Author: lethuha

Conclusion: A study published in January 2026 indicates that AI only truly enhances creativity for a specific group of employees, not all. Only those with strong metacognition can leverage AI to expand knowledge and free up cognitive capacity, thereby creating newer and more useful ideas. Metacognition is the ability to plan, monitor, evaluate, and adjust one’s own thinking process. Low-metacognition employees often accept AI’s first answer without vetting. The issue is not about using AI, but the way of thinking when using it.

📌 Conclusion: The U.S. state of Utah is making a big bet by granting AI the power to renew prescriptions, setting an unprecedented precedent in American healthcare. With a 99.2% accuracy rate according to corporate data, this model promises to reduce costs and improve access to care. However, the line between innovation and risk remains thin,especially as the FDA has yet to clearly define its regulatory role for AI “practicing medicine” in 2026.

Conclusion: AI is rapidly penetrating all sectors in Thailand, yet the nation remains a “technology taker” reliant on foreign expertise. While the private sector adapts fast, the public sector lags due to mindset issues. The government is urged to adopt an “AI-first policy” across all departments rather than focusing on isolated areas.

📌 Conclusion: New research from Johns Hopkins University demonstrates that AI can display brain-like behavior even without prior training data. Scientists are shifting focus toward bio-inspired AI architectures instead of data-heavy scaling. This not only opens new directions for AI but also raises significant questions about current generative AI models that depend on massive data.

Conclusion: The CEO of Perplexity AI warns that the data center-centric AI model could be disrupted if intelligence is packaged and run directly on personal devices. He raises a $10 trillion question regarding current infrastructure investment strategies. If local AI becomes feasible, Apple, Qualcomm, and device makers could benefit greatly, while the centralized data center model faces risks. This is not just a tech story, but a risk of an economic bubble and a reshaping of the entire AI value chain.

📌 Conclusion: 2026 is a watershed moment where old governance models and general-purpose hardware fail to meet the demands of an AI-driven world. Success now hinges on microshifting, AI-augmented talent focusing on EQ, and a hardware shift from training to specialized inference (prefill vs. decode).

📌 Summary: BCG is transforming from a strategic consulting firm into an “AI product factory,” with over 36,000 agents and a bottom-up innovation model. The combination of front-line consultants, centralized R&D, and strict risk control demonstrates that AI is no longer an experiment. With the message “every company must become a tech company,” BCG is turning itself into living proof of that strategy.

📌 BCG is transforming from a strategy consulting firm into an “AI product factory,” with more than 36,000 agents and a bottom-up innovation model. The combination of frontline consultants, centralized R&D, and strict risk controls shows that AI is no longer experimental. With the message “every company must become a tech company,” BCG is turning itself into a living proof of that strategy.

• The current AI bubble is being compared to the dot-com bubble of 2000, when internet technology drove stock markets to record highs before a sharp collapse.• When the dot-com bubble burst, the Nasdaq fell nearly 80% between 2000 and 2002, while S&P 500 investors needed about six years to break even.• A U.S. study from 2000 showed that the retirement rate among workers fell by around 3% as pension assets evaporated.• Today, around 45% of UK pension fund assets are invested in U.S. equities, a sharp increase from previous levels.• AI now accounts for a growing share of the…