How AI Drives Innovation and Economic Growth

20 Feb 2026 13:00h - 14:00h

Session at a glance

Summary

This discussion, moderated by Jeanette Rodrigues at the Bharat Mandapam, focused on how artificial intelligence can either narrow or widen development gaps between countries, particularly examining opportunities and challenges for emerging economies like India. Johannes Zutt from the World Bank opened by highlighting AI’s potential as a game-changer for developing nations, noting that 15-16% of jobs in South Asia show strong complementarity with AI, enabling workers to enhance their skills and effectiveness across sectors like agriculture, healthcare, and finance.

The panelists explored the concept of “small AI” – practical, affordable, locally relevant applications that work with limited infrastructure – as opposed to large foundational models concentrated in the US and China. Michael Kremer emphasized AI’s potential to provide public goods like weather forecasting and digital identity systems, citing India’s success in distributing AI weather forecasts to 38 million farmers. Anu Bradford discussed regulatory approaches, comparing the EU’s rights-driven framework with other models, while debunking the myth that regulation necessarily stifles innovation.

Ufuk Akcigit raised concerns about market concentration in AI’s foundational layer, noting worrying trends of talent migration from academia to large tech companies and the shift from open to protected science. Iqbal Dhaliwal stressed the importance of evidence-based evaluation of AI interventions, highlighting examples where promising technologies failed due to trust issues or inadequate adaptation of existing systems.

The discussion revealed both optimism about AI’s transformative potential in healthcare, education, and government services, and significant concerns about labor market disruption, market concentration, and the risk of humans becoming overly dependent on AI systems. The panelists concluded that realizing AI’s benefits while mitigating risks requires careful policy design, robust governance frameworks, and continued investment in human capabilities alongside technological advancement.

Keypoints

Major Discussion Points:

– AI’s Dual Potential for Development: The discussion centered on how AI could either narrow or widen development gaps, with particular focus on “small AI” – practical, affordable, locally relevant applications that work in environments with limited connectivity and infrastructure, versus large foundational models that require significant resources.

– Market Concentration vs. Democratization: A key tension emerged between AI’s democratizing potential at the application layer (where small businesses can access previously unavailable tools) and concerning concentration trends at the foundational layer, where high barriers to entry in compute, data, and talent are creating oligopolistic conditions.

– Real-World Implementation Challenges: Panelists emphasized that successful AI deployment requires addressing fundamental systemic issues – from basic infrastructure (electricity, internet) to business environments, regulatory frameworks, and human adaptation. Technology alone cannot solve problems without proper institutional support.

– Regulatory Sovereignty and Global Power Dynamics: The discussion explored how developing countries can maintain AI sovereignty when foundational technologies are concentrated in the US and China, examining different regulatory approaches (US innovation-focused vs. EU rights-driven) and their implications for emerging economies.

– Evidence-Based Evaluation and Scaling: Strong emphasis on rigorous testing of AI interventions, moving beyond technological capability to measure actual user impact, scalability, and continuous improvement, with multiple examples of promising pilots that failed to scale due to political economy factors.

Overall Purpose:

The discussion aimed to provide policymakers in developing countries with practical guidance on harnessing AI’s benefits while mitigating risks, moving beyond both utopian and dystopian narratives to focus on real-world implementation challenges and opportunities.

Overall Tone:

The tone was notably optimistic yet pragmatic, described as representing “hope” rather than the “fear” that characterized earlier AI summits. While panelists acknowledged significant risks around market concentration, job displacement, and governance challenges, they maintained a constructive focus on actionable solutions. The conversation remained consistently grounded in empirical evidence and real-world examples, avoiding both technological determinism and excessive pessimism.

Speakers

Speakers from the provided list:

– Jeanette Rodrigues: Moderator/Host of the panel discussion

– Johannes Zutt: World Bank representative (referred to as “John” in the discussion)

– Ufuk Akcigit: Macroeconomist, working on World Development Report 2026 on AI and development with the World Bank

– Michael Kremer: Nobel Prize winner, involved with Development Innovation Ventures and various AI development initiatives

– Anu Bradford: Legal scholar/academic based in the U.S., originally from Europe, specializing in AI regulation and policy

– Iqbal Dhaliwal: Works at J-PAL (Abdul Latif Jameel Poverty Action Lab), former civil services exam topper in India, focuses on evidence-based policy interventions

Additional speakers:

None – all speakers mentioned in the transcript are included in the provided speakers names list.

Full session report

This comprehensive discussion at the Bharat Mandapam, moderated by Jeanette Rodrigues, brought together leading experts to examine one of the most pressing questions in international development: whether artificial intelligence will narrow or widen the development gap between nations. The panel featured Johannes Zutt from the World Bank, Nobel laureate economist Michael Kremer, macroeconomist Ufuk Akcigit, legal scholar Anu Bradford (originally from Europe but based in the US), and development practitioner Iqbal Dhaliwal (a civil services exam topper turned researcher), each offering distinct perspectives on AI’s transformative potential and inherent risks.

Rodrigues noted this represented the fourth AI summit, following previous gatherings including the first in the UK, and observed a notable shift from fear-based discussions in earlier summits to the hope-focused approach evident in India’s “AI for all” objective.

AI’s Transformative Potential for Development

Johannes Zutt opened the discussion by positioning AI as a potential game-changer for emerging markets and developing economies, presenting evidence from the World Bank’s recent research in South Asia. The findings revealed that approximately 15-16% of jobs in the region demonstrate strong complementarity with AI, enabling workers to expand their skills and effectiveness rather than being displaced. This statistic challenges the common narrative of AI as primarily a job destroyer, instead highlighting its potential as a productivity enhancer.

Zutt described practical applications that illustrate AI’s democratising potential: farmers using AI to identify crop diseases and pests, nurses leveraging AI for diagnostic support in unfamiliar cases, and financial institutions employing AI to better assess borrower creditworthiness. These examples demonstrate how AI can fill critical skill gaps in healthcare, education, and financial services.

However, Zutt acknowledged significant challenges facing developing countries in harnessing AI’s potential. Basic infrastructure deficits—unreliable electricity, weak internet connectivity, limited digital literacy—create fundamental barriers to AI adoption. Many users may need to rely on voice-based interactions with basic devices rather than sophisticated smartphones.

The Small AI Revolution

Central to Zutt’s analysis was the concept of “small AI”—practical, affordable, locally relevant applications that address specific problems whilst working within constraints of limited connectivity, data availability, skills, and infrastructure. This approach contrasts with large foundational models that require massive computational resources.

Zutt emphasised that small AI represents the most promising pathway for developing countries, requiring bespoke solutions that help users conduct basic investigations using their phones, identify problems, find solutions, and connect with local resources. India emerged as a compelling example, with the world’s third-largest digital universe after the United States and China, built on strong foundations through digital identity programmes and payment platforms.

Market Concentration and Creative Destruction

Ufuk Akcigit introduced a crucial analytical framework distinguishing between AI’s foundational layer and application layer. At the application layer, AI democratises capabilities previously available only to large businesses, enabling small enterprises to access sophisticated tools. However, the foundational layer presents extraordinarily high entry barriers due to compute-intensive requirements and massive data needs, creating conditions prone to market concentration.

