Singapore has invested more than S$1 billion in AI, but AI Singapore sees a shortage of people who can build it

Singapore has already invested over S$1 billion to become a global artificial intelligence hub. But within the national AI ecosystem itself, a warning is sounding: the country needs to do more than teach people how to use ready-made tools — it needs to grow faster the people who know how to build their own models and products.

Betting on Homegrown Models

Singapore has been consistently strengthening its AI agenda in recent years: it has updated its national strategy, allocated separate budget measures, and is expanding programs for business and education. The official goal is not simply to implement foreign solutions, but to form a local school of developers, researchers, and teams capable of creating technology within the country. In the NAIS 2.0 strategy, authorities directly speak about growing the domestic pool of AI practitioners to 15,000 people over five years, and about AI becoming a factor in economic resilience, not just a tool for improving efficiency.

Against this background, the statement by Leslie Teo, senior director of AI Singapore and one of the architects of local AI policy, is particularly striking. According to him, the current approach to the country's readiness may be shifting toward "certified users" of AI, while the economy needs "builders" — engineers, researchers, and product teams who know how to train, adapt, and deploy systems. For a small country, this is a question not just of growth, but of technological sovereignty: if domestic expertise is limited, you have to live by rules set by others.

Where Learning is Stalling

The main state instrument here is SkillsFuture, a program of training credits and subsidized courses for citizens throughout their careers. In 2025, approximately 606,000 people went through supported programs. But Teo points not to reach, but to speed: by the time a course is designed, approved, and officially launched, its content can become outdated. In AI this is especially painful, because tools, approaches, and even basic practices change literally every quarter.

• Formal programs are updated more slowly than the AI market changes
• Companies are increasingly unwilling to retrain junior staff internally
• The gap is growing between the skill of "using AI" and the ability to "build AI"
• It is dangerous for a small economy to be completely dependent on models created abroad

"Junior staff are cheap.

AI is even cheaper." This leads to Teo's conclusion: basic training of early-level specialists may need to be considered by the state as a public good. Previously, employers took on part of this function, but with automation, the motivation to invest in entry-level positions is falling. As a result, the market may get many people with AI certifications, but far too few who have gone through real engineering training on actual projects.

The Market is Already Changing

The effect is already visible in the labor market. According to an annual survey of Singapore's universities, the share of graduates who obtained permanent full-time employment fell to 74.4% in 2025 compared to 79.4% a year earlier. This is not proof that AI alone is "eating" entry-level jobs, but an alarming signal: automation and restructuring of junior roles are already affecting the first rung of the career ladder.

At the same time, the question for Singapore is broader than graduate employment. If the country relies entirely on AI systems created abroad, it has almost no influence on how these systems develop, what languages and cultural contexts they support, and whose interests are embedded in them by default. This is why AI Singapore is developing SEA-LION — a family of large language models for Southeast Asia, which is already being used by regional companies, including GoTo Group. A homegrown model doesn't solve the staffing deficit, but it gives the country a seat at the table where technological decisions are made.

What This Means

Singapore demonstrates a problem that will soon become common to many markets: mass AI literacy by itself does not create an AI industry. The next stage of competition is not teaching everyone how to use chatbots, but quickly building a pipeline of engineers, researchers, and product teams, before formal education and the labor market fall completely behind the technology.