Brainstorm AI Singapore 2025

DBS, Southeast Asia’s largest and most profitable bank, is betting big on artificial intelligence to define the next era of global banking. From algorithmic credit scoring and AI-enabled fraud detection to hyper-personalized customer engagement, the bank is embedding intelligence across its platforms to drive both efficiency and growth. With regional competition heating up and digital expectations rising, the challenge is no longer whether to adopt AI—but how to do so responsibly, securely, and at scale. CEO Tan Su Shan shares how the bank is navigating that challenge, what being AI-fueled means in practice, and how institutions across Asia can turn automation and analytics into real strategic advantage.

The Intelligence Dividend: How DBS Is Deploying AI To Reshape Regional Finance

Conferences

Artificial General Intelligence—the idea of machines that can think, reason, and learn across domains like humans—has long been a philosophical puzzle. Now, it’s becoming a technical one. From building scalable, real-world-ready systems to aligning AI with human values, builders at the frontier of AGI are facing complex design choices with far-reaching consequences. In this candid town hall, we bring together deep-tech founders, academic leaders, and provocative thinkers to explore what happens next. How close are we to scalable AGI? What are the national strategies, ethical fault lines, and cross-industry applications to watch? And what kind of future are we designing—whether we’re ready for it or not? Get your popcorn ready.

Town Hall – Beyond Human: AGI And The Future We’re Building

Artificial intelligence is transforming the foundations of global finance, accelerating innovation and challenging the way financial systems are built, regulated, and accessed. In this new landscape, the future of fintech must be global, inclusive, and collaborative by design. From real-time credit scoring for the unbanked to AI-driven regulatory sandboxes, the true promise of fintech lies not just in what it can build—but in who it can reach. How can we ensure fintech becomes a force for equity rather than division? And what steps can we take to invest in interoperable systems, embrace distributed intelligence, and embed ethics at the core of AI-driven finance?  

The Infrastructure Of Inclusion: AI, Fintech, And The Power Of Global Collaboration

We're going to turn to a conversation about how AI and global collaboration are reshaping the finance industry to be more inclusive and resilient. From expanding access to credit to rethinking regulation, we want to know more about building fintech systems that serve people, not just profits. Please welcome Global Finance and Technology Network co-founder and group CEO Satanhu Mohanty and Fortune's Clay Chandler. Thanks Andrew, um. So it's a real privilege to have Fernando here with us this afternoon. This is a man with a rare career and and a remarkable set of insights into how the world is organizing, you know, when we talk about AI and technology, we often are talking about uh developed economies. We're talking about Silicon Valley, we're talking about what's going on uh in uh in China, but there's a whole big world out there. There and a lot of people don't have access to this technology and I want to talk a little bit to Subundo uh about that but first I wonder if we could uh just have you explain a little bit about uh the global fintech uh network. This is for those of you who don't know who spent many years in the monetary Authority of Singapore and then it was uh in charge of uh Fintech for the central bank at Singapore and then. Uh, very entrepreneurial, helped the central bank develop this entity called Elivandi. Which launched the Singapore Fintech Festival. This is, I would say, probably the most successful Fintech festival in the world. It's been going on, how many years now? 10th annual. So 60,000 people descend on Singapore from all over the world for this festival and discuss every aspect. We went last year and we're thrilled to be a part of that, but it's it's. It's blockchain. It's talking about, you know, different forms of finance, but it's also made a real effort to look into the developing world, you know, the global South, and how FinTech can help people that are unbanked, gain greater access to financial markets. So I hope we can talk a little bit about that, but talk to us about uh. You know, the, the global fintech network and what it's evolved to be because it has 4 different functions, right? Oh yeah. Well, um, it's kind of an extension of my last 10 years in MAS because we are, uh, If you take the last 10 years in Singapore, we built a fintech hub, uh, which kind of expanded beyond the Singapore mandate and we went to the global ecosystem. We influenced a lot of the choices financial sector are making. So we decided that to really super scale that idea, uh, MS created this GFTN. Um, and, and the, and the core idea behind this is to create this global network of things what we are doing here, primary information, right, talking to each other at a scale so that we can capture around the world conversation around. The innovation and financial services. Not in isolation, but in partnership with the regulators, investors, founders, fintech CEOs and all. And to do at a scale is the first mandate we thought, uh, do as part of GFTN. Which is an Eliandi extension. Then we added, OK, now we have done this. Let's add the advisory piece. How do we advise low income, middle income, emerging market on a strategic policy, uh, for innovation and financial services, uh, almost replicating the Singapore success in many emerging markets. And then we bring the platforms, the public good platforms to the market, create a more connected equitable system around the world. And then we thought, OK, now we've done the first three pieces. Let's put some venture money to boost innovation to upcoming startups. This is a comprehensive ecosystem building framework. So this is really a unique entity that's part think tank, part advisor, part investor, part investor, um. And has this tremendous global reach. Now the uh Syntech festival here extremely successful, but you've extended it. You now have a version in Japan and you've also brought a version to Kigali, is that right? Tell us about that and why you've done that. Well, we wanted to take the narrative to the emerging market because it Good to have a program in Singapore or in Hong Kong or in Tokyo, but how do we take this program at this scale to emerging markets, to Africa, to Latin America, right, to deep in ASEAN, our desire is to take this conversation to every hotspot in the world because we know the future of financial services are not going to be. Created out of developed market, it will come from emerging markets. It is going to come from low income countries. They're going to redefine the future of finance with better innovation, better technology. We got to go there and have this conversation. That drove that whole network build up. So tell us where do you see some of the greatest, you know, opportunities for bringing AI and FinTech to uh empower uh the developing world. I mean, is it in India? Is it in Africa? Well, where are the kind of urgent opportunities? Well, I'm going to be a little more uh provocative here, OK, today, if you take every history of technology, we have seen different tech stack coming and solving certain piece of financial services. Now, financial sector. Historically, is the biggest paymaster for technology. Even today, look at the AI revenue breakdown. Almost 40% or 50% goes to the financial sector, but we are, but when we see our, our design, we are archaic. We are not the best. Tech tech tech stack in the world, right? So we pay the bill, but we get the bad technology. So where is the issue? The core to the issue of, of AI or any technology solving financial sector challenges is there's an elephant in the room. It is the trade market now. If you, if you say to somebody, I'm a bank, one expectation is they lend you money. Yeah. Now, when you are borrowing money from a bank, the banks ask for you from your collateral, right? Right. That's the standard process of lending. That model cannot be solving the global need for credit, right? The financial inclusion challenge is the lack of banks' ability to extend credit to low income segment, right? The low income segment has no collateral, right? What they have behavioral data, right, intend to pay back, right? They can if you give them money, they will do something about it and they'll pay you back. To me, that's the. Problem AI should solve for create credible, predictive golden source behavior data which will replace the need for a collateral for a bank to lend. If you don't solve the problem, AI is still solving the classic bank problem which is make our productivity go up. We can fire hundreds of people. I think that's not what we want AI to do. We want AI to solve the foundational credit challenge banks have. That's very interesting. So when you, when you talk about, uh, you know, solving. I mean there are two ways to see the behavioral data and I've talked to companies that are kind of viewing it both ways, but there's a set of companies who are saying, oh look, this is just about risk analysis. If we know that we're gonna get paid back our risk is 0% the lending to low income. Uh, uh, you know, uh, people, then it means that we don't have to worry about it. We can set a fair rate and, and we can lend it, but there's another category of people that I've talked to that say actually we want that behavioral data because it's valuable to us. We can use it to make a bunch of predictions. Uh, we can use it to kind of gain people's behavior. We can use it to sell it to other people. So I mean, do, are you advocating both of those things or just but I think, I think that's less of a problem today. If you ask a credit officer, they want to establish two things ability to pay, uh, intend to pay in, OK. Collaterals gives the ability to pay matrices, behavioral data gives the intent to pay matrices because the way we lend today, you go back to the default side, the non-profitting loan, all our intent, yeah, largely intent, right? So we are denying people because we don't understand the intent and at the same time we, we, we, we give people money and so that they have a bad intent later on. Now the gaming behavior comes. Also for the same segment of people who have collateral, you want them to use more of the collateral to borrow more, but banks will never lend to people who have no collateral. So my sense is that there's a two sets of problems. The bad use of data, I think we can't control because regulators have enough protections in place, but what regulators cannot. Is the bank's inability to lend to people who don't have collateral that has to come from technology that has to come from a different set of business models which I hope this AI revolution can get us there. It's really interesting. So to anchor this evaluation of behavior though, you've also got to be able to establish some firm. Identity matrix for the uh uh you know, the borrowers and, and, and consumers in, in low income countries. Now I know this because I spent a spent a fair amount of time in India and I watched Nandan Neilani build the, the Adhar biometric identity system and It's fascinating because I had several conversations with Naan about this, and he explained to me that look, you're all hung up in the developed markets about privacy. You want to protect your privacy, but you don't understand that if you're somebody out of a village, uh, that's come into the big city looking for a job in India. You don't care about privacy as much as you care about getting your identity on the grid so that you can be paid, so that you can receive government benefits, so you don't have to deal with all these middlemen and and money change money changers, uh, to get what's, what do you. Africa must be the same, right? How do we Well, if you define the last 10 years of financial sector, I say DPI, digital public infrastructure, API, how to abstract complex system and connect to third party value providers, and then comes the front end user experience, apps, the super apps, that's how defined the last 10 years, the code to. FinTech revolution was the digital public infrastructure which people like London design which took the burden of identity from corporates individuals to the state. Now the question is, do many countries trust that state to hold the identity? So the first single biggest friction to replicate the India success is the. for a state to hold the data of identity and determining your your transaction at a national infrastructure, state infrastructure sovereign level. But what is going to happen for the next 10 years is an interesting shift with the coming of the tokenization and the blockchains and the digital asset piece, we are, we are decentralizing ideas now. I think if there is a there's a mechanism of of able to take the text stack of tokenization and the ethereums of the world and create a decentralized ID on those networks, you don't need a state to control and deliver that trusted ID. So there is a hope 10 years from in the next decade to shift identity from a single single state controlled mechanism to a decentralized design, you know, in addition. Another big problem for for people from these uh emerging markets is is cross border remittances, moving money, right? They're not they they don't have bank accounts. It's not sort of simple, uh, to do, and especially when it involves going across border very challenges challenging lots of people kind of get in the middle of that and extract fees. You've had some recent success just for working with that problem. You wanna talk a little bit. I think this is the first time globally yesterday. Somebody from Singapore can use pay now, right? Go to a Fintech app, but we have now a thresh called Liquid Pay or one of them. Send money to India with a UPI ID from here with no bank detail to be to be no banks in the middle of that in the system in the user perspective, put the money put the UPI ID in a fraction of a second money goes to other side. Of course, the bank rails are behind, but the consumer experience able to send the money at a full price transparency. You know exactly what you're getting the other side with the price being displayed with a very attractive. Cost of transaction. The target is to make it below 3%, including FX, yeah, so you can send the money. It took us 3 years to build it, and it requires a great amount of regulatory policy synchronized, yeah, yes, and that that is now going to be the global template of how to connect cross-border payment across different banks using national infrastructure with regulatory policy synchron. Multiple jurisdictions Project Nexus, which BIS was heading in, it's actually going to happen in Asia. 5 Asian countries have come together, are taking this model of India, Singapore, India, Malaysia, and Singapore, Malaysia, Singapore, Thailand and creating a multilateral connectivity architecture called Project Nexus which will go live hopefully next year to do replicate at a multilateral level this whole experience of connecting. National IDs or identity reflected to different IDs to send money to each other without the complexity of banking systems. That's a huge issue. I mean, particularly within this region, right, especially emerging low income migrant workers sending money back home, right? They can now send small ticket size money without without a huge cost on the top of those transfers. Wow. So, uh, what's next for you in terms of priorities then? My biggest. Priority now is going to be upskilling. One of the single biggest challenge in our economy will be lack of job right. I think killing for the future is is an acute crisis now with AI coming, the number of jobs in the market is going to dramatically go down. If we don't upskill our people, I think we are heading to a massive economic disaster. It is already real in many countries, unofficially. 2 out of 32 out of 3 don't have a job or will not have a job, and especially for a young workforce, there's a very little hope for a job in the job market now. We got to get it right. So upskilling for the future will be my single. Biggest priority for the next 2 or 3 years. People often say AI is a very democratizing technology, but it's only democratizing if you've got skills and and I think AI can be a great tooling for our new future upskilling designs. That's fascinating. So, uh, the next, uh, FinTech festival is in November. Yes, and it's the 10th anniversary. We're going to celebrate last 10 years of super success. We're going to talk about the future 1010 years of possibility. I strongly encourage your audience to come and join us at Singapore 10th anniversary. It's very exciting. Thank you for joining us and sharing you. We really appreciate it.