Akcigit presented empirical evidence of troubling trends: market concentration in the United States has been increasing since 1980, accelerating after 2000, with innovative resources increasingly shifting towards large incumbent firms. He highlighted a significant brain drain from academia to industry, with dramatic salary increases in industry accelerating after breakthrough moments in 2012 (image processing) and 2017 (foundational models). When researchers move to industry, their publication output drops significantly whilst patenting increases dramatically, representing a shift from open science to protected intellectual property.

Akcigit’s most provocative insight challenged AI’s development premise, questioning why entrepreneurship and dynamism were absent in emerging economies before AI’s arrival. He noted that firm size in developing countries was often best predicted by family size rather than competitive performance, suggesting AI alone cannot overcome deep-seated institutional barriers.

Public Goods and Government Investment

Michael Kremer provided analysis of market failures and government roles, arguing that whilst private firms develop profitable AI applications, critical public goods applications require government and multilateral support. He cited AI-powered weather forecasting as an exemplar: India’s distribution of AI weather forecasts to 38 million farmers demonstrated both scale of impact and public good nature of such services.

During an unpredictable monsoon season, AI forecasts accurately predicted early arrival in Kerala and southern India followed by unexpected delays—information that reached farmers when other sources failed. Survey evidence showed farmers responding by adjusting transplanting schedules and seed varieties.

Kremer also highlighted India’s digital identity system as a powerful example of government investment in AI-enabled public goods creating platforms for broader innovation. He referenced Microsoft Research India’s HAB program for driver’s licenses as another example of AI applications in traffic safety.

However, Kremer expressed concern about public sector adoption challenges, noting that government systems may resist AI technologies, potentially excluding the poor from benefits in public services.

Regulatory Sovereignty and Global Power Dynamics

Anu Bradford addressed how developing countries can maintain AI sovereignty when foundational technologies are concentrated in the United States and China, with DeepSeek representing China’s position in large language models. She argued that the Global South has the same incentives for regulatory sovereignty as developed nations but faces extraordinary implementation challenges.

Bradford’s analysis of the European Union’s rights-driven regulatory framework offers lessons for countries seeking to balance innovation with protection. Crucially, she challenged the conventional wisdom that regulation stifles innovation, calling this a “false choice.” Her analysis of Europe’s innovation gap identified four structural factors: lack of a digital single market across 27 jurisdictions, absence of robust capital markets (with only 5% of global venture capital compared to over 50% in the United States), legal frameworks discouraging risk-taking, and failure to harness global talent effectively.

This reframing suggests developing countries can pursue protective regulation without sacrificing innovation, provided they address underlying structural factors that drive technological development.

Implementation Challenges and Real-World Constraints

Iqbal Dhaliwal brought crucial field experience, emphasising that successful AI applications must be demand-driven and free up time for frontline workers rather than adding burden. His example of AI-powered essay feedback in public schools illustrated this principle—the technology eliminated routine tasks like correcting spelling errors, freeing teachers for higher-value activities like analytical thinking instruction.

However, Dhaliwal’s research reveals systematic implementation failures even when AI demonstrates superior laboratory performance. His most revealing example involved machine learning for tax collection in India: despite successfully increasing identification of fraudulent firms from 38% to 55% at low cost, officials refused to scale the programme because it threatened existing power structures by removing human discretion in enforcement decisions.

Evidence-Based Evaluation and Scaling

Both Kremer and Dhaliwal emphasised rigorous evaluation methodologies. Kremer outlined a four-stage framework: model evaluation (technical performance), user impact assessment (efficacy trials), scalability testing (effectiveness at scale), and continuous improvement systems. He referenced Development Innovation Ventures as an example of tiered funding approaches—small grants for pilots, larger grants for rigorous testing, and substantial funding for successful scale-up.

Future Risks and Opportunities: 2035 Predictions

In rapid-fire predictions for 2035, panellists identified both opportunities and risks:

Ufuk Akcigit expressed optimism about government productivity improvements but concern about labour market disruption, particularly for entry-level jobs that represent aspirational opportunities in developing countries. He highlighted a policy contradiction where governments incentivise AI adoption whilst taxing human employment through provident fund contributions and labour regulations.

Anu Bradford showed excitement about education and health improvements but worried about humans “getting dumber” by outsourcing thinking to AI systems. As an educator, she emphasised using AI to enhance rather than substitute human capabilities.

Michael Kremer was optimistic about health and education advances but concerned about public sector adoption failures that could exclude the poor from AI benefits in public services.

Iqbal Dhaliwal shared optimism about healthcare and education whilst worrying about market concentration preventing broad benefit distribution.

Johannes Zutt expressed excitement about targeted poverty reduction through AI-enabled individual-level interventions but warned that inadequate governance frameworks could enable serious abuses.

Balancing Hope and Pragmatism

The discussion successfully balanced optimistic potential with realistic assessment of implementation challenges. Unlike earlier AI summits dominated by fear about job displacement, this conversation maintained constructive focus on actionable solutions whilst acknowledging genuine risks.

The panellists’ diverse backgrounds provided complementary perspectives, with convergence on key issues like evidence-based evaluation, locally relevant solutions, and market concentration concerns suggesting robust foundations for policy development.

Conclusion and Policy Implications

The discussion revealed that AI’s impact on development gaps depends critically on policy choices made today. The technology offers genuine opportunities to leapfrog development challenges, particularly through small AI applications working within existing constraints rather than requiring wholesale infrastructure transformation.

However, realising benefits requires addressing structural issues predating AI: market concentration in foundational development, inadequate governance frameworks, institutional resistance to change, and policy contradictions favouring capital over labour.

The conversation suggests developing countries need not choose between innovation and regulation but must address structural factors driving technological development: market access, capital availability, talent retention, and risk-taking culture. Success requires coordinated action across infrastructure investment, regulatory frameworks, education systems, and labour market policies.

As Rodrigues observed in closing, noting the “messy human notes” visible on panellists’ screens, the experts weren’t outsourcing their thinking to AI—embodying the principle that AI should enhance rather than replace human capabilities. The choice between AI narrowing or widening development gaps remains open, contingent on the wisdom and effectiveness of policy responses implemented today.

Session transcript

Jeanette Rodrigues

all around the Bharat Mandapam. So once again, thank you very much for your time this afternoon and for choosing us to have a conversation with. To start off, I would like to introduce John, who will make some opening comments for the World Bank.

Johannes Zutt

So thank you very much, Jeanette. It’s a great pleasure to be here speaking to all of you this afternoon. Over the past week, we’ve heard from a lot of world leaders, tech leaders, experts from across many, many countries about how AI is fundamentally reshaping our world, presenting not just a technological shift but a structural transformation with profound implications for economies and societies everywhere. For emerging markets and developing economies, as for all economies, AI could be a game changer. So sorry, that probably helps. I thought the mics were on. So, you know, for all countries, but especially for emerging markets and developing economies, AI can be a game changer, a unique opportunity to leapfrog longstanding development challenges.

