Author: lethuphuong

📌 China views AI compute as strategic national infrastructure rather than the private resource of Big Tech. With AI token volume increasing more than 1,000 times in just about two years, Beijing aims to build a “super computing network” similar to a power grid or national telecom network to reduce AI costs and broaden access. This approach shows that China considers AI tokens as the “new mobile data” of the digital economy. If successful, the country could create the world’s first national-scale AI utility model while strengthening its advantage in the global AI infrastructure race.

📌The Chinese AI ecosystem is shifting strongly toward a state-led model rather than depending on private capital like the U.S. DeepSeek has become a new symbol as its valuation surged to $50 billion with direct backing from China’s national chip fund. In a context where both the U.S. and China are tightening cross-border capital flows into AI and semiconductors, the technological competition is increasingly taking on a geopolitical character. The multi-hundred-million-dollar investments in DeepSeek, Moonshot AI, or Moore Threads reflect Beijing’s strategy to build “national champions” in the global AI race.

📌 DBS is becoming one of the most aggressive Asian banks in adopting agentic AI, with 10,000 AI agents deployed in just a few months and 1.8 million prompts per month from employees. The bank uses AI to automate everything from data synthesis and credit memo writing to corporate workflow management, while maintaining strict control and monitoring mechanisms. Although AI may eliminate many traditional jobs, DBS continues to hire 500 young personnel and focuses on retraining its workforce to adapt to the AI era.

📌Sovereign AI will become a survival issue for Singapore in the AI era. While the nation may successfully train its workforce and promote AI adoption, it still faces the risk of dependence on frontier models, token pricing, and US-controlled GPUs. The author argues that Singapore needs to view AI as strategic national infrastructure similar to oil or electricity, including token reserves, guaranteed model access, and building long-term semiconductor relationships to avoid future disruptions or geopolitical pressure.

📌 This study shows that AI does not automatically help teams work better and can even reduce interaction if implemented incorrectly. A test with 60 managers across 12 companies proves that when a team collectively controls prompts, uses AI in multiple roles, and maintains collective debate, engagement increases by 30% and decision quality clearly improves. AI is most effective when it becomes a “flexible member” of the team rather than a passive answering tool.

📌 AI is restructuring the entire corporate management system, not just automating simple tasks. Big tech companies are moving from layered management models to “megamanager” models with leaner teams and a heavy reliance on AI agents. However, high management pressure has brought the risk of declining employee engagement and leadership quality. In this new phase, surviving managers will have to both understand AI and directly participate in operations instead of just supervising as before.

📌 Nvidia’s true power lies not in H100 GPUs or expensive AI hardware, but in CUDA — a software ecosystem for optimized parallel processing built over many years. CUDA creates a lock-in effect across the entire AI industry as almost every machine learning framework depends on it. While rivals like AMD, Intel, or OpenCL try to compete, the gap in ecosystem, kernel engineers, and software optimization currently makes Nvidia more like the Apple of the AI era than a typical chip manufacturer.

📌 The “shadow APIs” market is becoming a bridge for the Chinese developer community to access advanced US AI models despite geographical barriers and policies. The fact that relay services support Claude, Gemini, and ChatGPT with up to 1 million tokens of context, without a VPN, and integrate directly with Cursor or VSCode shows that AI coding demand in China is massive. This also reflects the increasingly tense technological competition as global AI access restrictions become harder to fully control.

📌 AI has moved beyond the support stage and begun directly replacing many high-level intellectual jobs such as scientific research, content creation, and knowledge production. The fact that the AI Scientist was peer-reviewed and published in Nature is a massive milestone because, for the first time, an automated system completed almost the entire research process. However, the greatest risk is not just job loss but a fundamental shift in how society values knowledge, copyright, and professional expertise when machines can produce academic products at an industrial speed.

📌 Scott Galloway is strongly rebutting the view that the AI future belongs only to programmers or technical experts. According to him, as AI becomes better at logic and coding, human competitive advantage will shift to storytelling, EQ, communication, and relationship building. It is noteworthy that many major leaders like Jamie Dimon or Andy Jassy also share the view that soft skills, continuous learning, and the ability to endure failure are the true “professional shields” against the wave of AI automation.