The future of autonomous vehicles is here. From Qualcomm’s cutting-edge chips powering next-gen perception to WeRide’s real-world deployments across urban Asia, AI is transforming not just how we drive, but how mobility is imagined, regulated, and scaled. We unpack the breakthroughs and bottlenecks in hardware, data, safety, and trust that will define whether robotaxis become everyday reality or remain stuck in the slow lane.

The Reality Of Robotaxis

Autonomous vehicles have promised a future of convenience, but trust, policy, and performance are still major factors in how mobility is regulated and scaled. Here to discuss the reality of RoboTaxis, please welcome We Ride General Manager of Singapore, Dr. Kerry Xu, Qualcomm Technologies vice president and Q Qualcomm president of Taiwan, Southeast Asia, Australia, and New Zealand, ST Liu, and Fortune Andrew Neska. How fun was that session, right? Um, OK, we're here to talk about autonomous vehicles, robotaxis, if I may, um, so let's level set as I often like to do, um, Carrie, I'll start with you. Where are robotaxis specifically in the region? Where are we seeing them in city streets and where have they not reached yet? OK, um. Uh, I think, uh, a lot of people are already familiar with, uh, like what's happening in the US. So I focused on Asia and just, um, uh, actually I think China is uh really seeing a large scale the robo taxi deployment, uh, in Beijing, Guangzhou, uh, like we ride with in this year at February, we just launched our robo taxi fire charging commercial operations in Beijing. And we now have like more than 50 of the robotaxis providing service and in Guangzhou, same thing, we already launched our commercial charging robo taxi service and most of you may also know Baidu, I think they launched the robotaxi in Wuhan with like 100 of the vehicles. And the second, uh, hot field, uh, in Asia, I think it's Middle East, um, a lot of the you maybe all the red news we are teaming up with Uber to launch our robotaxi actually in Abu Dhabi, Dubai, and in fact we already started the driverless robo taxi test in UAE right now. Wow, that's quite, I mean just thinking about all those cities, they are dramatically different. Urban landscapes and I can't even imagine the technological challenges, but that's precisely what we're going to talk about today. Um, ST, the tech is advancing rapidly. And yet there is so much we can't do. So, again, tell us what's possible, tell us what's on the cusp of changing as it pertains to autonomy. Sure. Uh, so our company comes from uh Years of innovations and creativity in coming up with a computing efficient, very efficient computing. Uh, devices and solutions and from there also with very efficient power consumption, uh, intelligence on the edge and all that. And as the automotive progresses, the need for intelligent processing, very, very high power processing at a very efficient power consumption has become more and more important. Now using that legacy of knowledge and experience over the 40. Years or so, um, we have put together a solution, what we call like Snapdragon chassis, which, which is a solution combining all the different platforms that you would need to make a AD you know automotive driving car, autonomous driving car or AAS or anything that you need to make driving or not to drive such car very comfortable infotainment, telematics and all that. Our role is to provide such a solution so that our customers can put them into products relatively faster and then as the need for autonomous driving cars become more and more important, especially in Southeast Asia or Asia where the driving condition, the weather condition is very, very different, that solution is important because, you know, ouan will bring. And encompassing that solutions, these knowledge and then on top of that allow our OEM to expand onto it, you, you know, especially with AI become available and all that, yeah, so obviously there's a lot of technology that goes into in an autonomous vehicle, right there's the power train, whether it's electric or otherwise they're almost uniformly electric, um, there's. The sensors themselves, which are relatively straightforward affair. One thing I've noticed is, as you said, you know, the kind of ever evolving intelligence of it all perfect for this conference here, you know, it has been clear in Qualcomm's earnings reports, for example, that the company is very bullish on. Artificial intelligence moving to the edge, right, moving to these out of the data centers that we love to talk about at AI conferences like this and into these other places like cars and whatever where the power, as you said, the power and the efficiency and the size, it's all a different consideration set. How is that changing the autonomous market? Well, A lot of this decision making, the decision making process and the speed of this decision making needs to happen locally. You know, latency is extremely important. So intelligence at the at the edge is a very pivotal feature. Uh it's, it's, you know, you, you imagine driving down the road, you know, you have to make decisions very, very quickly instead of going out to the car and come back in again. That's a very critical and a very typical example of intelligence at the edge. That's that's where our forte is, you know, AI at the edge, and that is, uh, that is bringing a lot of benefits from multiple perspectives, latencies one. Um, it also helped to bolster, uh, what do you call it, the protection of information data and so on and so forth. And so that's how it is changing and, and, and with AI coming on board, the, the process of learning, the, the deductions, decision making is becoming more automated and it allows a lot of developers to come up with applications that you can actually use inside the car. So autonomous driving car is not just a car that by itself, but there are lots of things you can do inside the car which bolster the drivers or the passengers' confidence sitting in the car and all that. Yeah, and to do some of that compute um locally is candidly necessary. I live in Los Angeles. There is a mountain range that cuts through the center of LA, uh, in many of the, in many places in that mountain range, there is no cellular service. I could not imagine the car just deciding, ah, we're gonna just give up, you know, mid-drive, um. I want to come back to you, Carrie, um, what are you learning as your company deploys in these different markets? As I said earlier, very different urban landscapes, very different challenges, we mentioned weather earlier, there's the landscape itself, um. There's obviously, uh, uh, uh, you know, kind of, uh, the expectations of the drivers in a given market as well. What are you learning as your company expands? Yeah, so so far we have been deployed our erasing more than 30 cities across 10 countries. We do learn a lot of uh lessons. But I want to emphasize the three lessons we learned. The first is that we, I think we have to work closely with the government and the regulators. We monitor the regulations to make sure we actually conform to the local traffic laws and the regulations. And uh in this part we actually uh hosted many delegations from different countries um like in the a few weeks ago, I think Singapore's acting Minister of Transport Jeffy, he actually led a delegate visited our HQ in Guangzhou and At our at there, he actually announced in the next 5 years, Singapore is going to see many, many AVs in the uh on the streets and they are actually planning to integrate the Aways into the uh public transport system. So I think the government support. You know, working closely with the government is one of the things when you deploy in a different country, when no matter which country you go, the first thing is that you should work closely with the government on regulation. It's an age old technology question, but we've articulated all of the technological, technical challenges of of autonomous driving, but Given what you've just said, in your opinion, do you think the greater hurdle to full deployment are the humans or the tech? Um, from my point of view, I think technology is actually mature enough. Uh, we have seen the like operations of way more in San Francisco, Los Angeles, and like uh we ride, we are operating driverless, no safety operator on board in Beijing, Guangzhou, you know, Abu Dhabi, Dubai, so technology wise. I think it's clearly mature enough to scale, but we have to work with the government on the regulation part, public acceptance, data transparency, all this. I think it takes time to for the community to completely accept the AV as like a part of their normal life. I think it will be. There, but it's just step by step taking some time to achieve it. Everybody remembers their first autonomous ride once you do it, you know, um, SD, I'm gonna, uh, come back to you. You work with many different companies and many different geographies. Uh, same question. Do you feel like the technology is what's, uh, preventing full deployment of these things, or is it in fact the humans, the policy, the. You know, I'm gonna answer this way, we always advocate that um uh that we, we always should have a cross-indust industry benchmark transparency in the training data and uh make sure that we are compliant. To safety, to, uh, to