It offers clear opportunities to enhance growth and productivity. We recently did some work in South Asia at the World Bank Group to see what sort of impact AI was having on jobs in the region, and we found that approximately 15 or 16 percent of jobs here have strong complementarity with AI. AI enables people in those jobs to expand their skills and their effectiveness in delivering the products and services that they are trying to provide. It also helps, you know, very, very diverse groups of people in many, many different sectors of the economy. It helps farmers to identify pests on their crops. It helps farmers to identify pests on their crops, diseases in their crops, and also how to address them.

It helps farmers to identify pests on their crops, diseases in their crops, and also how to address them. It helps nurses to identify the ailments and illnesses that their patients may be suffering, particularly the ones that they’re not very familiar with, but that they can research using appropriate AI applications. It helps financial institutions to understand better the ability of borrowers to take on loans, which, of course, expands the ability of the borrower to expand his or her business. So there’s clearly enormous potential for AI to fill skill gaps in the areas that I mentioned, also in education, in health care services, to detect patterns, to generate forecasts, to guide the allocation of public resources, and so on.

Of course, at the same time, on the flip side, AI also creates a number of challenges. One of them is there will be some job losses, particularly sort of entry -level jobs that are very much knowledge or document -based, performing relatively rote work that can be taken over by automation. And we’re actually seeing this in the World Bank Group. We went and looked at the number – the types of jobs that we are advertising these days compared to a couple of years ago, and what we found is that that layer, sort of at the bottom of the professional classes inside the bank group, there’s just fewer of those types of jobs being advertised in the World Bank Group today than there were a few years ago.

At the same time, you know, particularly for developing economies and emerging markets, many of them are going to struggle to harness the potential that AI offers because of very basic issues around the foundations for effective AI use. They may not have reliable electricity. We can start with that very basic one. They may not have an internet backbone that’s sufficiently strong. People in these countries may not have very, very basic skills of literacy and numeracy that enable them to work effectively with higher end devices. They may need to use very, very basic devices, not even smartphones, and rely on voice communication, asking a question and hearing a response. So there may be struggles of that kind in developing countries and emerging markets.

And I’m not even talking about all the governance and regulatory safeguards that can also come into play. So the question, of course, is how can emerging economies, developing markets, harness the potential of AI and avoid the pitfalls? And for us in the World Bank group, we’ve been very, very focused on focused recently on basically small AI. Small AI meaning practical, affordable, locally relevant AI that addresses specific problems and also works where connectivity, data, skills, infrastructure are fairly limited. And this is extremely important in countries like India where all of those conditions can apply. And yet there’s tremendous potential for people to expand their, to grow their productivity if they have timely access to information of the right kind in their local language tailored to their specific circumstances.

So that’s what we are trying to do in South Asia today and across the globe actually. And this is really about some of the examples that I mentioned earlier, having bespoke… applications that help farmers to do very basic investigation of the types of issues that they’re facing using their phone to analyze what’s going on to identify it to find out how to address it even to find out who within their local area in their market space can help them by providing the tools or the products that are necessary to address whatever they’re running into so India of course is a very strong example of what’s possible India has been a leading country in digital innovation for quite some time after the United States and China it has the largest if you like digital universe you in the in the world today it’s got some very good foundations there’s the the digital identity program as well as the digital payment platform that currently exists.

There are lots of Indian firms that are innovating in AI, including in the small AI applications that I’ve been talking about. And the governments of India have an objective of ensuring that there is AI for all. So they are very, very aware of the challenges that need to be overcome to make AI accessible to a very, very broad spectrum of the population and not just the very rich that, to some extent, need assistance the least, right? It’s the poorer parts of the country that benefit the most because they will be leveraging a tool that they are not very familiar with and have not been using that much in the past. So we’re working in India.

We’re working in a lot of different states, Uttar Pradesh, Maharashtra, Kerala, Haryana, Telgana. these different aspects working with governments to work on the foundational elements, interoperability, making sure that the accessibility is possible, that programs can run offline as it were so that people who aren’t able to get online all the time can benefit and so on. And then we’re also working with private sector investors who are developing apps. I mean we’re not actually developing many apps ourselves. That’s not really in our comparative advantage. Our comparative advantage as the World Bank Group is to do the more advisory work, make sure that the backbone information that’s embedded in the application is reliable and trustworthy because of course that’s critical for ensuring successful uptake.

But we are helping governments to create. We are helping governments to create the space that enables experimentation in AI sandbox to develop the different applications that people in this incredibly creative country are coming up with to help people get on with their work and become more productive. So I think it’s important to recognize that if we’re going to make effective use of this tool, we need both a public -facing effort to address the standards and the other issues, the interoperability and so on that I mentioned before, but also a private -sector -facing effort because it’s the private sector that’s actually generating, creating most of these applications that are working, particularly in the small AI area.

We’re doing a little bit on bigger AI. There’s obviously a connection between the two. Big AI can, through computational power, generate new knowledge that can help us to do things that we haven’t done so well in the past much, much better. But for… There are countries like India translating that. into small AI will also be very, very important for uptake. So I’m looking forward to hearing from all the distinguished speakers in this panel about their thoughts on what’s happening today in this sector. So thank you very much.

Jeanette Rodrigues

Thank you very much, John. John spoke about, of course, the use cases for AI, and on the other side of the spectrum we have the large language models, we have the foundational AI. But no matter where you sit on the spectrum, no matter where your interests lie, AI, innovation never disperses and never diffuses equally. Today on this panel, I hope to unpack what determines whether AI narrows the development gap or whether it widens the development gap. Especially we are looking to talk about the real world. What should policymakers in the real world think about and keep at the top of their mind as they go ahead preparing policies considering AI? Before I start, just setting the stage.

To a man, to a woman, everybody I spoke with who’s attended the first AI summit to today, this is, I think, the fourth AI summit being held. The first one was held in the UK. And without exception, all of them made it a point to tell me how the first session was full of fear. It was, oh, my God, AI is this terrible technology which is going to steal all our jobs, make us redundant. And when they come to India, they see the hope that technology and AI brings. And that’s the spirit of the discussion this afternoon, to figure out how can we balance both of those extremes, hope and concern, and go ahead in a pragmatic, policy -first way to prepare for the real world.

So if I could start with you, Ufuk, how do you think about AI? And especially, where do you see areas of creative destruction? To foster the innovation that we need.

Ufuk Akcigit

Thank you very much. And so, of course, creative destruction is an important driver of economic growth in the long run. So that’s why, you know, it’s an interesting question how AI will affect creative destruction in general. Of course, we are at a very early phase of AI, and it’s a GPT. And typically, you know, when GPTs are emerging, there’s a huge surge of new businesses. And this should not be misleading. I think the main question we should be asking ourselves is what will happen to the creative destruction in the future? How does the future look like in terms of creative destruction? And I’m a macroeconomist, so that’s why I like to look at this with a, you know, bird’s eye view.

