Author: lethuha

📌 Nvidia is not accused of fraud like Enron, but its growth model relies heavily on circular, vendor-financing-like AI deals—investing or lending so customers can buy Nvidia’s own chips—concentrating risk on the assumption that AI will boom rapidly. With massive figures such as $125 billion in annual transactions and $1.4 trillion in AI infrastructure bets, Nvidia’s future depends on customers becoming profitable quickly enough to keep buying chips. If AI fails to “take off” as expected, investor confidence and the stock price could face a major shock.

📌 As China tightens controls on private tutoring, parents are turning to AI to reduce education costs. Dola, developed by ByteDance, has around 172 million monthly users. It reminds children to sit properly, stop playing with pens, and work faster. Dola also acts as a tutor by grading work, explaining mistakes, and creating similar questions based on students’ weaknesses. Some parents believe AI helps avoid conflicts because children listen to it more readily and it speaks calmly and patiently. Experts warn that AI may reduce the level of conflict necessary for children’s brain development and social skills.

Business leaders are facing an uncomfortable reality: employees are already using AI—often before formal approval—forcing organizations to turn spontaneous experimentation into a strategic advantage if they don’t want to lose to more agile competitors.AI-native companies, built from the ground up with AI as their foundation, are pulling far ahead of those that merely “adopt” AI; however, the latter can still reach AI-native status through a four-stage enterprise AI maturity journey. Stage 1 – Curiosity: employees independently use popular LLMs such as ChatGPT and agents like Genspark for Q&A, call recording, and basic research; usage frequency is three times higher than leaders…

📌 The wave of massive AI infrastructure construction is creating a major transparency issue in accounting, as chip and data center costs are often lumped together. Over $214.5 billion in construction-in-progress is combined between short-term chips and long-term assets. When AI chips may become obsolete in under 3 years but are hidden within 20–40 year projects, investors struggle to assess true risks. This lack of transparency could obscure capital waste and distort profits in the global AI race.

📌 Yann LeCun, one of the godfathers of modern AI, declared that Large Language Models (LLMs) cannot reach human-level intelligence and called the current approach “nonsense.” His new direction focuses on “world models”—systems that build models of how the world functions and predict the consequences of actions. Leaving Meta to found a startup in Paris, training models with only a few thousand GPUs, and comparing them to 200,000-GPU systems demonstrates a completely different path.

📌 With a support package of approximately $6.3 billion, Japan is sending a strong signal that AI is a national priority. Focusing on developing domestic AI, combining the public and private sectors, helps reduce foreign technology dependence and enhance global competitiveness. If effectively implemented starting from fiscal year 2026, this strategy could reshape Japan’s position in the international AI race.

📌 McKinsey estimates sovereign AI could become a $600 billion market by 2030, with about 40% of AI workloads in the public sector and highly regulated industries. Sovereign AI is becoming a decisive factor in national competitiveness, but the path from ambition to execution remains fraught with obstacles. Currently, only about 30 countries possess domestic compute infrastructure strong enough for advanced AI; many lack models, applications, energy, and governance frameworks. Building sovereign AI requires a synchronized ecosystem ranging from energy, chips, and data to applications and talent.

📌 When AI becomes the primary news source, the issue is no longer just “true or false,” but how the story is told. AI’s communication bias can silently shape public opinion and social emotions without spreading fake news. This poses a major challenge to democracy and media: to protect the information space, society needs technological competition, model transparency, and more user control rather than just relying on regulation.

📌 The great paradox of the AI era is not a confrontation between humans and AI, but a gap between leaders with high versus low Communication Intelligence (CQ). Since this is a neurobiological capacity rather than just a social skill, it includes adaptive communication under pressure and real-time physiological regulation. Therefore, as AI becomes more sophisticated, CQ becomes the ultimate strategic advantage for any leader.

📌 Summary: UK banks are racing to deploy Agentic AI, which can plan and act independently for retail customers. Unlike generative AI, it can automate fund transfers and portfolio adjustments. Regulators warn of new consumer risks even as the technology promises to revolutionize financial management.