policies, etc. etc. I think those are the things that are built into our architecture. And by doing so, uh, there's you, you build trust in people, you have to build trust in passengers, in policymakers, in general public, etc. etc. So that's part of the importance of building that into the ecosystem and it's not just a technology thing, it is a private sector having to work with the government and uh you know, work with the cities and education and so and so forth, and it comes from both the Tech technological solution for us, it comes in the form of the chip set, the SOCs and the and the software stack that our OEM partners can, you know, choose and, and, and, and, and build into their cars. But at the same time, the build the confidence, I earlier mentioned the things that you can, you can do in the car, significant increase. So take care of the safety, make sure that it is transparent and then you can enjoy yourself while you are in the car, can build up that kind of Acceptability to this whole thing, but I, you know, safety is extremely important, so we have to benchmark, we have to be transparent and so on and so forth. Yeah, yeah, and I'm hearing the word trust quite a bit at this event, so I, I understand. I have one more question for you, but I just want to tell the audience, hey, we're gonna come to you for questions next, so, uh, get ready, um, all right, so. I see you, your organization works with virtually every automaker everywhere and you're of course, you know, ultimately an American company, a global company, um, and you're seeing uh massive differences across those markets in some regard. um I'm curious how you're, you know, you're supplying these, uh, automakers with this technology and working with them to integrate it in the way that it happens. Technology has been incredibly disruptive to the automotive industry. Just speaking from the US perspective, um, Detroit has built a car a certain way for many, many, many years, and it was very, very good at that. And then Silicon Valley shattered it, just totally reversed it, you know what I'm talking about, just completely just rethought the way you would put together, uh, honestly an end point is the way you might think of it in technology, right? ST as this stuff sweeps through, are you finding with your automaker partners a difference in the way they build their vehicles, the way they incorporate this technology, and by the way, is that different in, say, China or Southeast Asia versus the US? I think the big shift to the automotive is one term that you have heard a lot about is like the software defined car. I think universally I can, I can say that all the automakers. Uh, coming to the realization that this is one area that uh everybody needs to get master in. In the past, you look at power trend, you look at the car design, you look at, you know, the things that uh automotive auto automakers will look at. Now your conversation and your considerations uh Kind of get changed a little bit and say, what is your experience when you get into the car and that's where a lot of the differentiations will be brought up as well. And so if I go into the car and then I can conduct my meetings, I can use AI to ask where I am, book my restaurants and all that, just like an extension of my office or my home. I think they will make wonders and do wonders for a particular OEM or any OEM that will, you know, that's one of the ways now to differentiate the vehicles and then on top of that, no one market, no one country, no one geography is the same. So we must enable the continuous learning and with AI being available then now you do the continuous. Learning, use the smart way to factor all these things into the solutions. I think that is the way I would answer these questions. I think the opportunity is there for everyone and I can see very clearly the realization that a software defined car is is is is that is very much with us today, so I just want to correct one thing that you said. You're not going to be going into a self-driving car and booking your meetings. We have AI agents for that. Yeah, yeah, yeah, yeah, we have AI AI that's right. We solve that right here at this event. OK, let's go to the audience for questions about autonomous vehicles and robo taxis taxis. Yes, we have one right here. We just wait for the mic to reach you, and when it does, your name and who you're with. Thank you. Thanks. Hi, uh, my name is Firoz. I'm with Syngenta. Uh, my question is, how important is the infrastructure outside the car? Quality of roads, lane markings, curb, behavior of other drivers on the road, uh, for, for it to be safe. Gentlemen, which one do you want to take that, OK? Uh, yeah, I think that's a very good question. So I think earlier Andrew mentioned is the vehicle itself must be intelligent because, uh, we put the like brains in the vehicle, but Infrastructure is also important. For example, uh, network connection is like 5G, uh, a lot of this, uh, autonomous vehicles right now still have like remote monitoring in case the vehicle stuck or in certain. Conditions, the people who are monitoring remotely can still give a hint or help the vehicle. Also, in Singapore, we are already working with the government on like how to design the pickup job point to be friendly to Aways. And how you uh monitor all the different uh pickup points to uh be able to uh see where where the vehicles, all these kinds of uh infrastructure, even the how to educate the people to better interact with the AVs. I think this is something a lot of government. Actually already start looking at and I have worked with some scholars whose study topic is how to make our infrastructure friendly to uh driverless vehicles. I think this is something is going to be happening in the next uh maybe a few years. Yeah. Very good. All right. Another question. Yes, we have one over here, right here. Just wait a moment again, your name and who you're with. My name is Selvin. I'm with Alliance Asia. Just have a question. Do you think that LIDA technologies is necessary to make autonomous vehicles more safe? Is light are necessary to make it. Yeah, um, I think, uh, from my point of view, I think, uh, LEA is very necessary. Uh, in fact, is, uh, my, my, my idea is the reason some companies don't install the ladder is probably some a few years ago, ladder is still pretty expensive, uh, sensors to install which increases the cost for the vehicles. However, if you look at the edge case, so the vehicles are different from like uh some other like your cellphone. Uh, tolerance is different for vehicles. We are not looking at 99% safety. We're probably looking at 99.9999 safety. We don't, we can't afford the accident due to age case. You cannot catch it. So cameras has uh vision based has some limitations like in the very low light environment like midnight, no light, but lighter. You know, it's uh it's emitted the light, so it doesn't rely on any lights and also the lighter can accurately detect the shape of the obstacles which also helps the algorithm to more advance the control and planning algorithm to bypass or to avoid the obstacles. So with the lighter price is cheaper and cheaper, I think. Uh, like us, we are doing is we do the fusion between the camera and the ladder. So all this allows us to detect all the obstacles in like 200 m, 300 degrees, very accurately to do the planning and controlling. So yeah, that's how I believe the ladar is still very necessary. If I just add to the conversation about the infrastructure around the vehicles. I think one of the key things going into the future is. So the cellular V to X, cellular equal to any everything. And then if you think about the future of autonomous driving, uh, CV to X is something that we in Qualcomm, uh, continue to, uh, to invest a lot in. And to your point of light, when you have CV to X, obviously you can plan ahead on many, many things and and so and so forth. So I think, uh, to the questions about the road marks and things, CV. To ask the road maps and all these, just point to the fact that we need to work very closely with infrastructure partners as well as ecosystem partners and all that. Yeah, in terms of the infrastructure, I have a very good example. I keep telling my friends is that right now in the car park in certain areas we install mirror for the help drivers to look at the blind spot, but in the future for avis actually we actually can install. Those B2X equipment to help the AVs to look at the black spots. I think these are all the future probably development for the infrastructure to be AV friendly. But it is unquestionable that our our urban landscapes are going to change dramatically because of of the rise of robotaxis and autonomous vehicles. So gentlemen, thank you so much for an enlightening conversation, a round of applause please for our panelists. Thank you. Thank you.

Kyogu Lee, Co-founder and President of Supertone, is reimagining voice for the age of artificial creativity. He reveals how Supertone’s ethical, AI-powered tools are amplifying artistry and reshaping the future of sound. From multilingual vocals to real-time vocal transformation, his work is opening new possibilities for creators in music, film, and more. Featuring a live tech demo, we'll explore how AI can amplify human expression without replacing it.

