LIVE: World Bank and IMF leaders discuss AI and jobs in Bangkok

Well, without further ado, let's kick off this session. Do join me in welcoming three leaders who are helping to shape that conversation. I'm joined by Ajay, president of the World Bank Group, President Tharman Shanmugaratnam, president of the Republic of Singapore, and Kristalina Georgieva, of course, the managing director of the International Monetary Fund. Welcome to all of you.

Hi. Hi, President. Hello. Hello. Welcome to all of you. The 81st meeting, as we were saying. Um, and a lot has changed, of course, hasn't it? Um, so, I think we'll start with you, um, Ajay. Um, this session is called small AI, more and better jobs. Jobs and opportunity are at the heart of the World Bank's agenda. How's the bank changed in that way, and how does AI fit into all of this right now?

Yes, so, thank you, and thanks for having me on a panel with the two of them is a privilege, and I'm lucky to be here. I believe that the bank's entire focus has to be on jobs for a very simple reason. 1.2 billion young people in the coming 15 years are going to become 18 years of age. So, that could be a huge demographic dividend. If, when they're growing up, they get clean air, clean water, health care, education, skilling. But, once they're grown, they need the opportunity to have and be a productive part of society. So, the word job does not mean working for a big company or a small company or as an entrepreneur. It's being productive in society, having a sense of optimism and a sense of dignity that comes from earning and being a part of that system. That's what we're talking about. The bank's entire focus has moved towards that over the last few years. It's not that we don't build a school, or we won't build a highway, or we won't build a skilling center, or a health care clinic. We have to do all those, but we got to do them with the perspective of job creation. Which starts from having the right foundations, which is physical and human infrastructure, having the right governance and policies in place. Something that Singapore has done very well in its history, and then having the right way to de-risk private capital to be able to come in and invest at scale because it is private capital that creates jobs. Governments enable and the private sector generates between 75 and 85% of jobs in any country. That's what we're trying to do. How does AI fit in? In the emerging markets, where the conversation is not about large language models, where people are concerned about the possible impact on jobs. Possible, but not proven yet, but possible. In the emerging markets, it's about applications that could add enormously to productivity for a school, an educator, a health care system, an agricultural system for small farmers to get to scale. And hopefully, as we speak, as well as from the subsequent panels, you'll get a bunch of examples of that type. That is what we're focused on, the role of small AI into the emerging markets as a productivity enhancer.

Okay, thank you very much, Ajay. Um, let's move on to you, President Tharman. Ajay mentioned your country and the work that you're doing when it comes to developing AI. Um, you've said in the past that AI may disrupt jobs in advanced economies, but could create a different risk for developing economies. And Ajay made that distinction in some way just then. Um, you say that new jobs may simply not be created fast enough in developing countries. What do developing nations need to do now, um, President Tharman, to make sure that their young people are able to participate and benefit from the next wave of technology and economic growth? And as we just heard there from Ajay, you know, the future of young people and jobs is so crucial to all of this conversation. Good. Well,

I think taking off from what Ajay said, although I'll speak at 10% the pace that he spoke at.