And I would like to, you know, separate advanced economies from emerging or developing economies. So when it comes to advanced economies, there, again, we need to split the issue into two layers. One, the foundational layer. and the other one is the application layer. When we look at the application layer, it’s great. You know, the entry barriers are low. Small businesses can do what only large businesses could do in the past, and, you know, they can do their accounting, marketing. You know, there are so many opportunities now. The entry barrier is low. As a result, this suggests that, you know, this is going to be more, you know, friendly for creative destruction on the application. But then there’s also the foundation layer, and I think that’s exactly where the bottleneck is.

When we look at the foundation layer, the entry barrier is really, really high, and, you know, the compute is very compute -heavy. It’s very data -heavy. It’s very talent -heavy. So as a result, you know, this market, at least this layer, is very concentration -prone. Of course, it’s very early. But, you know, normally we have to be concerned about the foundational layer and how things will pan out because this is the upstream to the application layer, which is downstream to foundation layer. So that’s why whatever will happen at the foundational layer will potentially spill over to application layer two. So that’s why I think we need to look at early indicators. But, you know, in the interest of time, I don’t want to go into the empirical evidence yet.

Maybe we can come back in the second layer. When we look at the developing countries, so I think, you know, I agree with Johannes. You know, I think AI is creating fantastic opportunities. So that’s why I think it’s really important to understand the opportunities as well as the risks for developing countries. And together with the World Bank, we are working on the world development. Report 2026, which is going to be on AI and development. And these are exactly the issues that we are focusing on. But I think before we go into those details, we should ask ourselves one major question. Why was there no entrepreneurship and dynamism before the AI revolution in emerging economies? Why was, you know, when we looked at the firm’s life cycle, for instance, why was it not up or out?

Why was it not, you know, very competition friendly? Why did the best predictor of firm size in emerging economies or developing economies was the size of the family and or the number of male children? These are still lingering issues and AI is not, you know, will not bring magic unless we understand and fix the business environment in these economies. You know, AI will just create new tools. But at the end of the day, we need to make sure that the business friendly environment is there for entrepreneurs to come and exercise their ideas

Jeanette Rodrigues

Ufuk, that’s a very interesting leaping of point, the real world. And the intention of this panel is to get exactly there. So if I may turn to you, quite literally turn to you, Michael, and ask you about the real world. You’re obviously doing a lot of work on the ground. Where do you see the potential for AI to spur gains? And are there any really transformative breakthrough areas that you’re looking at right now?

Michael Kremer

Yes. Thank you. Thanks very much. You know, I don’t want to minimize the existence of forces that may widen gaps. I think that if policymakers, primarily at the national level, but also in multilateral development banks, take appropriate actions and make appropriate investments, then I think AI has the potential to substantially narrow some of the gaps. And, you know, I think the… which policy actions to take can be informed by thinking through relevant market failures and relevant government failures. Let me give a concrete example or two. So private firms have incentives to develop and improve applications of AI that can generate profits. But there are some very important applications of AI for public goods, for example, that will not attract commercial investment to measure it with their needs.

And that’s an area where I think governments and multilateral development banks can play an important role. And I think some of this very much echoes what you were saying about small models, but also I’ll mention the link between the two. So an obvious example where I think India has been a leader for the world is in the development of digital identity. You know, this is… will enable, as Ufuk was saying, this enables a lot of work by individual entrepreneurs, a lot of other applications. So that’s a huge success, and I think multilateral development banks together with India can help bring that to many other countries. Let me take another example, one that’s not as well -known, but picks up on your comment about farmers.

So one thing that’s critical for farmers, they have to make a bunch of decisions that are weather -dependent. You know, when do you plant, for example? What varieties do you use? A drought -resistant variety, another variety. That, most farmers don’t have access to state -of -the -art weather forecasts around the world. I’m not talking about one country. In low – and middle -income countries, they don’t have access to that. Now, there’s a huge advance. We tend to think of large language models, but obviously AI is pushing science forward, and that includes in weather forecasting. There’s really a revolution driven by AI. But weather forecasts are non -rival. They’re largely non -excludable. They’re the classic definition of a public good.

So there’s a strong rationale for national governments, in some cases supported by multilateral development banks, to make investments in producing and disseminating AI weather forecasts. Again here, India is a leader. So if you, India in particular, in particular, India’s, the Indian government distributed forecasts to AI weather forecasts to 38 million farmers last year. And the evidence suggests that farmers, both from India, from this particular case, that in areas, I’ll say a little bit about last year’s monsoon, it came early in Kerala and southern India, but then there was an unexpected delay in the progression. The AI forecasts got that right, that was the only source of information that reached farmers with that. In the areas, we did a survey above that line, and farmers are responding, and they transplant more, they use hybrid seeds more.

Evidence from around the world is consistent with this. Farmers respond to these AI weather forecasts. So I think that’s one example, but many others, and happy to discuss them in education and traffic enforcement and elsewhere.

Jeanette Rodrigues

Michael, your answer should be read the book. Okay. We’ve spoken about the use cases of India, but setting up digital IDs, of course, is a sovereign decision. It’s something India could do unilaterally. When it comes to the large language models, that’s not reality. The large language models are concentrated in the US, in China now with DeepSeek. Anu, in a world where you largely have the rules being set by the two large powers, the US and China, arguably, there’s of course the EU as well, and you’ve done a lot of work on that. Who sets the AI rules for the Global South? Is there even the possibility for the Global South to talk about sovereignty?

Anu Bradford

So I think the Global South has the same kind of incentive for their own AI sovereignty, including then regulatory sovereignty, to design the rules that better work for their economies, for their societies, for what the public interest in these jurisdictions calls for. But regulating AI is really difficult even for very established bureaucracies. You need to be able to make sure that it is an innovation -friendly, and yet you at the same time need to be careful in managing the risks for individuals and societies. So even very established regulators like the European Union have found it one of the most challenging tasks to come up with the AI Act. So there’s probably something to be learned from these jurisdictions that have gone ahead and done the kind of thinking that had then resulted into some of those regulatory frameworks that we have now in place.

So if you think about the choices that India has when it looks around, one of them is to think about, okay, how does the EU go about this? The EU follows what I would call a rights -driven approach to regulation. So what is really characterizing this, the first horizontal binding, so economy -wide regulation that the Europeans enacted, it is a regulation that seeks to protect the fundamental rights of individuals, the democratic structures of the society, and that also seeks to ensure a greater distribution of the benefits from AI revolution. So the European approach is very conscious that it wants to also share some of the benefits so they don’t all go to the large developers of these models, but individual use as society at large.

smaller companies benefit from AI as well. So there’s something I think the Europeans can teach in terms of that regulatory approach in addition to maybe then some details of how that regulation in the end was constructed. But just one word, India is a formidable economy that doesn’t need to take a template and plug it into the economy as such. I think India is in a very good position to take the lessons that serves its needs yet make the kind of local modification and variations that are more reflecting the distinct priorities of this country.

Jeanette Rodrigues

Anu, before I turn to Iqbal, a quick follow -up question to you. As India makes its own rules, where does the trade -off lie between regulation and innovation?

Anu Bradford

So this is very interesting because often I am based in the U.S., but I’m initially from Europe, and these two jurisdictions are described as the U.S. develops technologies and the Europeans regulate those technologies. many ways does India want the innovation path or the regulation path? And I think there are many votes who would go for innovation. But I really would like to debunk this myth that to me it’s a false choice to say that the reason we don’t see these large language models being developed in Europe is not because there’s a GDPR, the General Data Protection Regulation. It’s not because there is AI Act. So the reason there is a perceived innovation gap between the United States and Europe is, I think, four things.