Voicing The Future

OK No. So thanks everyone for joining my debut as a K-pop star. There will be autographs after this session, but in all seriousness, you know, um. In all seriousness, thanks to Supertone, I was able to be part of this duet with Cindy, a group made of AI-based voice characters. I'm excited to be here with Supertone co-founder and president Ko Go Lee. Artists around the world are using AI to expand their narrative and creative possibilities rather than fearing it will replace them. Singers are working to actually co-create. with voice AI tools and we're going to take an in-depth look at some of the real world tools that are changing creativity and artistry. So let's get started, you know, I want to start by asking like what you guys actually did. So for context, I gave Superone three videos of me singing in my hotel room, Call Me Maybe by Carly Rae Jepsen. Uh, I heard it through the Grapevine by Marvin Gaye, Pink Houses by John Mellencamp. I gave Supertone these videos and you know, voila, thanks to AI, what you heard was the result. But what actually is Supertone doing with with these voices? OK, so let, let me first uh ask you a couple of questions. Uh, do you speak Korean? No. OK. Are you a good singer? I don't think so. OK. So, uh, and that's what we achieved at Superone. So let me tell you, uh, so we developed a foundational model for voice generation and you can think of it as a larger language model like KGBT or loud, but for voice. So, our model can generate incredibly natural and expressive voices, including singing voices using inputs uh such as uh a speech, singing voice, text or melodies. And in this demo we uh transformed the original singer's performance into your voice. We call this uh thing with convergence. So unfortunately we didn't use any of your videos. We just took the audio of your recordings and to add more detail so our model can. Uh, take as an input any speech or sy signal and break it into four different attributes. The first one is a pitch and the loudness, I can whisper, I can speak louder, right? And the timbre, which reflects most of your or anyone's the vocal identity and linguistic content. Of course, it depends on what kind of language you are speaking. So we have two recordings, right, original singer performance and your recordings. So we take them as inputs and we break them into these four attributes I just I just mentioned and we took. Uh, the, uh, the timbre part from your recording and the rest the three, the lingu consonants pitch and loudies from the original performance, and you can mix them together to reconstruct the singing voice which is in your voice. That's what happened, and, and there's, there are some other things that your that that your AI can do and you're gonna show us actually now kind of what what your programs can do to someone's voice. OK, so, uh, imagine you're an artist. And working on your next album. And for this new album, you want something different, you want something new, uh, totally different from your previous albums. So you have, uh, you are ready to explore this bold new idea, but your natural voice doesn't quite match well with your artistic vision. So with this uh voice technology. Uh, we can instantly, uh, transform your voice into other musical personas to bringing new, uh, different musical identities to life. So, uh, let me show you how it works right here live because, uh, listening is believing. So when I speak, Like this The energy does a shit, doesn't it? So you can feel a different kind of presence, a calm, yet very powerful. Now, notice how capital my voice changes to the feminine to. It's much lighter, brighter. And carries a complete uh different kind of energy in the field. I can even sing very high notes that I can with my natural voice. So you know, I, the, the clip we showed earlier was, was me and a duet with the virtual group Cindy H. it's a fully AI generated virtual group, you know, what does it mean to have a fully AI generated pop group? What does it actually mean in terms of how they perform? What music they're putting out. What does this project actually mean? OK, so let me explain. So this product, it was a very bold experiment uh from our side as well. So we started two years ago and we wanted to showcase our like a voice technology, how we can uh empower the, the creators or, or the world uh to unlock their creative potential. So we formed uh this kind of virtual group. And uh which is composed of four female singers. So let's, uh, I mean, assume that you are a producer. So you have this idea of this virtual uh girl group and we have different uh personas, uh, four of them. And uh we have songwriters, we have some music, and we have a production engineers and everything. So you have the songs and everything is ready. So if you're a producer, what would you do next? What, what's gonna be your next step to actually produce music? I mean, they, I mean, you have to let them sing and record the performances and Uh, in, in, in the legacy, uh, media production, you need to, I mean, resort to human, uh, Personnel, I mean, the singers, artists, right? And you need to uh find uh the very good artist that matches your artistic vision. And we, I mean, usually do an audition or casting uh to accomplish that, but in this case, we designed a unique voice, voices for uh each of them. So our technology can uh design or create uh theoretically unlimited number of unique voices that Or never heard before. So for this uh 4 members, we created their voices uniquely and using our singing voice synthesis, uh, model, we put the melodies and lyrics and let the models sing. So with all of these AI models that that can generate kind of these new voices, they are always very impressive, um, they are very impressive about what they can do. They're also a little bit, you know, spooky at times, you know, and there have been, there have been debates about, you know, about voices sounding like celebrities and celebr. being upset about that, but you know how is Supertone navigating this? What, what are the kind of the, the rules of the road that need to be set down involving, you know, voices who owns them, the voices you generate from them? Are you thinking about these ethical questions? Yes, this is a very good question. So, uh, the respect and consent, uh, for the audience rights must always come first. So a Superone. We, uh, we have very clear and transparent licensing frameworks and the uh license approved, uh, the, the uses, uh, from the, the artists or creators themselves. So we never use their vocal identity without explicit and informed consent. That's how we uh build trust between the the the. The artists and the creators, so they, that's why how we can, I mean, uh, accomplish this uh past projects over the past few years, you know, Korea's cultural industry is very strong, you know, K-pop, K-dramas, K music. Um, and you know you're operating K-pop space. You work with hive, you know, what's the interest in Korea for embracing these new AI tools? Are they excited to adopt them? Is there some hesitance? What's the mood like in Korea about? Well, it's, it's, it's both, I mean, like every other country, I mean some people see it as a huge potential for the content creation. New ways of uh exploring the fresh ideas and everything, but the other parties, especially the uh the artists and the creators themselves, uh, some of them uh look at this technology as a very uh threatening because it could um replace the, the human artist, but I don't agree with that because I mean. Uh, it's tool. I mean we, we see the creators and artists as co-creators or collaborators, uh, which means that in, uh, they, uh, we collaborate with them not only in some specific project. But also when we uh develop and refine the technologies so that uh we can reflect their feedback and concerns to better shape our products and uh and the, the tools. You know, I was thinking about, you know, technology and music and the idea of virtual bands. I mean, it's not a new idea. Guerrillas in 2005 is a virtual band. Japan has vocaloid software where you can use to generate songs. Japan also has, you know, their own virtual pop stars. Yes, Avatars in front of a human performer, but still, I mean, there the persona is, is artificial. Um, but this seems to be kind of a step beyond that. Where do you see entertainment developing, whether it's in terms of AI assisted artists or even wholly AI generated artists, where do you see entertainment developing in the next, you know, 5, even 10 years? Well, one thing for sure that uh this before we say that technology along with the other technologies. Uh, it will, uh, democratize the creative process for sure. So the, uh, look at every creator or artist can uh expand with new technology to uh explore and experiment with new ideas. I mean, it can empower, empower them to, uh, I mean, yeah, uh. Expand their boundaries and deepen the connection with their fans and collaborate with other artists as well. So I think it's a uh uh both a an opportunity for the creators and the and the uh uh so that they can, I mean, uh engage uh with audience. More immersively and more interactively and, and how do you see Superton's journey over the next 5 to 10 years? You know, Entertainment will change and there will be more brace these AI tools and artists. How do you want Supertone and its journey to change over the next 5, 10 years? Well, so. We, uh, as I said, over the past years, we have, uh, collaborated with many artists, producers, directors, writers, and all kinds of creators, and they help us, uh, define and reshape our tools. And they are also uh affected by the tools and the technology we have developed so they can try new experiments and everything. So I mean, as, as, as with all the technologies we have, uh, witnessed, uh, so far, I think the AI is is another yet very powerful tool for co-creation and collaboration. So that's where our super uh what I mean, has been. Having and this will be the way that we can still uh explore with the the creators and and and and so when you do this, this kind of voice work I mean who ends up who ends up owning the final product, um, you know, is it, is it the person who contributed their voice is it Supertone kind of how does the. Either like who owns it and how do you think like these thoughts about IP need to change with more AI systems? Yeah, it's like a conventional music production if I uh take an example for music because I mean in music there are many uh right holders, right? The the production company and the Uh, the agencies and the and the singers were performance as well. It's, it's same as this. So it's like, uh, I mean, if we uh contribute to a singing voices uh by uh uh by obtaining the rights for the uh the voice owners, then, uh, the, the singing part belongs to this voice owner or artist or whoever. Um, you know, maybe to help kind of close things up. I mean, obviously this, this works in Korean, it works in the K-pop genre, but you know what's a music genre that you might want to try next? If you could, if you could spend time to get this AI model right, what's another genre of music that you would really want to test this with new genre or existing genre? Well, I'm as a, as a huge fan of the, the 70s, 80s wrong in metal band and yeah, uh, rest in peace of Osborne who passed away. Yeah, so yeah, that's because I mean it's, it's very technically challenging and the growling sound and everything, the vocal idity, that's very challenging. So that's, I mean, as a technology provider I like to, that's where I like to try and experiment with my, I mean our technology. Do your, do your models yet work with other languages other than Korean? Oh yeah, sure, yeah, it can work I mean in any. Well, I think this has been a great conversation. It was really great to, to see this work for myself again to see, to see my voice change into this K-pop song. Um, it really was a remarkable experience. I really want to thank you, Kyogu and Supertone team for walking us through this and showing us how this all works. Please thank Kogu and the Supertone team for their work. Thank you very much. So yeah.

Large language models may be global in reach—but that doesn’t mean they’re global in understanding. While these systems are now used by billions across languages and continents, they continue to struggle with underrepresented languages, cultural nuance, and historical context. Whose voices are encoded in AI—and whose are left out? Hear from leaders in national policy, frontier research, and responsible AI development as they explore what it takes to build models that reflect the full spectrum of human experience—and what’s at stake if we don’t. 

Whose Language Is It Anyway? Equity, Empathy and AI’s Linguistic Blind Spots

As artificial intelligence reshapes the foundations of innovation, how we design, fund, and govern technology has never mattered more. We’ll talk with a policy strategist, a systems thinker, and a market-focused investor to explore how our collective choices can shape AI not just for scale—but for human flourishing. From institutional trust and design frameworks to capital flows and societal risk, we'll unpack what it means to invest in both human and machine intelligence in ways that are ethical, adaptive, and imaginative. At the crux of this conversation is creativity itself: how can generative AI unlock new forms of expression, co-creation, and cultural value—and what it will take to fund and govern those possibilities responsibly?

Intelligence by Design: Aligning Policy, Capital, and Creativity in the Age of AI

Nils Pihl, founder and CEO of Auki Labs, is developing technology that lets robots navigate the physical world on their own—without human input or cloud-based maps. Together with Terri, the humanoid robot, he’ll demonstrate the posemesh protocol, a system that gives machines a shared, real-time understanding of their surroundings. It’s a step toward a future where robots can move through space with the awareness and coordination we expect from people.

