
- South China Morning Post
How US tech curbs pushed China to innovate, and where a ‘super AI' could emerge
Angela Zhang is a law professor at the University of Southern California Gould School of Law. Zhang is an expert on technology regulation and antitrust in China, and her research focus has recently been on artificial intelligence oversight. This interview first appeared in SCMP Plus . For other interviews in the Open Questions series, click here Advertisement How will the new US-China tariff war affect the Chinese technology sector and competition between the two countries on artificial intelligence ? Tariffs can affect the Chinese tech sector in two main ways. First, there are the direct effects. Higher tariffs make it harder for Chinese tech products to compete in the US market. If the tariffs stay relatively modest – like the levels we saw before 'Liberation Day' – Chinese suppliers might absorb some of the extra costs. Prices would go up a bit, but their products might still be competitive. But if the tariffs go up dramatically – say, to the 145 per cent that President [Donald] Trump proposed before China and the US reached an agreement in Geneva – then Chinese products would probably lose their price advantage and be effectively shut out of the US market. Second, there are indirect effects. As Chinese companies face more trade barriers from the US, they'll need to work even harder on cutting costs and scaling up. A good example is what we saw in response to the chip embargo – many Chinese tech firms started open-sourcing their AI models. That's a more cost-effective and collaborative way to keep developing advanced technologies when resources are tight. I expect we'll see similar strategies in other areas like electric vehicles, batteries and renewable energy. These firms are going to keep pushing hard to make their products even more competitive. Advertisement At the same time, they're not going to sit still. I think we'll see Chinese companies look more aggressively to other markets – Asia, Latin America, Africa. And we'll likely see more efforts to deepen trade with Europe, including offering technology transfers in exchange for market access. Of course, there's a lot of uncertainty ahead. But one thing is clear: Chinese firms are going to face serious challenges in the coming years under the Trump administration.
Forbes
27-05-2025
- Business
- Forbes
The AI Arms Race: Why China May Be Playing For Second Place
Tencent's hunyuan model and OpenAI's ChatGPT In the high-stakes arena of artificial intelligence, where tech giants vie for dominance, a fascinating new narrative is emerging. Observers at Google's recent I/O Developer Conference couldn't help but notice the striking presence of Chinese-developed AI models prominently featured alongside American tech stalwarts. As LLMs (large language models) become critical yardsticks of technological prowess, China's rapid ascent is reshaping global AI dynamics. At Google's annual showcase, the Chatbot Arena leaderboard—an influential crowdsourced benchmark hosted by LMSYS on Hugging Face—highlighted remarkable advances by Chinese AI models. Names such as DeepSeek, Tencent's Hunyuan TurboS, Alibaba's Qwen, and Zhipu's GLM-4 weren't just entries—they were top contenders, especially in critical tasks like coding and complex dialogues. This shift suggests that while U.S. companies like OpenAI and Google maintain overall leadership, China's AI ambitions are gaining undeniable momentum. TOPSHOT - Google CEO Sundar Pichai addresses the crowd during Google's annual I/O developers ... More conference in Mountain View, California on May 20, 2025. (Photo by Camille Cohen / AFP) (Photo by CAMILLE COHEN/AFP via Getty Images) Yet, intriguingly, China might not be racing to win outright. Angela Zhang, a USC law professor and author of "High Wire: How China Regulates Big Tech and Governs Its Economy" argues a contrarian view in a recent essay in the Financial Times. According to Zhang, Beijing may have strategically decided that being a close second in AI serves its broader economic and geopolitical interests better than direct supremacy. This counterintuitive stance arises partly from recent aggressive U.S. measures restricting advanced semiconductor exports to China. By blocking sales of critical chips like Nvidia's H20—optimized for AI inference tasks—Washington aims to maintain a technological edge. However, these policies inadvertently push China towards accelerating its domestic semiconductor capabilities. Chinese firms like Huawei and Cambricon have swiftly moved into the vacuum, with Huawei's Ascend 910c chip already delivering about 60% of Nvidia's H100 inference performance. Moreover, U.S. chip export controls