The first thing we have to realize is it's early days. This is not a shock. This is not a pandemic. This is a whole new era. And we should start with the recognition in the public sector and across all our stakeholders. Growth is going to be critical to secure our good jobs, but growth is not itself going to be sufficient to achieve those good jobs. And the evidence is already there. If you look at China, India, developing Southeast Asia, they're growing quite well, but job creation is weak. So, there's already a divergence between growth and job creation. And AI is going to widen the divergence if we are too passive about it. So, we've got to do everything we can to first use AI to create good jobs and to create jobs that can be done well by human beings, can allow them to ride up a career ladder despite AI. Second thing we've got to be really concerned about is a K-shaped economy and a K-shaped labor force. In the first wave of automation, it was the low end of manufacturing that fell out. And some of them moved into services, to the gig economy, and so on. This time round is the broad middle in the workforce that we have to be concerned about. And we cannot afford to have a K-shaped workforce or population where the broad middle and that broad set of skills that the middle in the workforce has accumulated falls out. So, bear that in mind. These are two broad structural changes now. I'm an optimist, actually, like Ajay and like Kristalina. I think there are sectors of the world economy that still offer very significant promise for the developing world. First, don't give up on manufacturing. I'm not a pessimist on the manufacturing. There are whole swaths of the labor force of the developing world where costs are still cheaper than China and still cheaper than robots. Just look at four or five states in India that are now growing at a remarkable pace in export manufacturing. If the other states could do the same, India gets lifted up despite its late start. Look at Vietnam. And look at those that haven't yet started that game. Courting investment, making land available without encumbrances, sorting out labor regulations so that workers can get into a job and move up the ladder is critical. Green energy is another opportunity with many multiple benefits. First, of course, it provides for energy security and it allows you to take part in the AI economy because you need energy. But it's also a job multiplier because renewable energy generates three times as many jobs as fossil fuel energy for the same million dollars that you put in. Third opportunity, to think of the jobs that can be enhanced by AI and are very unlikely to be displaced. Healthcare is a huge opportunity in the developing world. The opportunity to just get anywhere close to advanced country standards. You don't have to be terrific. You don't have to get right to the top. Just close half the gap even means many more doctors and nurses and community health workers. The proportion of nurses per capita in the developing world is 1/5 to 1/10 the level of the advanced countries. Huge potential. In India, community health workers are now a major part of the health system because they're helping to do immunization, they're helping to do maternity health, they're helping to educate families. So, it's a whole set of jobs in health care that can be created. Finally, I just want to make one point which cuts across sectors. AI is going to blur the distinction between being an employee and being an entrepreneur. The rise of the one-person company, so to speak. A company that starts with one person, then it has a few more brought into it. Is a new phenomenon that's already coming up in many parts of the developing world and the developed world. And we've got to encourage that because that too is a new form of employment, but it's different. You have agency. Small players using AI can reach markets and access technology much, much more feasibly and efficiently and at a low cost compared to before. What can we do about it? If you look at Zhejiang province in China, they now have a service alliance for one-person companies, providing a range of shared services including AI models, including sources of energy, and including some professional services. So, that's itself a new growth sector in the job market and we've got to encourage it.

Thank you, uh, President Tharman. It's interesting you mentioned health care there because we do have a speaker who's using small AI in India coming up in the program. So, uh, we'll get more on that uh, later. Thank you. Uh, Kristalina, now over to you. Um, what's the economic impact on all of this, particularly of course in developing countries as we've heard?

Thanks very much. What if he got it wrong? And told you to do something else?

Well, I'm going to call you president of the World Bank right now. So, that's not an AI instruction. So, let's go back to you then, Ajay. So, we heard from Kristalina there talking about the economic opportunities, particularly in developing countries. I want to pick up on what this event is all about in some ways, which is small AI in developing countries. Um, what does it look like in practice, Ajay? And how can it create jobs and opportunity? And I think just picking up from what Kristalina said about electricity and, you know, getting people online in the first place. How can small AI be accessible in the way that it's sort of developed? It's different, isn't it?

Yeah, so, you know, I've been very fortunate that President Tharman has helped us at the bank run what's called a jobs council. He's the co-chair of it, and we have a group of people from academia, from public and private companies, as well as from civil society. And we came up with five sectors that were critical to grow jobs, all of which are not reliant on outsourcing jobs from the developed world to the developing world, kind of the last 30, 40 years model, but instead build locally relevant jobs. And those five sectors connect to this conversation. And the first is infrastructure. It's construction, but then what it enables. Bridges, roads, airports, digitization, electricity, water, but also human infrastructure in terms of skilling and education and the like. The second is agriculture for small farmers. Most of our economies are very heavily dependent on food grown by small farmers. They get competed to the point where it's tough for them to play in the system, but access to cooperatives, access to technology of the type we're building. I'll give you a couple of examples. It enables them to get past that. The third one is primary health care and its delivery. President Tharman was just speaking to it, but think of Indonesia with primary health care centers, each with a midwife and a medical diagnostic technician. Think of the quality and number of jobs you create through that extension, and then use technology and AI to connect to remote doctors and the skills that may not be present in a primary health care center. And the fourth is tourism and what it can multiply in terms of jobs. I mean, Thailand is a principal example, but they've got ambitions to go even further than what they've done this far. And then manufacturing, particularly value-added manufacturing, which is not pure extraction, but creation of local jobs. Minerals and metals, the emerging markets are gifted with large quantums of deposits, but they need real quality jobs. But also, look at Africa, the creative industries or the like. There's so many opportunities on manufacturing across. Whether you do this as an entrepreneur to President Tharman's point about individual agency or you do it out of working for others, all are possible. That's the idea. Examples. Health care clinic in Indonesia. You're the patient, I'm the nurse, you walk in, and you say I have a rash. And I look at it and I can't figure out what it is, but I can take a photograph and send it to the doctor I share with 20 other clinics.

