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In a recent report, The Wall Street Journal revisits the rise of AI in China, highlighting a long-standing talent chain from Tsinghua University. Tang Jie, founder of Zhipu AI, and Yang Zhiling, founder of Moonlight Dark Side, were once teacher and student in a Tsinghua laboratory over a decade ago. Tang guided Yang in machine learning research and recommended him for Tsinghua's highest academic award. Today, they lead Zhipu and Moonlight Dark Side, becoming prominent figures in China's AI startup wave. Their story did not begin with DeepSeek's explosive growth; Tang has been researching machine learning for about 25 years. Over the past two decades, Chinese universities have cultivated a pool of computer talent, while the internet era has accumulated vast amounts of data and engineering experience. Some researchers have entered U.S. tech companies and then returned to China to start their own ventures, connecting academic research, engineering skills, and commercialization. Tang has long set his sights on AGI. After the release of GPT-3, he expressed his desire to create machines that can 'think like humans.' In 2019, he commercialized Tsinghua laboratory technology and founded Zhipu, subsequently betting on large models. The approach of Chinese AI companies to catch up differs from that of American giants. Due to chip and funding limitations, they find it challenging to replicate the high-investment strategies of OpenAI and Anthropic, and instead seek breakthroughs in algorithm efficiency, open-source, and engineering optimization. Technologies like DeepSeek's MLA and MoE exemplify this 'higher efficiency with fewer resources' mindset. In recent years, the competition in China's AI sector has superficially appeared as a race of models, but it is also a realization of accumulated talent. Research that once remained in laboratories is now entering the industry through a wave of startups.

On August 21, Tom Lee, Chairman of BitMine, published a list of 17 large-cap stocks (market capitalization over $2 billion) that have a high correlation with cryptocurrencies, for stock investors seeking crypto exposure. Among them, BMNR has the highest correlation with ETH at 80%, followed by COIN at 74%. MSTR has the highest correlation with BTC at 78%, with COIN again at 74%. Tom Lee stated that he expects ETH to outperform BTC in the current cycle, driven by tokenization and AI applications, and believes that this logic is far more significant than the previous factors that led to ETH's outperformance in cycles.

On August 21, Tom Lee, Chairman of BitMine, published a list of 17 large-cap stocks (market capitalization over $2 billion) that have a high correlation with cryptocurrencies, for stock investors seeking crypto exposure. Among them, BMNR has the highest correlation with ETH at 80%, followed by COIN at 74%; MSTR has the highest correlation with BTC at 78%, with COIN again at 74%. Tom Lee stated that he expects ETH to outperform BTC in this cycle, driven by tokenization and AI applications, believing that this logic is far more important than the previous factors that drove ETH's outperformance in cycles.