Author: lethuphuong

📌 The 10% layoff wave at McKinsey shows that the elite analysis-based consulting model is reaching its limit in the AI era. AI not only reduces information asymmetry but also “flattens” analytical and recommendation capabilities – the core value foundation of strategic consulting. The value focus is shifting from strategy to technology-based execution, where strategy and execution occur simultaneously and continuously.

📌 The phenomenon of “cognitive offloading” – outsourcing thinking to tools like AI – is becoming widespread and could erode core thinking capabilities. When given a choice between thinking for themselves or using AI, humans often choose AI, even when they are fully capable of solving the problem themselves. The cause is metacognitive: humans underestimate their own abilities, leading to a habit of avoiding mental effort. Smart people face a paradox: they leverage AI best but also risk losing the training process that created that intelligence.

📌 The phenomenon of “cognitive offloading” – outsourcing thinking to tools like AI – is becoming widespread and could erode core thinking capabilities. When given a choice between thinking for themselves or using AI, humans often choose AI, even when they are fully capable of solving the problem themselves. The cause is metacognitive: humans underestimate their own abilities, leading to a habit of avoiding mental effort. Smart people face a paradox: they leverage AI best but also risk losing the training process that created that intelligence.

📌 AI is fundamentally changing the working models of top consulting firms like McKinsey, BCG, PwC, EY, Deloitte, and Accenture. The focus is shifting from short-term consulting projects and slide decks to multi-year AI transformation programs, requiring capabilities in technology construction and operation. Engineers and tech experts are increasing rapidly. Competitive advantage now lies in hybrid personnel: understanding business, knowing how to deploy AI, communicating well, and learning extremely fast. The race is not just about hiring more engineers, but retraining hundreds of thousands of people to consult in the AI era.

📌 The Australian federal government announced an investment of 225.2 million AUD (approx. 151 million USD) for the deployment of AI in the public sector over the next 4 years. The focus is on the GovAI program – AI services hosted on sovereign infrastructure, used across all government agencies. GovAI includes a shared AI platform, training programs, and a secure AI assistant named GovAI Chat. 166.4 million AUD is allocated in the first 3 years to expand the GovAI platform and to design, build, and test GovAI Chat.

📌 OpenAI announced FrontierScience, a new benchmark evaluating AI’s expert-level scientific reasoning in three core fields: physics, chemistry, and biology, marking a shift from knowledge memorization to true scientific thinking: hypothesis formation, verification, refinement, and interdisciplinary synthesis. With over 700 difficult questions built by experts, results show GPT-5.2 reaching 77% on theoretical problems but only 25% on open research tasks. This indicates that AI is strong enough to support science, but a large gap remains before it can truly generate new scientific breakthroughs.

📌 President Donald Trump has signed an executive order aiming to limit the ability of US states to enact their own laws regulating AI companies. Trump’s AI order reflects the sharp conflict between the goal of technological leadership and the need for social protection. In the context of a deadlocked Congress, the White House has chosen to centralize AI regulation at the federal level, prioritizing accelerated investment and competition with China. However, nullifying state laws risks creating a “regulatory vacuum,” raising concerns about safety, citizens’ rights, and the role of local control over power.

📌 The Chair of the US Federal Reserve expressed concern that the AI wave in the US could cause more job losses than the number of new jobs created. He emphasized that over the past 200 years, technology has often destroyed some jobs but ultimately created enough new ones, yet this time “might be different.” In the context of the Fed lowering interest rates to 3.75% and forecasting unemployment rising to 4.5%, AI is becoming a new factor altering the balance between growth and labor.

📌 The race for space-based AI data centers is becoming a new technological front between China and the US, tied tightly to national security, economic dominance, and the future of global connectivity. China currently leads in actual deployment with a 5-peta-operations constellation and over 1,000 days of operation, while the US counterattacks with massive capital and a vision of 100 gigawatts of AI in orbit.

📌 The UAE government has officially launched a smart system using AI to manage and evaluate the performance of federal officers and civil servants, with the goal of increasing productivity, transparency, and strategic execution efficiency. The federal-wide implementation from January 1, 2026, combining OKRs, KPIs, and behavioral competencies, along with AI for goal suggestions and training, promises to create a flexible HR ecosystem, reduce bureaucracy, and promote positive competition in the public sector.