I do actually have a rash right now.

Sorry about that, but I'll ask the doctor to intervene. And so, he in turn can either look at it himself or run it through a simple local AI tool developed at the edge of a 3G phone. Local, it's very simple to do. Get started and says, 'It's an eczema. Give her this ointment, tell her to go home.' Or, 'Don't like it, tell her to come to the regional hospital to meet the specialist in 14 days at 10:30 in the morning.' That is a very difficult thing to do even in the advanced economies today. Getting a specialist appointment organized digitally is a very hard thing to do. So, you can cut through the arbitrage of all this by creating simple AI tools. Another one, something we're doing right now with farmers in India and elsewhere, with Google's help actually. And I think the Google guys are on the right track. You're going to see this. The idea is a platform that's been built to connect farmers and cooperatives to better fertilizer, better seeds, better crop insurance, better credit, and most importantly, better off-take prices for their product. And so, an illiterate woman farmer in India can point a 3G phone at the back of a leaf and say, 'That disease, I don't need to know the name, but that disease equal to this insecticide.' Press the button because India has put bank accounts in everybody's hands. You press the button, 25 rupees, that insecticide at my cooperative. That's the kind of stuff that small AI is about. The reality is that large language models are estimated probably to create some degree of disruption in jobs of the developed world. But by the time you get to middle-income countries and lower-middle-income countries, the opportunity from small AI to build jobs and build productivity is enormous. That's what we are trying to do, and that's what the Jobs Council started out. In fact, the small AI word came from a Jobs Council meeting with President Trump.

Okay. Thank you. And I think what's interesting is that, Ajay, is the accessibility of it. Like you say, it can just be in someone's pocket. Countries like India where smartphones have a huge uptake. Um, we'll come to you next, Kristalina. Um, you've described AI as a tsunami for the labor markets, um, with young entry-level workers especially exposed. What should policy makers be doing now to ensure that it creates more opportunities rather than disrupt? And I know President Tharman touched on some similar areas already.

Okay, thank you, Kristalina. Well, you mentioned a lot of things that governments should do. Conveniently, we do have a president sitting next to you. Um, obviously you have a role in the jobs council as well. There's lots of really interesting points that Kristalina raised about fiscal issues, about skills in particular. And let me ask you about skills then. Cuz you've emphasized the need to invest in skills regularly. Um, particularly the importance of public-private partnerships. What do you think governments and businesses can do differently then to make lifelong skills development a reality? I mean, it really does pick up on what we've just heard from Kristalina, doesn't it?

Well, you know, the silver lining in jobs displacement by AI is that it's going to happen task by task. And that gives us time. It means that in the workplace, whilst people still have jobs, there's that opportunity to adapt, to learn new skills, to build on what you already know, and to stay agile. So, AI isn't as a tsunami isn't going to come all of a sudden, as Kristalina was saying. It's going to come gradually. It's going to be diffused throughout the economy, and we have time to adapt. What's critical is learning on the job. There is a particular problem that we have to address very soon, though. Across the world, advanced countries and developing. And that is that the first rung in employment, your first job upon graduation, is now seeing less demand. The evidence is already very clear. It's clearest in the most AI exposed sectors, by the way, which are the tech sectors. But it's becoming clear across the board. And what governments did during COVID and what some governments did during the global financial crisis now has to be thought of not as a crisis measure but as part of the menu of structural policies that governments have for a new era, which is that governments have to get involved with the private sector to make that first job possible. To make that pathway into the first rung of your career ladder possible. We can't afford to lose that. Because if you lose that, you not only have that cohort of young people waiting a year and then competing with the next cohort the next year, you lose more than one year of skills. You lose a whole set of intangible attributes, and you lose trust in the future. So, we've got to do now as part of public policy, ways of incentivizing the private sector to take in fresh graduates, so they get that first foot in the market, and they learn from there. That's one policy implication. But, I also want to work backwards from what's happening in the world of work. Work backwards into the tertiary education system, because I think there are two very important shifts that are required there. If you look around Asia, you look around the developing world, in fact, if you look around the advanced world, and you think about how you have to avoid that middle getting displaced, the middle of the workforce getting displaced, it means we also have to change how the middle gets educated, and the reality of tertiary education systems, higher education, universities, and the like, is that it's overwhelmingly academically focused. And here again, the data is already clear. The link between academic credentials and career prospects is weakening. In the US, for the first time in a century, demand for graduates has gone down. The wage premium that college graduates earned has gone down very significantly. So, we've got to take these as early signals of a new era. Work our way back into the education system, and reduce that skew that you currently find towards an academically oriented education, towards an education that's more applied, has more bursts of work as part of education, more attachments, more internships, more the dual model of education, where you spend serious time in a firm as part of your tertiary education. That's one major shift that has to be made. It's a critical issue for developing countries, where you still have to develop that applied education stream. But one final point I'll have to make is one that comes from what Kristalina said about the fact that we don't know what'll happen in 5 years time. We need some humility in amongst education planners. Don't fixate on one particular model of education, particularly on the issue of depth versus breadth. We don't know what'll be required. The reality is in some areas you need great depth of expertise to be able to learn continually on the job. And there'll be other areas where you need more breadth of exposure to different disciplines because you have to integrate AI across functions. You've got to do many other jobs that require a breadth of human skills. But there is no one model. So, don't fixate on one model. Have some humility. Be clear-eyed enough to realize that the future is fuzzy. And agility is the way we have to go, not just for firms and industry, but agility in education itself.

