Author: lethuha

📌 Conclusion: Singapore is deploying a massive AI retraining program for all 35,000 domestic employees of DBS, OCBC, and UOB within the next 1–2 years. The focus is on agentic AI, enabling models to act autonomously and handle complex multi-step processes beyond previous AI capabilities. The government is supporting up to 90% of salaries to retrain mid-career workers.

📌 Conclusion: JLL’s report indicates that AI is not creating a bubble but is pulling the data center industry into a long-term growth cycle. With a 14% CAGR between 2026 and 2030, 100 GW of new capacity will be generated, matching the current 100 GW total. Starting in 2027, inference will surpass training, forcing data centers to be more decentralized to serve regional users. Power supply becomes the decisive factor for location, driving the “bring your own power” model, battery storage, and energy investment alongside real estate.

📌 Conclusion: 2025 shows that AI has not drastically changed total employment: the proportion of jobs with high AI exposure remains stable, and wages in this group have even increased. AI-first companies require proof that AI cannot do the job before hiring new staff; junior-level jobs in easily automated professions (such as coding and customer service) have significantly decreased post-ChatGPT, while experienced personnel roles remain stable or have increased. The concept of “workslop” has emerged, referring to AI-generated content that looks plausible but lacks depth. AI reduces thought and hiring friction, leading to the spread of superficial documents and a flood…

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.