So first, there is no digital single market in Europe. It’s very hard for these AI companies to scale across 27 distinct markets. Second, there’s no deep, robust capital markets union. 5 % of the global venture capital is in Europe, over 50 % in the United States. That explains why the U.S. has been able to take much greater steps in developing AI technologies. Third, there are legal frameworks and cultural attitudes to risk -taking. You are not alone. You are not alone. You are not alone. You are not alone. You are not alone. You are not alone. You are not alone. You are not alone. You are not alone. You are not alone. You are not alone.

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And the fourth, I think, the sort of foundational pillar of the robust U.S. tech ecosystem is that the U.S. has been spectacularly successful in harnessing the global talent that has chosen to come to the U.S., including many Indian data scientists, engineers, who think that U.S. is the place where they can start their companies, scale their companies, fund their companies, U.S. universities can attract them. So the idea that choosing to follow… Or imitate aspects of the European rights protective regulation would come at the cost of innovation, we need to understand better what drives the technological innovation and whether regulation should

Jeanette Rodrigues

Thank you, Anu. Iqbal, turning to you. You’re working in an area of the world, South Asia, where what is regulation? What is enforcement? At the risk of sounding like a provocateur, it’s the Wild West a little bit. And therefore, we talk a lot in our part of the world about small AI, about targeted AI. My question to you is that what should policymakers keep in mind when designing AI -enabled interventions, especially when it comes to small AI and the targeted use cases?

Iqbal Dhaliwal

vulnerable public schools all the way from 11th to becoming the second best performing state in just a matter of two or three years. Phenomenal results, right? But then you start saying, let’s unpack this. What was this thing doing? The first thing that they find out is that a lot of people are like, oh, does this mean that I don’t need teachers anymore? No, you still need the teachers. What it replaces is the road task of the teacher having to correct spelling mistakes, calling you to the room and saying, hey, you forgot your comma, you forgot to capitalize. Instead, AI takes care of all of that. And now the teacher can sit with you in the free time and say, how did you set up the structure of this essay?

Did you think about this analytically or not? And that’s the first insight that comes from evaluation. It frees up the teacher time. Everything that we do in the field ends up adding to teacher’s time, adding to the nurse’s time, adding to the Anganwadi worker’s time. Very few teachers do that. Free up time. So if your AI application can free up the time of the health frontline workers, first of all, that’s a winner. The second thing that is really important here was that this is a demand -driven thing, right? Like there was a demand by the kids to improve their essays. There was a demand by the teachers to free up their time. But most importantly, there was a demand by the school districts to show progress.

So I think those is kind of a great example of how everything comes together if you think about it ahead of time.

Jeanette Rodrigues

Ladies and gentlemen, a topper of India’s notoriously difficult civil services exam. So take Iqbal more seriously than you would as just a normal.

Iqbal Dhaliwal

Thank you. I thought that was history now.

Jeanette Rodrigues

It’s never history in India, Iqbal. Michael, turning to you, almost as equal in accomplishment by winning a Nobel. What risks should multilaterals like the World Bank keep in mind? Or let me rephrase that actually. Is there a risk that multilaterals are moving too slowly relative to the technology?

Michael Kremer

I think there certainly is. As I noted before, there are certain areas where the private sector is going to move, but there are other areas where they’re not going to move quickly, and it’s going to be very important for governments and for multilateral development banks and for philanthropy to move. I think there are a number of approaches to this. One way is by encouraging innovation by setting up institutions like innovation funds, particularly evidence -based, to echo Iqbal, I think evidence -based innovation funds. So I’ll give you one example of something that I’m involved in. Development Innovation Ventures, that was initially set up in the U.S. government, but it’s now been relaunched independently. It has tiered funding, so there’s initially very small… grants to pilot new ideas.

Then there’s somewhat larger grants to rigorously test them as Iqbal emphasized and then for those that are most successful there’s funds to help transition them to scale up. I think why is that important? Well that’s important because if we’re thinking about the services that public services and there are other sectors where this is needed but there’s probably going to be insufficient competition. Private developers are going to come up with innovations but then there if they have to sell them to the government they’re facing a monopsonistic buyer. They’re not going to probably not going to get rich doing that. So some support to generate more in that market, generate more entrance in that market, well I think is very important.

It’ll also mean that prices will go down and quality will go up when the government does that thing. Does that. Let me, I’ll just again let me give a example of the potential of how you know we we tend to focus on certain examples time after time here let me give another another example that is you know something that I doubt many people here are thinking of when they think of AI you know one of the things that you know traffic safety and we’ve all been exposed to traffic in the past few days you know traffic is a real problem interfering with urbanization which may drive growth there are a lot of deaths from from traffic a lot of citizens around the world have very difficult and painful experiences with traffic enforcement well you know you can have automated traffic cameras that have the opportunity to improve improve traffic outcomes but also improve people’s perception of fairness in government India’s moving in this let me mention another thing that within traffic safety that’s being done Microsoft Research India developed a program called the India Research Program and it’s a program that’s been developed by the government and it’s a program called HAB that is for driver’s licenses and that it automatically uses AI to test are that are the drivers until they actually pass in their exams they when this was introduced it’s been introduced I believe in 56 sites across India hundreds of thousands of people have taken tests this way we took a leaf from a false book we followed up the we’ve got information from Ola on ratings on and the number of drivers who were rated as driving unsafely that went down 20 to 30 percent where hams had been installed so you know that’s something that was developed not by Microsoft’s main business but by Microsoft research we can just create some support for more ideas like that to be developed to be rigorously tested that can benefit India can benefit the whole world we are we are running out of time probably this is this is one place in in India where time is really respected and we have to end in time.

So I had a list of wonderful questions, but if I could now move to a space where we are really giving shorter answers and quick answers and the deeply, deeply interesting ones about who’s winning and who’s losing. Michael, if I could start with you, actually. We’ve seen many promising technologies fail to live up to their promise. How should we think when we are evaluating AI interventions? How should we think about it? What should be the metrics that we use? Okay. First, model evaluation. So AI companies typically do that part. How good is the model output for specific tasks? You know, forecasting the weather. Does it do a good job? Does it match your local language well?

Second, user impact. Here, I think there’s a role both for sort of initial pilots akin to a medical efficacy trial. If you put the work into trying it, does it lead to improvements and outcomes for the users? Second… scalability and usage at scale that’s more like an effectiveness trial in medicine that it’s important to think not just about the tech but also about the human systems are the teachers actually going to use the product I think is it is an example how can you get the teachers to use the product and then the fourth area is continuous improvement you want a system that improves the underlying models so I think in procurement we might want to think about requiring continuous a B test publicity about what the what the impact usages and impact is and perhaps even thinking about requiring open access as part of the procurement package

Jeanette Rodrigues

thank you Michael. Iqbal, I want to flip that question to you where do you see where do you see hype in the promises of AI that you don’t think will play out

Iqbal Dhaliwal

I think hype is natural because the technology is exciting. It’s a general -purpose technology. It’s evolving so quickly. The marginal cost of deployment for the next users is very low. It’s multimodal. Today you are doing it in text. Tomorrow you’re doing it in video. Day after tomorrow you’re doing it on audio. Everybody who has a smartphone has it. So I can understand the hype, right, like where it is coming from. But I think what we really need to do is separate the hype from the reality on the ground. And the reality on the ground is that many of these technologies are not having the final impact that we are having. And I see kind of two, you know, like once again my job at J -PAL always, you know, sitting at the top is like to say not worry about one professor’s evaluation or one researcher’s evaluation, but say when I connect all these dots, what am I seeing?