Okay. Thank you so much, President Tharman. We've run out of time, but it was really interesting to talk to you. I wonder if just in two words you can just give me or three words, um, a quick fire thought on where you think things are going to be. You mentioned 5 years. Things are going to change a lot. What would you like, your one big goal in 5 years time. Start with you, Ajay.

A realistic application of small AI in as many emerging markets as possible. That's what I'd like to get to.

Thanks, Ajay. President Tharman.

Well, avoid a new divergence between the developing world and the advanced world and see continued convergence as we've seen in East Asia, especially, using AI, using technology, and developing the best of human capabilities.

Okay. Thank you, and last but not least, Kristalina.

Thank you so much for your time, all of you. A round of applause, everyone. Thank you, Ajay. Thank you, President Tharman. And thank you, Kristalina.

I'm sure you'll all agree that was a really thought-provoking beginning to this discussion looking at the issue of skills and education when it comes to the future of AI, and of course, looking at technology and infrastructure as well. Now, one of the central challenges around almost every conversation anyone has about AI is trust. The question's often asked, 'Is it safe to use? And do people really have the confidence to use it? Who's accountable? And will our AI support people rather than replace them?' Well, our next speaker has spent years working at the intersection of technology, trust, and responsible innovation. Please give a warm welcome to Tiffany Xingyu Wang, the national best-selling author of The Trust Code and the founder of Songsheets AI. Over to you, Tiffany.

Thank you. Hello, everyone. Thank you. So, in Silicon Valley, we talk in big numbers. Trillions of dollars for one company, billions of parameters for one model, and of course, gigawatts of power to sustain both. But today, the most debated question is, should we trust it? Oh, trust is the most overloaded and under-defined notion. It can mean alignment, safety, security, privacy, and before the list ends, we have already lost you. So, what should you know? AI can sound confident while being wrong. It can feel intimate without being responsible. It can act authoritative without being accountable. And behind every answer and every action lie the objectives you may never see. The engagement metrics, the revenue goals of AI labs, and with more evidence showing machines' own drive to survive. So, being capable does not mean being beneficial. The central question today is not whether to trust AI, but how to trust AI wisely. I've spent a decade building AI and the digital trust systems, and the last 2 years researching for the book The Trust Code: How to Unlock AI's Promise Without Losing Humanity. And there are two things for you to remember. One, trustworthiness is about machines, and trust is about us. Every single frontier model today has failed safety tests. Every single one. So, it is our responsibility to hold AI labs accountable for the potential liabilities, and build a scaffolding for safer deployment. Second, the biggest risk of AI is not replacing us, but persuading us to surrender our judgment without even noticing it. And which brings me to this room. Here I see the true promise of AI, and you are the stewards. There are three judgment calls I want you to think of as you walk into every single discussion about how to invest in, build, and deploy small AI in the most needed places in the world. First, model diversification. East or west, closed or open, the differences in performance for those models barely matters for the jobs small AI tries to do. Do not depend on one model because one day the investors' subsidies might dry up, and AI labs' incentives might stop aligning with yours. Your freedom to walk away is your path to sovereignty. Second, own your context. 2.2 billion people are not yet connected online. That means the big models have learned the world without them. They barely understand your languages, your laws, your crops, your patients. And their blind spot is your advantage. Rent the intelligence, but own the context. No training on your data, no data kept longer than needed. Technology scales, but the context doesn't. So, you have to keep your context. And third, build the human infrastructure, which the panel mentioned during the talk. AI will reach the people experts never did. Think of the patients without specialists, farmers without agronomists. But you need the people who understand how to trust it, how to verify it, when to refuse it. If in 10 years the job of your workforce is simply to click a button to approve AI, then we miss the entire point. Look, trust is built one judgment at a time. Which models? Whose data? Which skills? And these judgments are yours. Your stewardship. Now, let us bring AI to the most needed places. And that's the race of trust you can win. And with that, thank you everyone for your time. Thank you.