have broader global implications, extending restrictions to critical markets like India, Malaysia, and Singapore. Faced with these challenges, emerging economies may increasingly turn to China, indirectly spurring demand for Chinese technology. In a significant policy shift, the Trump administration recently rescinded the Biden-era AI Diffusion Rule, which categorized countries into tiers for AI chip exports. Instead, the administration has issued new guidance stating that the use of Huawei's Ascend AI chips—specifically models 910B, 910C, and 910D—anywhere in the world violates U.S. export controls. This move effectively imposes a global ban on these chips, citing concerns that they incorporate U.S. technology and thus fall under U.S. regulatory jurisdiction. The Department of Commerce's Bureau of Industry and Security emphasized that companies worldwide must avoid using these chips or risk facing penalties, including potential legal action. This unprecedented extraterritorial enforcement has drawn sharp criticism from China, which warns of legal consequences for entities complying with the U.S. directive, arguing that it infringes upon international trade norms and China's development interests. In response, China's AI leaders have redoubled efforts in semiconductor self-sufficiency. Huawei, for instance, spearheads a coalition aiming for China to achieve 70% semiconductor autonomy by 2028. The recent unveiling of Huawei's CloudMatrix 384 AI supernode—a system reportedly surpassing Nvidia's market-leading NVL72—signifies a crucial breakthrough, addressing a critical bottleneck in China's AI computing infrastructure. Tencent's strategy further illustrates this strategic shift. During its May AI summit, Tencent introduced advanced models such as TurboS for high-quality dialogue and coding, T1-Vision for image reasoning, and Hunyuan Voice for sophisticated speech interactions. Additionally, Tencent has embraced open-source approaches, making its Hunyuan 3D model widely available and downloaded over 1.6 million times, underscoring China's commitment to fostering global developer communities. Google's former CEO Eric Schmidt recently named directly, in addition to DeepSeek, China's most noteworthy models are Alibaba's Qwen, as well as Tencent's Hunyuan. their level has been quite close to Open AI's o1, which is a remarkable achievement. Angela Zhang suggests this positioning is intentional. Rather than risking further escalations in U.S.-China tensions, Beijing appears content to cultivate robust domestic and international ecosystems around its technology. This stance aligns well with China's traditional emphasis on strategic autonomy and incremental innovation. Open-source dynamics reinforce this calculated approach. With lower technical barriers in AI inference—a rapidly expanding market segment expected to dominate 70% of AI compute demand by 2026, according to Barclays—China's AI industry could benefit significantly from widespread adoption of its domestically developed solutions. Open-source releases from Chinese firms like DeepSeek and Baichuan also bolster global developer engagement, potentially offsetting U.S. containment efforts by creating diverse, globalized ecosystems reliant on Chinese technology. Still, it's crucial to note the challenges ahead. While Chinese models excel technically, global adoption remains limited, mostly confined to domestic markets. Issues like interface design, user familiarity, and developer support still give U.S.-based models a distinct advantage internationally. Moreover, despite impressive hardware strides, China continues to trail the U.S. in software sophistication and ecosystem integration. Yet, the trajectory is clear. China's foundational models are rapidly closing technical gaps. With strategic governmental support and substantial investment in semiconductor self-sufficiency, China appears poised not just to endure U.S. sanctions but to thrive within their constraints. Zhang's insight reframes the AI race less as a zero-sum game and more as a multipolar competition, where nations seek strategic rather than absolute dominance. For China, being second might be more beneficial, reducing geopolitical friction while securing substantial economic benefits through technology self-reliance and international partnerships. Ultimately, the AI landscape is shifting rapidly. Leadership in this field will increasingly hinge on adaptability, global collaboration, and strategic foresight rather than merely raw computing power. For now, China's measured pursuit of second place might be exactly the kind of innovative thinking the tech world needs—less about outright dominance and more about sustainable and strategic competitiveness.