The Robot Will See You Now

Hello, hello, good afternoon. Welcome back. Thank you, thank you. Welcome back, everyone. I hope you had a great lunch and even better conversations. I certainly enjoyed mine. To kick off our afternoon, we're going to start by exploring the next frontier of robotics. Oki Labs is developing tech to help robots navigate the physical world on their own without human input or maps. Here to demonstrate, please welcome Aki Labs founder and CEO Neils Peel and his co-presenter, Terry, the robot. All right, hello everyone. My name is Nils. This is Terry. Terry is a unitary robot made in Hangzhou, and today we are going to talk a little bit about how to make the physical world AI accessible, and that's not just about the ramp over there. It has a lot to do with machine perception. I believe that over the next 10 years or so, we are going to see civilization grow to over 100 billion productive participants, most of them AI. And as a result of this growth, I think we will see the world economy grow by something like 10X in the next 20 years. In fact, that may be conservative. Uh, I was born in 1986, let's say 20 years ago, and since then, the world economy has grown 10X. Uh, the world economy has grown 3x in the last 20 years or so, so you only have to believe that we will move about 3 times as fast as we have been to do a 10X in the next 10 years. And Behind the growth that we saw over the last 20 years was that software ate the white collar world. If you are a white collar worker today, you are definitely working on a computer, you're definitely working on a phone, you are definitely using software. Software is a big industry. But 70% of the world economy is still tied to physical space and physical locations, and I actually believe this is going to increase. So What happens next? I believe that faced with the realization that most of human productivity, but also the things that bother us in the world are still tied to physical locations, we can't say that the AI revolution has truly begun. Until Terry can get a better job than being an intern. So let's talk a little bit about what needs to happen for Terry to become productive. We must make the physical world accessible to artificial intelligence. Today, out of the box, Terry is blind. He has a camera, he has a liar, but he has no modality for understanding what he's looking at. He has no, you can't ask him to guide you to the bathroom or anything like this. And we believe that the future humanoid robot stack is going to be made up of 6 layers, and since I only have 7 minutes to go, we'll go through this very quickly. Number one, of course, which you've seen already is we need locomotion. Robots need to be able to walk around without falling over, and I can tell you that I'm going to need to help Terry down the stage today because locomotion is still early. But of course we also need manipulation today. Terry cannot open a can of Coke for you or iron your shirt or anything like this. There's a lot of work left to be done with manipulation, but even if Terry knew. How to manipulate things. You still couldn't ask him to, for example, empty all the trash cans here at the conference. To do that, he's going to need spatial semantic perception as well, not just the camera, but the ability to understand where things are and what things are, and he needs to be able to put that into the context of a map so that the second time you ask him to empty the trash cans, he remembers what the trash cans are and doesn't bounce around like a first generation Roomba. And even when he has a map, he's going to need positioning to understand where he is in relation to the map. A lot of people don't know this, but the GPS is a line of sight technology, so your GPS does not know you're sitting at that table, doesn't know you're in this room, it doesn't know you're on this floor. And then finally we're going to need some kind of applications layer to tie all of these things together. In 2021 at Aki, we realized that you could get a head start on the robotic stack by skipping. The tricky part of locomotion and manipulation. If you wear a pair of glasses. And you have an AI look through your glasses. Then you could do what we call hybrid robotics, where we have human locomotion, you can have a worker that walks as well as me. You can have human manipulation. But you can still have AI perception, you can still have AI mapping, you can still have AI positioning, and you can still run AI applications. In fact, you can create AI co-pilots for physical work. And why wouldn't that be one of the stops on the way to the full AI revolution? Because today, did AI take all the engineering jobs or did AI become a fantastic co-pilot for engineers? And just 2 months ago, we closed our first multi-million dollar deal where we used this stack with just phones and glasses and soon also robots to help retailers understand what is happening in their physical environment. So for the first time ever they can capture an accurate digital twin using spatial computing and AI telling them where their products actually are so they can get better sales reporting, but it also helps them do AR navigation for the staff, reducing the walking distance by a whopping 40% for the people that pick up your online groceries, and they can also do AI and AR task management uh using the system. This is being rolled out into thousands of locations today, and later this year, together with our partner Mentra, who makes smart glasses, we will be releasing an AI co-pilot for physical labor, capable of detecting empty shelves, things that need to get done. So as the retail worker walks through, it turns out we don't replace. The retail worker, we replace the store manager. You get an AI co-pilot that helps you figure out what to do. And that same system is what will allow someone like Terry to understand where are the products and know what to build and where. So what comes after the GPS? Asia is a good place to talk about that. Uh, you need spatial semantic perception, meaning you need to understand where, where things are and what things are, you need to understand the difference between a bicycle and a stoplight. And this hit home for me when I was living in Beijing. Beijing is a big city. I lived there for 7 years. In fact, it's so big that there are more cars on the road in Beijing than there are people in Los Angeles. And if you do a quick back of the napkin calculation based on the average commute, you find that roughly the time it took to build the pyramids. Lost in Beijing traffic. Every single week. And now we might imagine that self-driving cars is going to solve this because cars are going to be coordinating with each other and we won't need red lights anymore, sure, if and only if. These robots, these cars, learn to describe space to each other and their own precise location and the precise location of things that they see. The GPS is never going to work for that because the GPS is a line of sight. This is New York, the biggest city in the West. New York has over 900 buildings taller than 100 m. Astonishing. Until you realize that Hong Kong, where I live, has over 4000 buildings taller than 100 m, and Hong Kong. It's just this little area down here. The rest of this is the Greater Bay Area. It's Shenzhen, Guangzhou, Dongguan. It's an area 2/3 the size of Greater LA but home to 87 million people. And more high-rise buildings than all of North America and all of Europe combined. So it's no wonder why Buddha, that so much machine perception work and GPS replacement work is happening here in Asia by the standards of this century. There are no big cities in the West. So what happens next? We believe that the most important work that civilization can undertake right now is to make the physical world accessible to AI so that we can as a first step, get AI co-pilots for all the physical labor that we wish we could do better and ultimately replace that drudgery with the help of companions like Terry that soon we'll be able to do these tasks autonomously once we've made the physical world accessible to AI. Thank you very much. Nils, that is just so impressive. Hi, Terry. Good to see you. I have just one question for you. You know, you were talking about the development of the robotic technologies and, you know, where we're at and where we're going. By when will Terry be able to bend it like Beckham? Uh, there's already, uh, robot soccer leagues happening sponsored by Booster Robotics. Um, it's not super exciting to watch yet. Terry, I tear you down the ramp in a second. It's like my grandma, you know, uh, needs a bit of help down the stairs, um, but yeah, it's already happening. Uh, robots fighting rings have already started happening. Uh, I think, uh, robot entertainment is, it's now. Yeah, I think I saw a Black Mirror episode about that. Thank you very much. Thank you.

From market projections and risk assessment to fraud detection and personalized wealth management, AI is quietly—but profoundly—changing traditional and decentralized financial models. The stakes are high in global financial hubs everywhere, including Singapore. This session explores how AI is transforming how money is managed (and made) while confronting new challenges around bias, accountability, and trust.

Rethinking Global Finance

Asia isn’t just following the global AI innovation playbook, it’s writing it. From China to Singapore and from chipmakers to LLM’s, the region is producing unicorns at a pace that is capturing the world’s attention. How do you go beyond the headlines and spot the next unicorn like Zhiphu AI, Moonshot AI or Sakana AI?  We'll explore the venture trends and sectors that are driving Asia’s AI start-ups forward and hear from the experts who know how to identify and bet big on the region’s next potential decacorn.  

Spotting the Next $1Billion Opportunity

Agentic AI is coming for the world’s knowledge workers—accountants, lawyers, consultants, and customer service teams alike. From boardrooms to BPOs, this session explores how generative and task-based AI are reshaping professional workflows, global outsourcing models, and entire industries. Is this the dawn of a productivity revolution—or a workforce in transition? 

White Collar Wake-Up: Productivity and the Professional Reckoning

At Walmart, a strong tech team in India is building and deploying generative AI tools that touch nearly every part of the business—from supply chains and storefronts to merchants and developers. We discuss one of the most ambitious enterprise AI rollouts on the planet to unpack what’s working, what’s breaking, and what it tells us about the future of the world's largest retailer. 

How Walmart Puts The 'AI' In Retail

A decade after IBM Watson's smarter healthcare revolution fizzled out, a new wave of AI systems—powered by large language models, multimodal learning, and surgical robotics—is quietly reshaping how care is diagnosed, delivered, and optimized. From AI-assisted stroke detection and LLM-powered patient triage to precision-guided surgical robotics and digital health platforms, hospitals and medtech companies are deploying these tools in real-world settings. But as machines take on more cognitive and procedural load, critical questions loom: How accurate are these systems? Who is accountable when things go wrong? And can medicine preserve its human touch in an algorithmic age? 

Second Opinion: How AI Could Improve Patient Outcomes

As generative AI encroaches on tasks traditionally handled by junior professionals—brief writing, memo drafting, spreadsheet modeling, legal discovery, and audit reviews—the apprenticeship model that has long underpinned law firms, consultancies, and accounting practices is under threat. AI replaces the bottom of the ladder, how do we train the next generation of partners, principals, and experts? Are firms ready to redesign the talent pipeline—or are they quietly bracing for a skills crisis a decade down the line?