And I’m seeing two patterns. One is about trust in technology, and the second part is about the reality of the policy world. Let me elaborate quickly on both. Trust in technology. There are studies which found that even if you give doctors and frontline health care workers access to diagnostic tools, including radiology, tools, using AI, AI enabled prediction of the diseases, oftentimes it doesn’t lead to an improvement in results. And when you try and unpack that, even though this technology worked even better than the human intervention in the lab, right? So some of these diagnostic things can work, have better predictability in the lab, but in the field, they end up decreasing, not only is their efficiency lower, but it lowers the efficiency of the doctors, because we have not trained them enough important.

And the second thing is the enabling mechanism, the world around us. We just assume that just because the technology works, even if it works in the field, the rest of the system will adapt to it. No, you have to adapt the system to the rest of the world. So this example quickly comes from India, where, you know, we have a with one particular state government, we try to improve the collection of value added taxes, it’s called GST in India, there is a whole worry about bogus firms that are created to get these GST or value added tax thing. The machine learning algorithm is able to increase the probability of predicting a bogus firm from 38 % to 55 % in one shot at a very, very low cost.

When it came time to scale up this program by the government, they refused to scale it up because you think about it, you have taken away the discretion of the human to decide whether they should raid Michael’s firm or they should raid Iqbal’s firm. That is power. And if you haven’t thought through that point, what is the point of technology?

Jeanette Rodrigues

I won’t terrify anyone in the room by asking why they didn’t want to scale up this tech. But talking about weeding out the bad actors, talking about firm -level decisions, moving on to UFOOC, does the firm -level evidence show productivity gains diffusing evenly across?

Iqbal Dhaliwal

So just going back quickly to the question of the firm. In the earlier model that I highlighted, I think it’s important to understand what’s happening at the upstream. so that we can then understand where things will be going in the future. And the evidence there, the early signs, is a bit worrying. So first of all, when we look at, for instance, the dynamism or market concentration in the U.S., market concentration has been increasing since 1980 but in an accelerating way after 2000. So that’s the first set of evidence. The second set of evidence comes from how innovative resources are allocated across firms. And when we look at the inventors who are creating the creative destruction and technologies, there’s a massive shift towards market incumbents.

And when I say incumbents, those firms that have more than 1 ,000 employees. In around 2000, 50 % of employees used to work for incumbent firms in just 10 years. That shifted. To more than 60%. A massive reallocation of innovative resources. And the final piece of evidence, and we are going to release this study next week, we looked at the universities, how AI is impacting universities, and we look at the AI publishing scientists. And AI publishing scientists in academia, the top 1%, used to make around $300 ,000 in 2000. It went up to $390 ,000 over two decades. Similar people in industry used to make around $550 ,000. Now it went up to $2 million.

And there has been two breakpoints. One of them was in 2012. The other one was in 2017. Of course, image processing and then the foundational model revolution in 2017. The more worrying part about this, which brings me back to the foundational model side of things, is that this created a massive out -migration from academia to industry.

Ufuk Akcigit

And after 2017 especially, B2B. When the compute and infrastructure became so important. And then we saw the rise of AI. The target or the destination is large incumbent information companies, which again highlights where things are going in terms of the concentration. And the worrying part also is that when people are moving to industry from academia, their publication record goes down by 50%. They start patenting by 600 % more after they move, which means that we are moving from open science to more protected science. Now, spillover is extremely important for creative destruction, for the future of innovation. So that’s why, and if we will keep the foundational layer contestable, I think that the fundamental players there will be universities.

And keeping universities in a healthy way is extremely important, but there is very little discussion on this, which I think before it gets too late. Because once you start buttoning the wrong button, and then the rest will follow wrong as well. So that’s why I think we have to have this frank conversation early on in the game, otherwise it might… too late.

Jeanette Rodrigues

Ufuk, what you spoke about boils down to something Iqbal mentioned as well, power. Because power still makes decisions in this world today. So Anu, before I move to the final section of this panel, if I could ask you if the finance minister of a developing country let’s say India, comes to you and asks you, Anu, how should I think? What would you tell her?

Anu Bradford

So today if you think about how much political power but also geopolitical power is shaping our conversations around AI it is something where I think each country is now pushed towards greater techno -nationalism, techno -protectionism AI sovereignty has become almost a sort of uniformly goal for everyone. But I would remind even when encountering players like the United States and China that nobody in today’s world will be completely sovereign when it comes to AI space. If I just take one layer of the AI stack as an example. What is now driving a lot of the global AI race is this idea that we want to do frontier AI we want to have these powerful foundation models.

That means you need to have a lot of computers. You can’t have a lot of compute unless you have access to the high -end semiconductors. The U.S. is well positioned there. It is hosting companies like NVIDIA. The U.S. leads in the design of semiconductors. But who is manufacturing them? We really need to think about the role of Taiwan there. But then the Europeans have ASML in the Netherlands that leads in the high -end manufacturing with the equipment needed for manufacturing. But that is dependent on chemicals where Japan is leading. And the entire supply chain relies on raw materials from China. So ultimately, all these choke points can in principle be weaponized, but that is not ultimately a sustainable strategy.

Even President Trump had to walk back some of the export controls to China because Chinese were saying, okay, then the raw materials are not coming your way. So there are the potential ways to weaponize these interdependencies that ultimately make us all poorer. So as a finance minister of India, when approaching other middle powers, the great powers,

Jeanette Rodrigues

Easily said than done. Our final, final section is, of course, the rapid fire round. We all love this in this room. In one sentence, in one sentence, if I could ask all of you, and Johannes, you’re not getting away easily, you’re going to answer this as well. So in one. if I could ask you, we’re sitting in New Delhi 2035. Could you predict one development outcome that will have dramatically improved with the use of AI and one risk we’ll regret not addressing now? I guess you already know my second answer.

Iqbal Dhaliwal

I think the concentration, the future of market concentration is something that we should be concerned about and we might regret not having discussed this sufficiently in 10 years. On what will change in a positive direction, clearly health care and education, I think. It’s a no -brainer.

Jeanette Rodrigues

Anu?

Anu Bradford

So first of all, it’s so inspiring to hear all the use case examples, whether we talk about traffic or agriculture or education, because I often talk about the risks and the downsides, so it’s a really good reminder. I’m personally very excited, especially what happens in the education space but also in the health space. In terms of the risks, I think one thing that we are not paying attention to, and what I would even call a systemic risk, is the idea that many worry about AI getting almost too smart. But I am more worried about us getting dumber as a humanity. There is a temptation to start skipping steps, outsourcing your thinking and your creativity to these models.