A different kind of AI is changing someone's life right now, somewhere in the world. This is small AI, already at work. AI that tells you when to water your crops, what's harming them, and when to harvest.

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So, thanks once again for Tiffany's talk there. And we heard from her, didn't we, about why trust matters. Before that, we heard from leaders about their vision for small AI. Which leads us on to our next question and panel. Um, if you want to stand in front of your chairs and I'll introduce you one by one. Um, and the question we're looking at is how do you make small AI work on the ground? What does it take to scale up

Initiatives, and how do countries build the infrastructure and skills to make it all work? Well, they're already on stage, as you can see, surrounding me. And our next panel brings together a diverse range of perspectives on exactly those questions. So, I'm joined by people from governments, tech companies, and philanthropists. A round of applause for our next panel, everyone.

So, I'm just going to introduce you all. We first have Jimena Londoño, Minister of Science, Technology, Knowledge, and Innovation of Chile. We have Pierre Laporte, His Excellency, Minister for Finance, Economic Planning, Trade, and Investment from the Seychelles. On the other side of the circle, we have Vilas Dhar, the President of the Patrick J. McGovern Foundation. Doron Avni, Vice President, Public Policy and Emerging Markets at Google. And last but not least, Kathleen Reen, Head of Global Affairs and Market Development at Space AI. Welcome to all of you. We have about 20 minutes or so to discuss what is a really interesting issue because in the end we've heard from the presidents, haven't we? The heads of the World Bank and of course the IMF and the president of Singapore giving their big visions, but this is looking at the practical now in a way.

So, we'll start with you, Minister Ximena. You're speaking today as a government minister, but what struck me actually is your previous lives as well. You've been a school teacher, you founded a number of AI tech companies as well. With all of that experience, how do you think AI can be used to raise productivity and create jobs for young people? Of course, something we were just discussing in that earlier panel.

Well, AI gives us a very large number of new jobs, and some of the new jobs can be trained quickly. Data annotator, for example, is one of them. It's needed for a very large number of applications, and it can be trained in four or five months. And so, it's really reshaping how education works. Unfortunately, in the education world for a long time we've been saying that schools have not changed. Everything has changed, and we keep educating kids the same way. And so, my takeaway is that we're not going to change. Unfortunately, some of these manners, but we can change how we change the paradigm on not thinking about education sequentially, where you study first and then you go to work. In countries like Chile, where university has become the most respected way to have a career, we have to also break the paradigm that a technical education is also a solid alternative.

Halonen was talking about, wasn't it? And Kristalina as well. The idea that actually the education could come at different stages in a way.

Absolutely. It's a continuum to life. For a long time we've been talking in the world about the future of work. Unfortunately, Chile didn't anticipate that change. So we have almost 10% of unemployment. So we don't have a lot of time right now to make dramatic changes that can take a long time. We have to be quick in training people quickly. We have made a lot of good synergies with the private sector, with Google, Amazon Web Services, doing quick certifications, but apprenticeships that have been a huge factor in innovation in Switzerland. The US also adopted them some years ago. They provide this alternative education of learning by doing. Very well paid in Latin America has not been something exposed. So we're trying to make a lot of quick changes that get us there. But hands-on learning has to be the new sexy, and not necessarily just going to school for 16, 18, sometimes even 20 years, and then at the end just work. So we have a very good number also of people with very high degrees that are unemployed.

Okay.