Author: lethuphuong

📌 Generative AI doesn’t just assist thinking; it is gradually reshaping how humans evaluate and form ideas. As the boundary between “my idea” and “AI idea” blurs, thinking risks homogenization, reducing creative breakthroughs. Even experts struggle to recognize this influence, making it essential that AI use be accompanied by clear awareness and active control.

📌 Only 5–30% of AI users actually become smarter because they possess metacognition—the ability for self-reflection. Instead of depending on AI, they use it to test, expand, and improve their own thinking. The three key factors are humility, flexibility, and vigilance. This shows that AI does not determine human capability; rather, it is the way it is used that creates the biggest difference in the AI era.

📌 The modern AI challenge is no longer about computing power but building a “trust architecture” similar to the 1832 banking system. As millions of AI agents begin autonomous negotiations in finance, healthcare, and commerce, issues like inconsistent results, lack of accountability, and irrational behavior will become severe. Without clear standards, identities, and control mechanisms, AI systems could undermine trust instead of driving the economy.

📌 AI is upgrading the research industry but simultaneously eroding the foundation of traditional training. As entry-level researchers shift toward the role of “system engineers,” they may lose the chance to hone their intuition and deep thinking. The future is not about AI replacing humans, but about the requirement for humans to become the bridge between data and emotion. Without investing in these skills, the research industry risks losing the essence of understanding humanity.

📌 AI is not killing the programming profession but is restructuring the entire industry. Demand is still rising but is concentrated in senior positions with salary increases of ~15%, while entry-level positions are nearly “frozen.” The work is shifting from writing code to managing AI and product thinking. The biggest challenge is not immediate job loss, but the risk of a shortage of the next generation of engineers in the future.

📌 AI agents only create value when managed like real personnel, with clear roles, KPIs, and supervision. Although 94% of tasks can be automated, AI currently handles only about 1/3, and less than 10% of businesses deploy them effectively. This shows the main challenge lies in management, not technology. Businesses need to shift from a “deploying AI” mindset to “operating an AI workforce” to fully leverage its potential.

📌 The Chinese government has banned Manus co-founders Xiao Hong and Ji Yichao from leaving the country after meetings with economic regulators. This move is related to the investigation into Meta’s $2 billion acquisition of the AI startup Manus in 2025. The incident highlights growing US-China tech tensions, particularly in the field of generative AI. With a $2 billion deal and a startup hitting $100 million in revenue in months, Manus has become a strategic focal point. The travel restrictions on the founders indicate that China is tightening its grip on technology. The outcome of the investigation could significantly impact…

📌 Research indicates that LLMs are not the neutral strategic advisors many leaders believe them to be. In thousands of tests and over 15,000 simulations with GPT-5, AI consistently recommended “trendy” strategies such as differentiation, cooperation, and long-term thinking, regardless of the business context. This tendency to provide identical strategic advice, favoring modern management jargon over specific situational analysis, is termed “trendslop” by researchers. This stems from the internet data and modern management culture that AI learns from. Therefore, LLMs should be used to generate ideas and analyze options, but the final strategic decisions must remain the responsibility of humans.

📌 According to AI expert Ayesha Khanna, the most important skill in the AI era is “learning how to learn,” which means knowing how to handle ambiguity, experiment, fail, and combine knowledge from many different fields. While AI can provide information rapidly, humans need to focus on soft skills such as creativity, critical thinking, and complex problem-solving. She argues that universities should shift from an exam-based model to one centered on discussion, debate, and real-world problem-solving. In enterprises, AI should be used to expand employee capabilities, helping them perform tasks that were previously impossible.

📌 The AI boom is creating a new demand for frontline deployment engineers—those who directly implement AI into businesses. In 2025, job postings for this position increased more than 10 times compared to 2024, while mentions in corporate financial reports rose from 8 to about 50. Due to the requirement for both deep technical skills and business operational understanding, only about 10% of engineers are willing to do this work. This scarcity makes engineers who directly deploy AI the key factor in determining whether AI can function in the real world.