And as an educator, when I think about how I will teach my students to use generative AI to enhance but not substitute their capabilities, we will just make a tremendous mistake if we just forewent that hard work, that beautiful moment of thinking hard problems and creating and investing in our own capabilities. And all that just cannot be so outsourced, because otherwise we don’t even know what kind of questions we should be asking the AI going forward.

Jeanette Rodrigues

Michael.

Michael Kremer

I agree that there is huge potential in health. and education. I think we’ll see big improvements in that, but the risk is that the public sector won’t adopt these, and therefore the poor won’t have access to them. And that’s because the public sector, as Iqbal indicated, the government systems and the government workers may not adapt to use these. There’s also risks of copycat regulation that are over -focused on certain problems that other countries may be worrying about, but might not be relevant for emerging economies. And then final risk is that the procurement systems are just set up in such a way that we don’t get sufficient competition, we get lock -in, and then we just don’t wind up with good quality.

Jeanette Rodrigues

Thank you, Michael. The buzzer’s down, but I’ll take a risk and quickly run through the other.

Ufuk Akcigit

Yes. I think I am much more optimistic about the government actually adopting this thing. Whether it is when you call 100, your call is going to get answered very quickly. The PCR van is going to be at your house much faster. The hospitals are able to be able to link your health record. So I think the government sector productivity is going to improve leapfrogs. The biggest risk, I think, is definitely the labor market. If there was a dial where I could slow down the adaptation and give time to the labor market to catch up, that’s my biggest worry. You talked about entry -level jobs. An entry -level coding job might be an entry -level job in the United States.

It’s the aspirational job that created Gurgaon’s and Noida’s and Mohali’s of this country. And those people are going to be running out of jobs very, quickly. And I think the labor market, whether it is ESI, Provident Fund, Gratuity, we are piling on and making it harder and harder to hire labor. when, on the other hand, capital is not taxed. We are giving incentives to people to use AI, and we are taxing them through provident fund and labor market regulations to hire labor. And I think that, for me, is the biggest risk, actually.

Johannes Zutt

So I think that for the first time in human history, we may actually have the tools available to enable us to target poverty reduction, poverty elimination initiatives on individuals. And that could be tremendously transforming. But at the same time, I do worry that we will not get the governance right or we won’t be able to make that governance sufficiently robust to prevent abuses.

Jeanette Rodrigues

Thank you very much to all of our panelists and to you for your time and attention once again. I had the very rare fortune of being able to peek into Michael’s screen while he was speaking, and I saw all the messy human notes. Our panelists are definitely not outsourcing their thinking anytime soon, and thank God for that. Thank you, ladies and gentlemen

Johannes Zutt

Speech speed

141 words per minute

Speech length

Risks of AI widening inequality and labor market disruption

Topics

Human rights and the ethical dimensions of the information society | Artificial intelligence | The enabling environment for digital development

Ufuk Akcigit

Speech speed

163 words per minute

Speech length

Risks of AI widening inequality and labor market disruption

Topics

The digital economy | Human rights and the ethical dimensions of the information society

Michael Kremer

Speech speed

160 words per minute

Speech length

Risks of AI widening inequality and labor market disruption

Topics

Social and economic development | Human rights and the ethical dimensions of the information society

Anu Bradford

Speech speed

199 words per minute

Speech length

Policy, regulation, and AI sovereignty

Topics

Artificial intelligence | Human rights and the ethical dimensions of the information society | The enabling environment for digital development

Iqbal Dhaliwal

Speech speed

183 words per minute

Speech length

Risks of AI widening inequality and labor market disruption

Topics

Artificial intelligence | Social and economic development | The digital economy

Jeanette Rodrigues

Speech speed

174 words per minute

Speech length

1039 words

Speech time

356 seconds

Policymakers need to keep AI‑enabled interventions in mind

Explanation

Jeanette asks what policymakers should prioritize when designing AI‑enabled programs, emphasizing the need for clear guidance and focus on impact.

Evidence

“My question to you is that what should policymakers keep in mind when designing AI‑enabled interventions, especially when it comes to small AI and the targeted use cases?” [61]. “What should policymakers in the real world think about and keep at the top of their mind as they go ahead preparing policies considering AI?” [170].

Major discussion point

Policy, regulation, and AI sovereignty

Topics

The enabling environment for digital development | Artificial intelligence | Policy design

Agreements

Agreement points

AI has transformative potential for healthcare and education sectors

Speakers

– Johannes Zutt
– Ufuk Akcigit
– Anu Bradford

Arguments

Arguments

Focus should be on ‘small AI’ – practical, affordable, locally relevant AI that works with limited infrastructure

Private firms develop profitable applications, but public goods applications need government and multilateral support

Summary

Both speakers emphasize the importance of practical, locally-relevant AI solutions that can work within the constraints of developing country infrastructure and address specific local needs

Topics

Artificial intelligence | Information and communication technologies for development | Closing all digital divides

Similar viewpoints

AI presents significant opportunities for development if implemented thoughtfully with appropriate support systems and policy frameworks

Speakers

– Johannes Zutt
– Michael Kremer
– Iqbal Dhaliwal

Arguments

AI offers opportunities to leapfrog development challenges, with 15-16% of jobs in South Asia showing strong complementarity with AI

AI has potential to substantially narrow development gaps if appropriate policy actions are taken

AI applications should free up time for frontline workers rather than adding to their burden

Topics

Artificial intelligence | Social and economic development | Information and communication technologies for development

Structural factors like market access, capital availability, and talent retention are more important for innovation than regulatory constraints

Speakers

– Anu Bradford
– Ufuk Akcigit

Arguments

Innovation vs. regulation is a false choice – Europe’s innovation gap stems from market fragmentation, capital constraints, and talent issues, not regulation

There’s concerning migration of AI talent from academia to industry, reducing open science and increasing protected patents

Topics

Artificial intelligence | The enabling environment for digital development | Capacity development

Successful AI implementation requires addressing institutional and governance challenges, not just technical capabilities

Speakers

– Iqbal Dhaliwal
– Johannes Zutt

Arguments

Technology deployment requires addressing power dynamics and institutional resistance to change

Governance and regulatory safeguards are critical challenges, especially for developing countries

Topics

Artificial intelligence | The enabling environment for digital development | Capacity development

Artificial intelligence | Human rights and the ethical dimensions of the information society | Capacity development

Overall assessment

Summary

The speakers demonstrate strong consensus on AI’s transformative potential for healthcare and education, the importance of addressing market concentration concerns, challenges in public sector adoption, and the need for locally-relevant AI solutions. There is also significant agreement on the importance of evidence-based evaluation and addressing institutional barriers to implementation.

Consensus level

High level of consensus across diverse perspectives (World Bank, academia, legal scholarship, development practice) suggests robust foundation for policy development. The alignment between theoretical concerns and practical implementation challenges indicates that policy frameworks addressing these shared concerns could gain broad support across different stakeholder communities.

