A recent interview with former Foreign Minister George Yeo got me thinking about happiness, although perhaps not quite in the way the interviewer intended. During The Mishal Husain Show Live from Singapore, host Mishal Husain raised the apparent contrast between Singapore's economic success and its less impressive showing on happiness rankings. In response, Yeo recalled Lee Kuan Yew's view that governments could provide economic development, infrastructure and education, but

Singapore has spent years asking: Why are Singaporeans having so few children? Perhaps we should also ask: What happens when fewer people bear the cost of raising the next generation, while all of society eventually depends on that generation? Singapore’s demographic direction is clear. Fertility is very low, people are living longer, and the population is ageing rapidly. That means tomorrow’s smaller working-age population will have to support a much larger elderly populatio

Singapore's leaders are urging companies not to use AI to cut headcount. The advice is thoughtful and partly right. But business leaders also deserve a clear account of the risks and constraints they carry, so they can make their own informed decisions. The question on the table On 28 September 2026, Senior Minister of State for Digital Development and Information Tan Kiat How told business leaders that companies using AI mainly to reduce manpower are "hobbling" their future

There are several stories about Singapore that seem to be gaining traction online. One says that an entire generation was sold a formula: Study hard. Get a degree. Find a good job. Work hard. Live a good life. Young Singaporeans followed the formula, the argument goes, only to graduate into a tougher job market, high housing prices and rising living costs. They did what they were told, yet the promised outcome never arrived. Another story is about happiness. Former Foreign Mi

Imagine an analytics proposal landing on your desk for approval. The idea sounds good. Perhaps the team wants to build a model to predict which customers are likely to leave, create a predictive-maintenance solution for expensive equipment, or develop an AI assistant to help employees find information more quickly. The technology appears capable, the team is enthusiastic, and someone may already have put together an impressive demonstration. Now the proposal is asking for som

Sometimes the most dangerous thing about data isn't that it is wrong. It is that it is completely correct — but answering the wrong question. There is a fascinating example from the First World War. When the war began, soldiers generally went into battle wearing cloth, felt or leather headgear. In trench warfare, this offered little protection from one of the biggest dangers they faced: shell fragments, shrapnel and debris raining down from artillery fire. Unsurprisingly, hea

In 2016, OpenAI trained an AI agent to play a boat-racing game called CoastRunners. To a human player, the objective seemed obvious: navigate the course, beat the other boats and finish the race. The AI discovered a rather different way to succeed. Around the course were targets that players could hit to earn points. OpenAI's researchers had assumed that a high score would be a reasonable measure of good racing because, under normal play, collecting points and completing the

Why working effectively with AI may depend less on technical expertise and more on knowing how to think, analyse and decide Whenever a major new technology arrives, there is a natural tendency to focus on learning the technology itself. When spreadsheets became important, people learned Excel. As organisations became more data-driven, employees learned analytics, dashboards and visualisation tools. Now that AI is becoming part of everyday work, many professionals are wonderin

Why good analysis starts before we open the data Imagine walking into a monthly management meeting and seeing a new sales dashboard on the screen. It looks impressive: revenue by month, sales by region, top-performing products, customer segments and year-on-year growth. There are filters to explore the numbers, several well-designed charts and perhaps even an AI-generated summary highlighting some of the more interesting movements. Then someone asks a simple question: “Why ar

Why one perfectly correct number may still give us the wrong impression. Imagine you're looking at the salaries of employees in a small company and you're told that the average monthly salary is $6,000. That sounds reasonably healthy. If that were the only number on a management report, you might assume that a typical employee earns somewhere around $6,000 a month. Now let's look at the six salaries behind that average: $3,000 · $3,200 · $3,400 · $3,600 · $3,800 · $19,000 The