The AI Arms Race: Why China May Be Playing For Second Place

Forbes

27-05-2025

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.