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The AI race is no longer just about scale—it’s about standards, trust, and strategic agility. As major powers build ever-larger models, Singapore is charting a different course: investing in global assurance frameworks, cross border digital integration, and a radically expanded pipeline of AI-skilled talent. Its approach is systems-first and credibility-focused—offering a model for how small states can lead by shaping norms rather than dominating infrastructure. But in an increasingly fractured landscape shaped by open-source disruption and deepening U.S.–China tensions, can neutrality still deliver influence? We'll discuss what makes Singapore’s AI strategy distinctive—and consider what other countries might learn from the Singapore model in crafting their own AI strategies. 

Beyond Scale: How Small Nations Can Lead In The Age Of AI

Now, to kick this off, I'm thrilled to introduce our opening conversation led by Fortune's executive editor for Asia and Brainstorm AI co-chair, Clay Chandler in conversation with our first guest, Singapore's Minister for Digital Development and Information, Josephine Teo. When we talk about global leadership in AI, it's easy to focus on the big players, but Singapore continues to prove that small nations can lead in big ways. This isn't just a story about scale. It's about strategy, standards, and Singapore's bet that credibility can compete with compute. Please now join me in welcoming Clay Chandler and Minister Teo. Thank you. Thank you, Jeremy, and welcome everyone and especially welcome Mr. Chou. It is such a delight to have you back. Uh, you were our kickoff speaker last year when we first launched this franchise in Singapore. We had a great discussion. Uh, that explored the ways in which Singapore was really pioneering how small and medium sized economies could punch above their weight when it came to artificial intelligence, and I'm really quite eager to have a kind of catch up conversation with you about that because so much has changed just in the last year since you were last here. Um, I wonder if we could start maybe by just a reference to the um. The AI action summit in Paris this year, which is a big event that got a lot of people's attention. Singapore there launched a global AI assurance pilot. And more recently, it's established a thing called the AI Verify Foundation. Can you tell us about these initiatives and and what they are and how they evolved? Well, it's great to be back and I'm also very happy that you've returned to Singapore for AI event. Um, you. Referenced what we were doing at the AI action summit. Uh, perhaps for context for the audience in the room today, uh, the AI action summit that took place in Paris is a 3rd edition of what started out in Bletchley as the AI safety summit. And then in between, there was a summit that was held in Korea. And by the time it got to Paris, our French colleagues decided that They didn't want to focus so much on safety, they wanted to focus on action. But if we ask what we really want to see in AI development, the way Singapore thinks about it is that this being general purpose technologies, you do want um it to be very widely adopted across many industry settings as well as across a whole range of organizations. So when we updated our national AI strategy, we thought that we needed to ensure that um uh people understood what this was going to be for. So we said that the vision for Singapore was AI for the public good, for Singapore and the world. And why we decided to focus on public good was that The incentives would naturally create many commercial applications of AI, but for the whole of society to benefit, for people to appreciate its value, and this being general purpose technology, you also want AI to be applied towards public healthcare, public transportation, uh, public safety in policing, for example. So that's one dimension of AI development that we wanted to see in Singapore. The other dimension is equally important. In many of these commercial settings, you would want AI to be implemented, deployed in a responsible way. So the question, however, is what does responsible AI look like? Well, it really depends on the application and at which layer of AI development you're talking about. Are you talking about the infrastructure, you're talking about the models, or you're talking about the applications? And Apart from being able to articulate what responsible AI looks like, you also need to answer the question of, well, how do you get that? And if you say that you are implementing responsible AI, how do you test for it? How do you assure people that in fact this is happening? So, As we were also promoting AI adoption, we decided at the same time that these questions deserved investigation. You need to put aside resources to research, but at the same time, it's useful for us to start developing practical tools. To answer the question of what responsible AI looks like. That's how the global AI assurance pilot came about. It was to create uh a number of methods by which AI developers as well as deployers were able to assure their stakeholders that they were implementing in a responsible way. And the idea was also to learn from it, accepting the fact that at this early stage, there are no interoperable standards that apply across the globe. We don't know when we will get there. And so Singapore's modest hope is to contribute to this conversation and to bring about international collaboration so that we can all develop best practices in AI development. There is a lot to think about there. Um, I want to come back and maybe talk a little bit more about how Uh, assurances, the strategy aligns with the kind of commercial competitiveness objectives of Singapore, but I want to, before we get to that, uh, ask you about one of the other big developments that's happened in the last year, which happened just about the time. Uh, a lot of people were in Davos, uh, and, uh, I remember being in Davos and hearing people at the beginning of Davos sort of pooh poohing China's ability to catch up to the United States. Oh, Silicon Valley is so far out in front in these foundational AI models that China can't play the same game. And then, uh, right in the middle of Davos there