Differences

Artificial intelligence | Human rights and the ethical dimensions of the information society | Capacity development

Unexpected differences

Regulation versus innovation trade-off

Speakers

– Anu Bradford
– Implicit assumption by others

Arguments

Innovation vs. regulation is a false choice – Europe’s innovation gap stems from market fragmentation, capital constraints, and talent issues, not regulation

Explanation

Bradford’s strong rejection of the regulation-innovation trade-off is unexpected given the common assumption that regulation stifles innovation. Her detailed evidence about Europe’s structural issues rather than regulatory burden challenges conventional wisdom about AI governance.

Topics

Artificial intelligence | The enabling environment for digital development | The digital economy

Trust in AI technology implementation

Speakers

– Iqbal Dhaliwal
– Other speakers

Arguments

Many promising AI technologies fail due to trust issues and inadequate adaptation of surrounding systems

Explanation

While other speakers focus on technical capabilities and policy frameworks, Dhaliwal’s emphasis on trust and human factors as primary barriers to AI success is unexpected. His examples of doctors not using superior AI diagnostic tools due to trust issues reveals a different dimension of implementation challenges.

Topics

Artificial intelligence | Capacity development | Social and economic development

Overall assessment

Summary

The speakers show broad agreement on AI’s transformative potential for development but significant disagreements on implementation approaches, risk priorities, and institutional capabilities. Key tensions exist between optimistic and cautious views of public sector adaptation, different prioritization of risks (labor displacement vs. human dependency vs. market concentration), and varying emphasis on technical solutions versus institutional reform.

Disagreement level

Moderate disagreement with high implications – while speakers share common goals of harnessing AI for development, their different approaches to risk management, implementation strategies, and institutional capabilities could lead to very different policy recommendations and outcomes for developing countries.

Partial agreements

All speakers agree AI has tremendous potential for developing countries, but disagree on implementation approaches. Zutt emphasizes small AI solutions, Kremer focuses on public goods applications needing government support, while Akcigit stresses the need to fix underlying business environments first.

Speakers

– Johannes Zutt
– Michael Kremer
– Ufuk Akcigit

Arguments

Focus should be on ‘small AI’ – practical, affordable, locally relevant AI that works with limited infrastructure

Private firms develop profitable applications, but public goods applications need government and multilateral support

AI creates fantastic opportunities for developing countries but requires fixing underlying business environment issues

Topics

Artificial intelligence | Social and economic development | The enabling environment for digital development

Both speakers recognize the importance of governance and institutional factors in AI implementation, but Bradford focuses on regulatory sovereignty challenges while Dhaliwal emphasizes power dynamics and resistance within existing institutions.

Speakers

– Anu Bradford
– Iqbal Dhaliwal

Arguments

Global South has incentive for AI sovereignty but regulating AI is difficult even for established bureaucracies

Technology deployment requires addressing power dynamics and institutional resistance to change

Topics

Artificial intelligence | The enabling environment for digital development | Human rights and the ethical dimensions of the information society

Similar viewpoints

AI presents significant opportunities for development if implemented thoughtfully with appropriate support systems and policy frameworks

Speakers

– Johannes Zutt
– Michael Kremer
– Iqbal Dhaliwal

Arguments

AI offers opportunities to leapfrog development challenges, with 15-16% of jobs in South Asia showing strong complementarity with AI

AI has potential to substantially narrow development gaps if appropriate policy actions are taken

AI applications should free up time for frontline workers rather than adding to their burden

Topics

Artificial intelligence | Social and economic development | Information and communication technologies for development

Structural factors like market access, capital availability, and talent retention are more important for innovation than regulatory constraints

Speakers

– Anu Bradford
– Ufuk Akcigit

Arguments

Innovation vs. regulation is a false choice – Europe’s innovation gap stems from market fragmentation, capital constraints, and talent issues, not regulation

There’s concerning migration of AI talent from academia to industry, reducing open science and increasing protected patents

Topics

Artificial intelligence | The enabling environment for digital development | Capacity development

Successful AI implementation requires addressing institutional and governance challenges, not just technical capabilities

Speakers

– Iqbal Dhaliwal
– Johannes Zutt

Arguments

Technology deployment requires addressing power dynamics and institutional resistance to change

Governance and regulatory safeguards are critical challenges, especially for developing countries

Topics

Artificial intelligence | The enabling environment for digital development | Capacity development

Takeaways

Key takeaways

AI offers significant potential for developing countries to leapfrog development challenges, particularly through ‘small AI’ applications that are practical, affordable, and locally relevant

Success requires addressing foundational issues like infrastructure, digital literacy, and business environment rather than just deploying technology

Market concentration in AI’s foundational layer poses risks, with innovative resources increasingly shifting to large incumbent firms and away from open science

Effective AI implementation depends on demand-driven solutions that free up time for frontline workers and integrate well with existing systems

The choice between innovation and regulation is false – successful AI adoption requires both appropriate governance frameworks and supportive business environments

Public sector applications of AI (weather forecasting, digital identity, traffic management) require government and multilateral support as they won’t attract sufficient private investment

Trust in technology and adaptation of surrounding systems are critical factors that often cause promising AI applications to fail in real-world deployment

Resolutions and action items

World Bank Group to continue focus on ‘small AI’ applications working with governments across Indian states (Uttar Pradesh, Maharashtra, Kerala, Haryana, Telangana)

Need for evidence-based innovation funds with tiered funding structure for piloting, testing, and scaling AI applications

Governments and multilateral development banks should invest in AI applications for public goods like weather forecasting and digital identity systems

Requirement for continuous A/B testing and impact evaluation in AI procurement processes

Development of AI regulatory frameworks that balance innovation with rights protection, adapted to local contexts rather than copying templates

Unresolved issues

How to address the fundamental tension between AI-driven job displacement and the need for economic development in emerging markets

Who will ultimately set AI rules for the Global South given concentration of power in US and China

How to prevent the migration of AI talent from academia to industry and maintain open science

How to ensure public sector adoption of AI technologies when government systems resist change

How to balance AI sovereignty aspirations with the reality of global interdependence in AI supply chains

How to address labor market regulations that incentivize AI adoption over human employment

How to maintain human cognitive capabilities while leveraging AI tools in education and decision-making

Suggested compromises

Focus on AI applications that complement rather than replace human workers, particularly in freeing up time for higher-value tasks

Develop regulatory approaches that learn from established frameworks (like EU’s rights-driven approach) while adapting to local priorities and contexts

Balance between foundational AI development and application-layer innovation, recognizing different entry barriers and concentration risks

Create procurement systems that encourage competition while ensuring quality and avoiding vendor lock-in

This powerfully illustrates how AI’s impact varies dramatically by economic context – what’s entry-level displacement in one country represents the destruction of an entire economic development model in another. It also connects AI adoption to specific policy contradictions.

Impact

This comment brought urgent specificity to abstract discussions about job displacement, making the stakes tangible for the Indian audience. It connected AI policy to broader economic development strategy and highlighted policy inconsistencies that needed immediate attention.

Overall assessment

Explanation

Understanding concentration trends is crucial for policy interventions before market dominance becomes entrenched

Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.