Vanishing Rungs: AI, Apprenticeship, And The Future Of Professional Services

From data to delight, Refik Anadol transforms algorithms into art. In this immersive visual showcase, the pioneering media artist behind the world’s first AI museum blurs the line between code and creativity—inviting us to imagine what machines see when they sleep. 

Dreaming With Machines And The World’s First AI Museum

All right. Our next guest is a pioneer in AI-driven art and media installations that convert data into immersive visual environments. His work has been featured at iconic landmarks and festivals worldwide, from the Walt Disney Concert hall in Los Angeles to the San Pompidou in Paris and Art Basel. His next big project is Dataland, the world's first dedicated AI art museum. Set to open in LA later this year. Joining us to share how he is using AI to expand the possibilities of human creativity and perception. Please welcome artist and director Rafik Anatol. Now Rafik will be here with us here virtually. Rafik, can you hear us? Yes, I can hear you. Hello, good evening from Los Angeles. All right, wonderful. Shall I let you take it away? Please, thank you very much. So, good evening, everyone. I'm excited to be connected with you. I would like to share my journey with you and happy to get some dialogue. I hope you can see my screen. All right. So, Give me 1 2nd, just 1 2nd. I want to do one more sharing technique to be sure you hear this sound and Here we go. All right. So, Again, good evening everyone. I'm so happy to be connected with you. I'm a media artist and director, and I would like to share my journey and key points that transform our work. Um I am at the moment working as a studio with my partner Epson. We are a wonderful group of people, 20 people in Los Angeles, and can speak 15 languages and 10 countries. And the journey for me started almost. 2008 when I coined the term data painting, the idea was when I saw the very first time, the first, I would say, data pigmentation moment in my journey, I couldn't forget the feeling of what will happen if data becomes a pigment, a pigment that doesn't dry, a pigment doesn't have any physical like constraints, that is always changing, always in flux and transforming build environments. In our work, one of the common patterns that you will see is our conversation between art, science, and technology, AI neuroscience and architecture, but most importantly, humans, machines, and environments are always coming together to create an exciting new form of art. And our work is generally is not only stuck in the gallery or a museum, but a place anywhere art can survive, meaning to bring inspiration, it can be in a school in a university, to bring hope, it can be in a hospital. And over the years we worked with incredible partners and I think one of the reasons I moved from Istanbul to Los Angeles to get my second MFA degree at UCLA allowed me to like practice. Mediums very early. I mean now I t for 10 years creating these art forms and artworks across the world, and I'm happy to say that this form of art is, I think, one of the things that I believe will be an inspiring part of creating value and digital art transformation and also transforming the idea of art for anyone, everyone, and pretty much any culture. And as you can see, the work is embedding media arts into architecture, meaning that it can be in any built environment. The floor, the ceiling, and every form of like space can transform into art. And I think that's the type of like world that I've been dreaming since my childhood. This work you are seeing here, 350 Miss Salesforce Building 2014 was the first time I was able to create this space, and over the years that took off and met with many pioneers and I would like to share some stories around them. 2015 for me was a very important time. That was my very first time start becoming the artist in residence at Google's AI program. What you see next to me is also Mike Taika. He's a wonderful person, my first, I will say, mentor, and on the, on our side, you are seeing the machine, I guess the the the the. The Dream, the software originated in a deep convolutional network code named Inception after the film of the same name. It was developed for the ImageNett Large-scale Digital Recognition Challenge in 2014 and released in July 2015, meaning it was the first time an AI output was visualized in a form of human recognition. And that's when I started this idea of a machine hallucinations. The question was if a machine can learn, can it dream, can it hallucinate? And it's, by the way, not for me new, but it's generally like the idea that artists, as we are always asking the question what is beyond reality? And I believe that AI is an incredible tool to think about this question and think about how we can push forward this idea. And the first project for me with AI. About 2016 Library of feature inspired and the project was called Archive Dreaming. As far as I know, this is the very first exhibition using AI in real time and 2016 and 2017, this exhibition was our first application and and again not on physical but also in the virtual world and transformed the idea of 1.7 million documents into a living library. And back on time, it was the first time I started experimenting what an AI dream could look like. And on the left side you are seeing an AI model train on the 1.7 million documents and on the right side me transporting that data into a kind of a data pigmentation or AI data pigments. And as Cargan mentioned, the man will When curiosity world that never were, but without it we go nowhere. As Carl Sagan mentioned, I was super inspired by the possibility of machine learning algorithms, and I'm calling these AI data universes, the idea of sculpting with intelligence. To me these are also sculptures which I'm calling them AI data sculptures. So what you are seeing here. From Rumi's pretty much like Persian poems like a poets me name me in 19 language, or you can see Mozart, every single music Mozart ever created. So if I look at them, I don't see just AI data plottings, but I see them three dimensional, even six dimensional, X, Y, Z, RGB coordinates, applied, machine learning algorithms recognizing this data, and, and truly human-machine collaboration. And the other topic I'm super inspired is the idea of embedding AI into architecture, dreaming the future of architecture. Here you can see our early work, 2017 and 2018 and Miss Hallucination NYC 1.13 million images of New York transforming into these environments, which is later transported into like a three dimensional environment. And again, 2018 was very early years of using AI and. environments and this became a body of work for our studio. We have been creating immersive environments more than 14 years, but the idea behind it like truly understand three dimensional imagination of going beyond two dimensional world of imagination. And the other works that I'm super inspired is the idea of using media facades, which is very much played on our inspired projects. I've been lucky to work with Frank Gehry, Hadi Gaudi. Maybe you may remember this project from 2 years ago in Barcelona. We transformed the Casabao, the Gaudi's incredible building by using real-time weather data and real-time weather application systems. We were able to create the building into a living architecture, meaning the wind data, the weather conditions like the wind and the humidity was transforming the building. And what was really special to me in this project was the idea of giving, like giving this life to a building through like data and AI and and giving this new conscious context to a building, transporting the facade into like a living surface was truly a very long time a dream. And I do believe that if we go beyond concrete, steel and glass, if we use light as a material, if you blend light with AI, I do believe that we we can invent new worlds of imagination. And this is this is like an obsession for more than 16 years, but I love this idea of still like it doesn't get to me repetitive or old because every building, every data, every AI, and every surface is unique and can be fascinating to experiment this. And most importantly, this brings people together. I mean, as you can see, 65,000 people came to our performance and The other project that I'm super excited about is 2 years ago I was the opening artist for this fair in Las Vegas. So what you are seeing is our iconic AI data sculptures and paintings transforming into the world's largest data sculpture in Las Vegas, fascinating pioneering work that I think that the the buildings are incredible, and we are excited and honored for our collector Jim Dolan and The entire sphere team allowed us to like create this artwork and on the other hand, I believe the most important moment for our studio happened also almost 2.5 years ago when the incredible team at MoMA, our creators Micao and Paolo Antonelli, invited us to think about an artwork for musical modern arts called Unsupervis. And by the way, what is amazing about this project is for 9 years ago, MoMA, one of the most important museums in the world, put their 200,000 almost artworks in their collection into GitHub, which was an open source data. And the question was, how can we treat the AI model and transform this into a new language, a new form of art inspired by all the pioneers in the history of MoMA. But instead of this AI transforming into a very realistic another AI, what will happen if you just create this unsupervised artwork, meaning what happens if we transcode the data into a kind of a living pigment, and to make it happen more unique and more transformative and innovative, we use a camera. and a microphone and a weather station as you can see here and also a real-time application meaning we were able to demystify every single mission decisions, meaning every day the artworks was unique and different and we also show exactly people what AI is dreaming from which clusters and which parameter is improving, which what part for it. It was absolutely demystified experience. And to be more clear and again thanks to our media friends and especially Jason Huang, there's two AI. Computers. One is on the left is computing AI dreams. The other mission on the right is me as an artist taking that data, transcoding into this real-time pigmentation, meaning it's a truly AI and human collaboration. So there's no lazy, there's no shortcut, there is truly an ethical data transcoding into a form of art, and the result was a media ball that is transforming every single day into a new form of art. The reaction was something exceptional. The artwork was only open for 3 months, extended 4 times to 1 year, and most importantly, we received around 3 million people, one of the largest audience in the MoMA history. And the average viewing experience was 38 minutes, which is a fascinating number if you think about the artworks and how much people spend time in the museum, and it was a fascinating to me a breaking mold points and how the new form of art, new form of imagination can transcode the entire. Information age and and make the audience become a part of the experience itself. And also because of that we start this extraordinary research and the exordinary impact of our work and and a new AI paper coming soon and you will be able to understand what exactly is happening in our mind when this work is happen. So transforming this work to this, and of course much bigger ns I guess is this artwork also went to the permanent collection of MoMA, which was an incredible moment and honor not only to me but also all the artists working in the field. This was a moment to give a positive signal that this is an art form accepted by the art world. And also this was not only just New York, but we found that this impact was almost everywhere in our exhibitions where the impact was significant. And from there we thought with my partner Epson what could be our contribution in our lifetime to this medium and this practice bringing art, science, and technology AI to the edge of imagination and how can we do it ethical and sustainable. That's when this last September we announced our AI museum called Datalands. It is opening hopefully in Los Angeles and the announcements are coming soon. And what Eland is a very unique form of institution. First of all, when we announced Etaland, I wasn't expecting such an incredible like audience and responses and I'm happy to say that the reaction was extraordinary. I think opening the first AI museum in the world was like something that I couldn't expect like this level of positive impact. The project is not starting as just another, I guess immersive environments or something like that. We've very much focused on our context and meaning and discourse, meaning we want to open the museum by the world's first AI model dedicated to nature, which I'm calling it larger nature model. What makes the AI model different or AI system different is we start by permission. We ask incredible people from across. The world, these are our data partners and also incredibly honored for Google and Nvidia friends giving us their support. We created the first AI system, I believe, the most advanced AI system about nature. And to be more precise, we have an incredible amount of data. We receive 5 billion images and on top of that, we are traveling 16 different rainforests and recording our own data. At the moment, the entire system is running on Google Cloud and a sustainable energy, meaning we are not harming nature when we use this AI system. And here are some results from AI models in collaboration with Black Forest. These are like our first image model resort transformation. And also last year we had an incredible chance to again work with JSson Huang and the wonderful people at NIDA again for almost 9 years with NIDA with a very close relationship, but most importantly, last GT. See thanks to Janson Huang and of course his wonderful team. We were able to transform before his keynotes. You can see me on the main stage, which I'm real time transforming L&M larger model into a real-time application in 16K resolution in real time. So what we are seeing here is, I think, an extraordinary transformation of art. What we are seeing here is a true collaboration between artists and the technology partners, but we are truly crafting a new form of art. And by the way, at Data lens, you will be able to enter this space. You will be able to see as incredibly complex. Transformation you can imagine and also what is exciting is you will be able to smell and hear this AI model. So that's where I am as an artist and I'm happy to hear some questions and I hope that uh to bring dataland experience around the world, and I'm excited to share more. So thank you very much for listening. Rafik, thank you so much. Yes. Uh, as a resident of Los Angeles County, I'm really looking forward to visiting Dataland, so I can't wait. I just have one question for you before we let you go, and that is, um, you have likened AI to pigment in paint. You have also likened it to a collaborator that dreams. Is AI a tool for you or a collaboration, a co-artist? So I think it's a great question. For me, AI is more than a tool because if I say AI tool, I feel like we are underestimating the power of this technology. I do believe AI is completely changing the human history, and I, I, that's why I'm calling it as a force, a force that I can blend with good, a force I can blend with. Creative becomes a creative force, and that force is enhancing my mind, my soul, my spirituality, my emotions, my imagination. I see that as a co-creator, I'll do as your second part, but I see a little bit kind of a force that is very powerful that we have to understand and be sure. And I also say that if AI means. Anything and everything. I just believe that we have to be sure it is for anyone and everyone. Fantastic. One more round of applause for Rafik and Nadol. Thank you. Thank you so much. See you very soon.