was this kind of announcement that emerged that there was this thing called Deep Seek. There was a Chinese hedge fund that had, uh. Sort of issued this new platform in AI that was produced with a fraction of the resources of OpenAI or Gemini, but seemed to be pretty close on all the significant benchmarks of performance. Last year we talked about, you know, the age of. Uh, you know, big data and, and AI superpowers and whether that created daunting obstacles for smaller states like Singapore. So I'm curious to hear your thoughts about the implications for countries like Singapore. For the deep sea breakthrough, we've had a couple of these other really kind of fast follower platforms come through in China recently. Um, is this something that even Singapore can now compete, you know, by following those kinds of examples? How does it open up and democratize the the the AI game for a country like Singapore? Well, the thing that we ask ourselves is what are the impediments of using any sort of technology for innovative purposes. And inevitably cost becomes an issue because for a company that is uh deploying technology already in a certain way to use AI as an alternative or as an addition, the question of cost must come up and it has to be weighed against the benefits. So when the cost comes down, then whatever benefits you were able to get from it may look more attractive. So from the perspective of bringing down costs. Innovation such as Deepeek are very much welcome. But I would also say that um this whole dynamic is not necessarily only a competitive one. It is also mutually reinforcing. Let me give you an example. We don't use deep per se to develop our own large language model, but within the context of Southeast Asia, we know that large language models that are trained primarily on the Western corpus, primarily on perhaps English as the language, um, they will have difficulties being applied in the Southeast Asian context. Um In our case, there are already probably more than 1000 local languages and if you build AI tools on top of these large language models that didn't incorporate the kind of data that could be found in our part of the world, then naturally the quality, the way it performs, the way it responds to prompts will perhaps not meet. The requirements of Singapore, as well as our neighboring countries. What we did was to therefore develop our own large language model, not necessarily the biggest, but certainly trained on languages that we find commonly in our part of the world. You know, um, Malay language, Tamil, Indonesia. This is sea lion, you are very familiar with it. So I think there is room for both, uh, and indeed, many companies, when they are thinking about how they can develop, for example, chat assistance that could be useful in our context, they would use a combination of both. And so that's how I think about it. I think that in an innovation ecosystem. Um, ways in which we can bring down the cost, ways in which we can complement one another in terms of the models that are available. I think they just open up the space for innovation to a larger extent. And in the Singapore context, um, when we think about AI governance and in AI innovation, The two are not necessarily at odds with each other, being able to assure ourselves of the safety and proper governance actually, I think can enable innovations to go further, because then the developers and deployers know where are the markets that they should work with. It's very interesting because it's a very, it's a much more kind of neutral approach to evaluating technology and And AI platforms in the US right now, a lot of this stuff is very politicized, so we thought that TikTok was a terrible national security risk until we realized that it was so popular that we couldn't really stop it and you know certain political leaders realized it was to their advantage to embrace it, but deep. See there's still a lot of ambivalence about deep seek in the US. The US government is prohibiting, uh, government agencies from, from using it, uh, uh, and trying to discourage companies from using it, whereas I gather in Singapore there's none of that. There's, well, let's look at it and is it useful or not? Or perhaps I would put it another way. I think when Companies, developers, deployers, they think about what model they want to use. There is always an assessment of a couple of things. One of the things would be, of course, performance. The other thing that they, they would take into consideration is security and resilience. And it's up to every organization to evaluate all three and draw their own conclusions about what can be used and what cannot be used, although they do not wish to use. And I would imagine that there will still be concerns about deep sea and other models that would cause companies. needs to take a pause and say that maybe this is something that we are still not very comfortable with. I wouldn't be surprised at all. This is a very natural consequence of organizations having to weigh the cost and benefits as well as the many dimensions of what makes an innovation worth the effort. So the other thing that's related to this and that that's new over the past year is that you know we in the US we have a new president now. And relations between the US and China have gotten, uh, safe to say much more tense and in many ways more acrimonious. President Trump is imposing very large tariffs on China. China has been retaliating in kind. The US has tightened restrictions on selling chips and other technologies to China. I wonder whether you know and we talked last year about Singapore's strategy of being sort of non-aligned, uh, being a kind of a Switzerland of Asia, a space where both Chinese and and uh uh US companies and tech players could could operate together. And so my question is can Singapore still do that? And this also sort of applies to the rest of ASEAN. Can it remain strategically unaligned, or are the two giants trying to force small and medium sized economies like this one to pick a side. I think Singapore's consistent approach is to act. In a way that meets our own interests. And that is the starting point that has to be our guiding principle. Now We would certainly hope that relationships between the two giants can warm up to a