South China Morning Post
05-02-2025
- Business
- South China Morning Post
China's antitrust probe into Google seen as warning shot to the US
Published: 10:40pm, 5 Feb 2025 China's antitrust investigation into Google , which does not offer most of its consumer-facing services including search and email in the country, seems to make little sense on the surface, but analysts said the move could be seen as a warning shot to the US and a threat to the Android operating system. The inquiry into Google was unveiled on Tuesday in tandem with China's fresh tariffs on US imports and new export controls of certain minerals, as well as the addition of two US companies to China's unreliable entity list . The move against Google showed that 'Beijing has effectively fired a warning shot to Washington, signalling its readiness to retaliate,' said Angela Zhang, a law professor at the University of Southern California and author of Chinese Antitrust Exceptionalism: How the Rise of China Challenges Global Regulation. US President Donald Trump, left, sits with Chinese President Xi Jinping during a bilateral meeting in Florida in 2017. Photo: AFP/Getty Images/TNS While the one-line statement from China's market regulator contained no details about the investigation, state media has suggested it was related to Android – Google's open-source operating system.
Yahoo
28-01-2025
- Business
- Yahoo
DeepSeek caused a $600 billion freakout. But China's AI upstart may not be the danger to Nvidia and U.S. export controls many assume
AI has fueled Nvidia's extraordinary rise to a $3 trillion market valuation. But on Monday, AI was the cause of a panic among Nvidia investors, sending its shares down almost 17% and wiping out nearly $600 billion in value. The selloff was triggered by Chinese AI startup DeepSeek, whose latest V3 and R1 AI models appear to rival the best of any U.S. company, while having been trained at a fraction of the cost. Since Nvidia's powerful graphics processing units are one of the biggest costs of developing the most advanced AI models, investors are suddenly, and radically, questioning their assumptions about the AI business. While there still many unanswered questions about how DeepSeek developed its models, the upstart is clearly shaking up the AI market. The prognostications of Nvidia's doom may be premature however. So too may be claims DeepSeek's success mean the U.S. should abandon policies aimed at curtailing China's access to the most advanced computer chips used in AI. DeepSeek has said it has access to 10,000 of Nvidia's older generation A100 GPUs—chips that were obtained before the U.S. imposed export controls that restricted the ability of Chinese firms to buy these top-of-the-line chips. It has also mentioned training V3 on Nvidia's H800 chips, a chip Nvidia sells in China that is designed specifically to comply with U.S. export controls. Either way, that is orders of magnitude less processing power than what U.S. companies typically use to train their most advanced AI models. For instance, Elon Musk's Xai built a computing cluster, called Colossus, in Tennessee, that has 100,000 of Nvidia's more advanced H100 GPUs. What's more, DeepSeek's R1 model, an AI model it created to do well on math, logic problems, and coding, and which is designed to challenge OpenAI's o1 'reasoning' model, is small enough to run on a laptop, where the primary processing power comes from a conventional central processing unit (CPU), rather than requiring access to many GPUs running in a datacenter. It's not just investors who have seized on this news. Critics of U.S. export controls on advanced computer chips have pointed to DeepSeek's success as evidence that the trade restrictions aren't working. Some even argued the export restrictions have backfired—meant to hobble China's AI companies and keep them from catching up to the U.S., they have instead forced Chinese AI researchers to develop clever ways to make AI models that are much more efficient in their use of computer power. 'China's achievements in efficiency are no accident. They are a direct response to the escalating export restrictions imposed by the US and its allies,' Angela Zhang, a professor of law at the University of Southern California and author of a book on Chinese tech regulation, wrote in an op-ed in the Financial Times last week. 'By limiting China's access to advanced AI chips, the US has inadvertently spurred its innovation.' AI skeptic Gary Marcus also echoed these arguments on his blog on Sunday. Both these emerging narratives could prove shortsighted. That's because DeepSeek's impact could, counterintuitively, increase demand for advanced AI chips—both Nvidia's and those being developed by competitors. The reason is partly due to a phenomenon known as the Jevons Paradox. Named for 19th Century British economist William Stanley Jevons, who noticed that when technological progress made the use of a resource more efficient, overall consumption of that resource tended to increase. This makes sense if the demand for something is relatively elastic—the falling price due to the efficiency improvement creates even greater demand for the product. Jevons Paradox could well come into play here. One of the things that has slowed AI adoption within big organizations so far has been how expensive these models are to run. This has had made it hard for business to find use cases that can earn a positive return on investment, or ROI. This has been particularly true so far for the new 'reasoning' models like OpenAI's o1. But DeepSeek's models, especially its o1 competitor R1, are so inexpensive to run that companies can now afford to insert them into many more processes and deploy them for many more use cases. Taken across the economy, this may cause overall demand for computing power to skyrocket, even as each individual computation requires far less power. Both Microsoft CEO Satya Nadella and former Intel CEO Pat Gelsinger made this point in posts on social media on Monday. Nadella explicitly referenced Jevons Paradox, while Gelsinger said 'computing obeys' what he called 'the gas law.' 