As climate risk, supply chain disruption, and population growth strain global food systems, the race is on to cultivate smarter, more resilient agriculture. From AI-powered greenhouses and robotic harvesting to digital twin farms and gene-edited seeds, this session explores how technology is transforming every layer of the agri-food chain. We’ll hear from leaders at the cutting edge of sustainable agriculture, data-driven farming, and food innovation as they unpack what it takes to feed the future—ethically, efficiently and at scale.

From Lab To Table: Reinventing The Future Of Food

What must an AI company do to justify an eye-watering $300 billion valuation? Answer: Make money—and lots of it. As OpenAI reconfigures itself for revenue, the U.S. company is looking further afield than its home turf for growth. We talk to its international chief about scaling responsibly, partnering globally, and the unending pursuit of AGI. 

OpenAI's Global Ambitions

As artificial intelligence reshapes global power dynamics, the competition between the United States and China is accelerating. This panel brings together leading experts to examine the strategic ambitions, regulatory approaches, and technological advancements driving this high-stakes rivalry—and what it means for global governance and economic security. 

How are AI Superpowers Navigating the U.S.-China Tech Rivalry?

Step into the Auntieverse, where Singapore artist and designer Niceaunties fuses AI, nostalgia, and biting wit. In this bold visual showcase, tradition clashes with tech—and no cultural quirk is off-limits. 

Welcome To The Auntieverse

Thanks so much Andrew, Will, and Nigel and hello everyone. My name is Aisha Khana. I'm CEO and co-founder of ADOAI and also a co-chair of this event. I am so excited to be here to introduce our next segment here to share her unique fusion of AI. Art and Southeast Asian culture, join me in welcoming the visionary behind the Antiverse, a surreal reimagining of the everyday that I hope will inspire and delight you all. Please put your hands together for Singapore-based artist and designer nice aunties. When I was young, my world existed in a radius of 2 kilometers. Most of the time was spent with my grandma and auntie while my parents worked. Life was very simple back then. There was no internet, just books and TV to spend the time. One day, my world and imagination exploded when my auntie introduced me to Doraemon, a robot cat from the future with really cool gadgets. Since then, I love imagining characters and making up stories. When I started using AI 2 years ago, I felt the same creative energy just like back in childhood. But this isn't to say that life before AI was boring. It just moved at a very different pace. I worked in architecture and it takes 3 to 6 years, sometimes longer, to complete a building. Even a small project like this charity doghouse, which I called the Reservoir Dogs, took 3 months to finish. Using AI, I was making these animated vignettes once a day. So the pace in creating these ideas was extraordinary. But of course, I knew that I could not have done it without the life experience and memory before AI. So 10 years ago, my partner in architecture had this idea to occupy the East Coast Park of Singapore with these colorful beach huts. They were to be made with upcycled ocean plastics. So this idea won an award at the World Architecture Festival in 2016. I remember my shock when he showed me this photograph of a dead sea bird with its stomach filled with ocean waste. 10 years later, this is my version of the same bird, except this is the auntie lounging on the beach in an imaginary place called Anillantis. And this is inspired by Atlantis and the Great Pacific Garbage patch and occupied by Auntie. So this became like 5 videos showing how aunties were chilling out on the beach for plastics, and they started to clean the ocean floor, dressed in mermaid costumes, and they also collected waste in the city. And then upcycle them in workshops and factories and eventually selling them in Auntie's outlet malls with items for plastics. So Anlantis is part of a larger universe called the Antiverse. Which is a world where aunties are the main characters. So the auntie is not just an older woman in the family. They are a stereotype figure, a social stigma typically associated with aging, being naggy and old fashioned. So the auntie has many, many traits. And um, for instance, um, an auntie can be very critical and sometimes conflicted. For instance, my auntie will call me fat and then she will feed me more food. So, you know, anyone can be labeled an auntie if you have these traits, even a man. But you know, growing up with women surrounded by them, I knew the auntie is much, much more than that. She's complex, she's funny, creative, quirky, and often misunderstood, um, but she shows her love in a multitude of ways. So the antivirus is my way of honoring their everyday labor and to question my own attitudes towards aging. But most of all, I wanted to reframe the idea of the auntie. I explored many things like aging, beauty image, the body image, and who gets to be visible on the fashion runway. I also explored auntie's big hairdo and how to maintain them. And everyday subject matters like laundry. Also things like astronomy, astrology, like the recent planetary alignment. Auntie even tried to have a cooking show. Welcome to Cooking with Auntie. First you add this and then. Zoom in. And then you add that, take photo. And then, and then here, fried rice with no rice, yay. It didn't go so well. The cooking show was canceled. But I was fortunate to talk about the Antiverse at TED in Vancouver and also exhibited my work all around the world like at Times Square and Sunset Boulevard in LA and also at the Science Museum. This is a character from the Antivert, the Falling Gallic. I also hung oversized auntie underwear at an art festival in Uruguay last December. Uh, it's called Nice Aunties by Nice Aunties. So the idea of beauty is often associated with youth and aging with decline or something to be reversed or concealed. I explored and questioned the pain and torture that people go through for beauty treatments to meet societal expectations. But at the same time, I marveled at the similarities between our skin and the planet. You know, like cellular regeneration in our bodies is akin to life growing in landscapes and nature. So this idea was developed into a physical sculpture for the beauty brand L'Oreal. And I worked with a sculptor and this was exhibited at the headquarters in Paris last month. So at the beauty of longevity event, I talked about the antiverse and I shared ideas, eastern philosophies about aging and social infrastructure. Now this was transformative for me and my project. Just not too long ago, you would never imagine an auntie being associated with new and evolving ideas of beauty. I mean, she is a very stereotyped figure. So this is amazing. And Singapore, I want to talk about Singapore. So this is a very beautiful and ultra modern city, but not many visitors get to see behind the scenes, you know, where people hang out in the heartlands, where they shop, where they do their groceries. So my grandmother. She was a fishmonger's wife, and she spent her whole life doing housework at home, serving the family. She used to make fish balls to sell at the local market, and we have no visual records of her for some reason, no photos, no videos, but luckily with AI I was able to recreate. My memory of her and her life. And here is the surreal story, goddess. Zamba Jack. Yeah job I. Boy. Once upon a time, a goddess arrived on the planet, saw this cute little place. And decided to stay a few days. She was having fun, tending to slippery things, making chewy balls. She could do everything, she handled it all. She was very, very busy and did it so well, and she forgot who she was, where she came from. Day after day she did this task. Centuries passed. Still she labored quietly unnoticed. And then one day all of a sudden she woke up, remembered who she was, and left. Gosh, what was that? I have to get back, have to get home. Too many chewy balls. The goddess was glad to have a break and then Her sister appeared and asked, Why, where have you been? The goddess looked back at the place she spent time at and realized it was not real. It was weird. No worries, her sister said. Let's chill out. And so they did. See T N E N D What Thank you. That was absolutely amazing. nice auntie. Thank you. So I have a question for you. Is the concept of nice aunties or women this age, is it just about Southeast Asian women or Singaporean women, or do you think these complexities that you explore can cross borders as well? Well, I started a project wanting to understand anti-behavior in Singapore and the region where I grew up in, but as I put the project out on social media, based on the feedback that I get, It seems to resonate in all parts of the world. I mean, everyone has aunties, everyone has a mother, and they could understand the connection and the nuances of life, I guess. And I think it's just so incredible that just to confirm, you use AI for every part of the videos you created. Yes, um, so AI to make the visuals, AI to make the sound and the voice, um, AI sorry except the editing that was manual, yeah, that was me. That was me. Well, thank you so much, I thought it was a pleasure to have you here and your work is amazing. Thank you. Thank you.