much greater extent, but it's not something that we can wish to happen and it will happen. I wish that was, um, you know, something that uh we didn't have to deal with, but it's a reality. Um, the way in which we go about it is to engage, you know, uh, all countries, not just China and US. Um, and Build upon our bilateral foundations. And to try and make headway in new technology areas too. So with the US, we have a dialogue on continuing and contin critical and emerging technologies. Similarly with China, we have a digital policy dialogue. They cover different areas of interest um that um we mutually believe are important for our own countries. But, you know, it doesn't prevent us from seeking to understand each other's concerns better and then continuing to find ways to move forward. So in AI Assurance in AI governance, for example, with our US colleagues, we did a mapping of our own risk assessment model against us, and this serves as a basis for us to engage with colleagues elsewhere around the world, particularly those that are involved in the AI safety network. Um, with our Chinese colleagues, we noticed that um there are industrial Uh, foundation is so broad and deep that the application of AI would be very interesting to watch and learn from. So there are different ways in which we engage with our ASEAN countries, we say, for example, that um even if we are not ready to move into the era of standards in AI governance, there's nothing to prevent us from agreeing on what the ethical principles could look like first. And then with other countries um that are like us, small, um, we created a digital pillar in the forum of small states, which Singapore is the convener of. And with countries like Rwanda, for example, we created an AI playbook for small states. So these are the ways in which we are coming together for conversations around different aspects of AI hoping to meet, make progress. Interesting and, and, and thoughtful. Um, I wanna ask quickly if I can about um. Scaling the digital workforce to kind of support a lot of these initiatives. The last time we spoke, you described for us, the government's plan had a goal for training 5000 AI practitioners in Singapore, and you have since tripled that, as I understand it, to 15,000. Why the increase and how will you achieve it and what will be the background of these practitioners? Well, number one, I think there's a lot of interest, um. So many people in, in, in Singapore are aware of the potential benefits of AI and they are themselves finding ways to acquire the skills. Uh, I would say that, um, whether it is AI creators or AI practitioners, Uh, I'm overall optimistic about what can be achieved through our workforce if each and every one, were enabled to acquire some AI related skills. And you imagine that um certain basic AI skills will become as ubiquitous as you and I being very familiar with using emails, um, and perhaps in the earlier stages, Maybe it was not so common for people to be able to use um digital tools, you know, whether for word processing or making slides and so on. But over time, we all acquire a certain level of competence. But we think that it's not enough just to have basic level of competence. So what you described, the 5000 and 15,000 are what we call AI practitioners, we are hoping also to grow another pool to complement the data scientists and machine learning engineers. We're talking about people who who are in the professions, lawyers, accountants, doctors, who will become the early adopters of AI and then they show their peers how to make better use of it. We're also talking about people in different sectors, whether it's manufacturing, whether it is, um, you know, in healthcare or um in financial services. They themselves also acquire this facility with using AI and then they demonstrate how it can create more value for their organizations. This group of AI practitioners will have to exceed the 15,000 that I talked about to a much greater extent. So in the Singapore context, our workforce is maybe 3.5 million. I think. At the level of AI users that are AI, they are competent, you, you will need to get to a much larger number than what we were talking about last year. So watch this space, we will have more to say about this. We, we will watch it with great interest. We've got just a couple of minutes here. I wonder if we can come back to this initial point about assurance. Uh, it's very interesting to me because it seems that what Singapore is doing is it's trying to become a kind of a test bed for safety and responsible AI and And to create a kind of haven. And build a brand around that in much the way that Singapore has done with FinTech regulation and cybersecurity services. Is that, is that the idea and Maybe you could say a little bit more about the philosophy behind the emphasis on insurance. The broad idea is that this technology um can have many applications and you don't want to overly restrict its growth and development. On the other hand, for a technology that can be used in so many different contexts. You've got to watch that it doesn't cause harm because the moment it does, and people become very wary of seeing it being used in even more ways, then it's much harder to recover from that sort of loss of momentum. So the way in which we'd like to go about it is to set out the broad directions and also to set out what are the principles that we should be working along. And then to uh say to industry as well as organizations that are using AI, let's try and find a way to define the risks and also then to find ways of mitigating those risks. That's generally the approach that we take. Small steps, um, not necessarily always big bang, but when we're ready to move, then move quickly. It's always fascinating for us to kind of watch those small steps and to try to observe them and analyze them. I think that many of them are of great relevance and and benefit to other kind of medium sized states as to how they can navigate in. This really rapidly changing technology environment. We look forward to tuning in again and coming back for the next installment of this conversation we hope in years to come. Thank you so much, Minister, thank you so much. Thank you. Thank you.

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