'Making it dramatically cheaper will expand the market for it…this will make AI much more broadly deployed,' he wrote. 'The markets are getting it wrong.' Now the question becomes exactly what kind of computer power will be needed. Nvidia's top-of-the-line GPUs are optimized for training the largest large language models (LLMs), such as OpenAI's GPT-4 or Anthropic's Claude 3-Opus. The company has less of an edge when it comes to what AI researchers and developers call inference—that is using a fully trained AI model to perform a task. Here some of Nvidia's rivals, including Advanced Micro Devices (AMD) and upstarts such as Groq, have claimed they can run AI applications faster and much more efficiently in terms of power consumption than Nvidia's GPUs. Alphabet's Google and Amazon's AWS also build their own AI chips, some of which are optimized for inference. Some of these rivals could indeed begin to erode Nvidia's dominant market position. (The company currently controls more than 80% of the market for data center-based AI computing; when the cloud providers' bespoke silicon is excluded, Nvidia's share may be as high as 98%.) But Nvidia is unlikely to lose this dominance quickly or entirely. Its GPUs can also be used for inference—and its GPU programming software, CUDA, has a large and loyal developer community that is unlikely to defect overnight. If overall demand for AI computer chips increases due to Jevons Paradox, Nvidia's overall revenues could still continue to climb, even if its marketshare drops, as it will own a smaller percentage of a larger, and growing, pie. Another reason that demand for advanced computer chips is likely to continue to grow has to do with the way reasoning models like R1 work. Whereas previous kinds of LLMs became more capable if they used more computer power during training, these reasoning models use what is called 'test time compute'—they provide better answers the more computing power they use during inference. So while one might be able to run R1 on a laptop and get it to output a good answer to a tough math question after, say, an hour, giving the same model access to GPUs or AI chips in the cloud might allow it to produce the same answer in seconds. For many business applications of AI, latency, or the time it takes a model to produce an output, matters. The less time, generally the better. And to get that time down with reasoning models still requires advanced computing chips. For these reasons, it probably still makes sense—if the U.S. sees it as a national security priority to make it more difficult for China to compete on AI—to continue to restrict the country's access to the most cutting-edge computer chips. Miles Brundage, an AI policy expert who recently left OpenAI, made this point on a podcast over the weekend, saying that even if DeepSeek proved that powerful AI models could be built on fewer, less advanced chips, it would still always be an advantage to have access to more advanced chips than not. 'I think everyone would much prefer to have more compute for training, running more experiments, sampling from a model more times, and doing kind of fancy ways of building agents that, you know, correct each other and debate things and vote on the right answer,' Brundage said. 'So there are all sorts of ways of turning compute into better performance, and American companies are currently in a better position to do that because of their greater volume and quantity of chips.' So export controls might still slow China down when it comes to using AI everywhere it would like to—which would give the U.S. an advantage economically and perhaps militarily, when it comes to deploying AI and reaping its benefits. On top of this, there's another argument for why this may not be quite as bad news for Nvidia and U.S. national security policy as investors and critics think: it's entirely possible DeepSeek has been less than truthful about how many top-flight Nvidia chips it has access to and used to train its models. Many AI researchers doubt DeepSeek's claims about having trained its V3 model on about 2,000 of Nvidia's less capable H800 computer chips or that its R1 model was trained on so few chips. Alexandr Wang, the CEO of AI company Scale AI, said in a CNBC interview from Davos last week that he has information that DeepSeek secretly acquired access to a pool of 50,000 Nvidia H100 GPUs (its latest model). It is known that HighFlyer, the hedge fund that owns DeepSeek, had amassed a substantial number of less capable Nvidia GPUs prior to export controls being imposed. If this is true, it is quite possible that Nvidia is in a better position than investor panic would suggest—and that the problem with U.S. export controls is not the policy, but its implementation. This story was originally featured on