AI is rewriting the rules of growth, trust, and competitive edge. From real-time underwriting and biometric ID to large language model copilots and predictive analytics, banking and insurance leaders are integrating intelligence into every layer of decision-making. This session brings together top executives from banking, insurance, and fintech to explore how AI is reshaping the financial stack—streamlining operations, deepening customer insight, and expanding access. What are the new design principles for intelligent finance? And as algorithms take on more authority, how do we balance automation with accountability?

The Intelligent Bank: AI And The Next Financial Frontier

Behind every AI breakthrough is a fierce battle over who controls the hardware that enables it. As models scale and compute demand surges, a battle for supremacy is playing out in silicon, supply chains, and sovereign strategy. This session explores how the future of AI is being shaped by force—in fabs, capital markets, and national capitals around the globe.

The Next Front Of The Global AI Chips War

From orchestrating complex workflows to delivering real-time decisions, AI agents are unlocking powerful new capabilities—and raising equally powerful questions about how organizations should be built for the future. Which questions must be answered on the road to enterprise deployment at scale? What does responsible autonomy look like in high-stakes environments? How can businesses move beyond automation to augmentation and innovation with purpose? Top tech executives discuss.

The Agents Are Here. Now What?

Southeast Asia’s startup ecosystem has experienced remarkable growth over the past two decades and now boasts more than 60 unicorns across diverse sectors such as fintech, e-commerce, and digital services. But for the past several years, the region has suffered through a venture “winter” marked by a downturn in funding activityand deal volumes at multi-year lows. Late-stage funding has grown particularly scarce. What are the reasons for the funding dearth, and what can Southeast Asia startups do to shake off the doldrums? And what lessons can be learned from Silicon Valley and other parts of the world where startup ecosystems continue to thrive? How can VC’s help encourage their portfolio companies to accelerate growth.

Navigating Southeast Asia’s Venture Landscape

Since its inception, the gaming industry has consistently led the charge in developing and embracing cutting-edge technologies. Now, with the rapid acceleration of AI, the global video game market is projected to grow by $111.7 billion over the next four years. We’ll dissect the next era of gaming and entertainment— from immersive AI-built worlds to smarter marketing and monetization—and what it means for creators, players and profits.

The Next Era Of Gaming

In the absence of consistent regulation, companies in finance, health, and infrastructure are crafting their own AI policy rulebooks. This session goes behind the curtain to explore how the most advanced enterprises are balancing performance vs. principle, transparency vs. trade secret, and innovation vs. institutional risk as they fully embrace AI. 

Bridging The Governance Gap

What can a global, multi-source dataset tell us about where AI is headed, and who’s steering the ship? Russell Wald, Executive Director at Stanford HAI, breaks down key insights from the AI Index, the world’s most comprehensive report on AI’s progress and impact. In this fast-paced session, he highlights surprising shifts in talent, investment, and regulation—and makes the case for keeping AI innovation rooted in human values and societal good.

AI By The Numbers—And For The People

So our next speaker takes us inside the AI index. This is one of the most comprehensive data sets tracking the field's rapid evolution. The AAI index reports on global trends, technical progress, investments, policies, and impacts. To tell us more about where AI is headed, please welcome Stanford Institute for Human Centered Artificial Intelligence executive Director Russell Walt. Russell. Good afternoon and thank you everyone. I'm Russell Wald, the executive director of the Stanford Institute for Human Centered Artificial Intelligence, or what we also call as HAI. We are Stanford University's globally recognized interdisciplinary Research Institute at the forefront of shaping AI development for the public good. HAI was established in 2019 with the goal of advancing AI research, education, policy and practice. And through our role. through our convening role and rigorous study of AI, we have become the trusted partner on AI governance for decision makers in industry, government, and civil society. And today I'm going to talk about what we produce at HAI, which is the AI index, and then which is an annual data-driven analysis of trends in AI and tracks research, development, deployment, and the socioeconomic impact of AI across academia, government, and industry. The index has gained worldwide recognition as the authoritative report on the state of AI. And for the next few minutes, I will highlight a few standout segments of the 2025 report, and I say it's a few segments of the report because it's over 500 pages, and these are just some quick hot takes from it. First up, we see AI performance consistently seems to improve year over year. An example of this is in this slide where we use mid journey, a text image generator with a query of a hyperrealistic image of Harry Potter. And from February of 2022 to July of 2024, we see rapidly increasing quality in these generated images. The progression demonstrates the significant improvement in Midd Journey's ability to generate hyperrealistic images over a two year period. In 2022, the model produced cartoonish, inaccurate renderings of Harry Potter, but by 2024, it could create starting, startlingly realistic depictions. And you'll note we have essentially gone from what mirrors a Picasso painting to an uncanny rendering of Daniel Radcliffe, the actor who played Harry Potter in the movies. And because of this consistent performance growth, we are increasingly challenged when it comes to benchmarking these models. As of 2024, there are very few task categories where human ability surpasses AI. And even in these areas, the performance gap between AI and humans is shrinking rapidly. Essentially, from image recognition to competition level mathematics to PhD level science questions, AI is exceeding human capabilities and it's becoming increasingly harder for us to benchmark that. AI is also coming into our everyday lives, and from healthcare to transportation, AI is rapidly moving from the lab into our daily life. In 2023, the US FDA approved 223 AI-enabled medical devices, and that's up from just 6 in 2015. On the roads, self-driving cars are no longer experimental. For example, Waymo, which I regularly take, living in San Francisco, is one of the largest US operators and provides over 150,000 autonomous rides each week. While Baidu's affordable Apollo Go Robo Taxi has a fleet now that serves numerous cities across China. Business use of AI increased significantly after stagnating from 2017 to 2023. The latest McKinsey report reveals that 78% of surveyed respondents say their organizations have begun to use AI in at least one business function, marking a significant increase from 55% in 2023. Driven by increasingly capable small models, the inference cost for systems for a system performing at the level of excuse me, the inference cost for a system performing at the level of GT 3.5 dropped over 280-fold between November of 2022 to October of 2024. Hardware costs have declined 30% annually, while energy efficiency has improved by 40% each year. I want to note, this is in reference to inference costs, not training costs, which I'll refer to in just a moment. And I'll also note here too from this, what we see is open weight models are also closing the gap with closed models, reducing the performance from 8% to just 1.7% on some benchmarks in a single year. Together, these trends. are rapidly lowering the barriers, uh, lowering the barriers to advanced AI. However, Even with infra and hardware costs going down, training costs remain out of reach for academia and most small players. Nearly 90% of notable AI models in 2024 came uh came from industry, which is up from 60% in 2023. And while academia remains a top source of highly cited research, it does struggle now at this point to stay as advanced in the game at the frontier level. Model scale continues to grow rapidly. Training compute doubles every 5 months, data sets every 8, and power use annually. Yet performance gaps are shrinking. The score difference between the top and 10th ranked models fell from 11.9% to 5.4% in a year, and the two top, the top two models are now separated by just 0.7%. Pardon me, that was the slide that was in reference to that. So give you all just a moment to see that I I didn't advance it. But the main point that I'm trying to make here is that the frontier is increasingly competitive and increasingly crowded. And In recent years, AI model performance at the frontier has converged with multiple providers now offering highly capable models. This marks a shift from late 2022 when Chat GBT's launch, widely seen as AI's breakthrough into the public consciousness, coincided with the landscape dominated by just two players, and that was OpenAI and Google. While the prior slide noted performance by model, this slide focuses on training cost by model. And what I want you to take away from this slide is one of the most important things is to note that the Transformer model costs $930 for Google to train in 2017, and that is the T and GPT, the baseline level of architecture, and now today we're at $200 million to train Gemini Ultra. Last year's AI index was among the first publications to highlight the lack of standard benchmarks for AI safety and responsibility evaluations, and while companies measure math, coding and language skills, there's no standardized benchmark for any of this. The index has also been analyzing global public opinion, and one point to note here is if you are from a non-Western industrialized nation, you are more likely to view AI positively than not. China has an 83% positive view. Indonesia 80%, and Thailand 77%, whereas Canada is at 40%, the US 39%, and the Netherlands 36%. And I'll close with the geopolitical situation in AI. The US still maintains a lead in AI followed closely by China. However, this gap is tightening. My intention with this slide is not to exacerbate the idea of an AI arms race between China and the US, but instead to highlight the different approaches between the most advanced frontier AI model developers. Over the last several years, the US has. On a few proprietary model providers. Meanwhile, China has deeply invested in its talent base and more importantly in open source environment which has allowed for a thriving base. If I were to as this trend continued and I appeared here next year, at this rate, China would surpass the US in terms of model performance. You can access this year's AI index by following the uh the QR code here. Thank you.

AI’s hunger for compute is sparking a global surge in data center investment—but the real story is what’s happening underground. In Malaysia and across Southeast Asia, sovereign ambitions, grid pressure,and ESG scrutiny are colliding with hyperscale demand. We’ll explore how the data center gold rush is playing out on the ground—and what it takes to stay ahead in the age of AI-fueled infrastructure.

Data Centers: The New Global Arms Race

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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