Author: lethuphuong

📌 “AI washing” is becoming a new trend as a series of businesses attempt to label products and services that are merely basic automation as AI. PR experts state that the press and customers are gradually losing interest in the phrase “AI-powered” due to its excessive abuse. This phenomenon reflects the immense pressure on businesses to appear relevant to AI amidst a wave of global job cuts and technological competition.

📌 AI is reversing the traditional occupational hierarchy in South Korea. While office jobs face automation pressure and large-scale layoffs, semiconductor production workers enjoy bonuses worth hundreds of millions of won thanks to the AI craze. This trend is leading many young people to prioritize skilled labor over university degrees, while simultaneously changing social perceptions of the value of blue-collar work.

📌 The Vatican’s first AI encyclical does not focus on technology but targets the power structures behind AI. The 200-page document warns that data, algorithms, and political influence are concentrating in the hands of a tech elite, thereby undermining democracy, cognitive freedom, and public oversight. The Pope calls for AI control through transparency mechanisms, effective monitoring, and social participation instead of allowing the AI race to continue escalating.

📌 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.