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That was really an interesting session by CEO Cisco, highlighting the agentic AI, the role of agentic AI, as well as the physical AI and the current scenario. And also the last line was really an assuring line saying that the future will not be built by AI, but by humans who can confidently put AI to use. Well, ladies and gentlemen, moving on. Now it’s my honor to introduce a leader who’s been at the forefront of shaping the future of wireless technology and intelligent computing. Mr. Cristiano Amon is the president and chief executive officer of Qualcomm, a company that has defined and continues to redefine the global compute connectivity and AI landscape. And well, AI doesn’t just live in the cloud, it runs in your pocket, in your car, in the factory floors.

And Mr. Amon is leading Qualcomm’s push to bring powerful AI processing to the edge. enabling billions of devices to think locally and act intelligently. Ladies and gentlemen, it’s my pleasure to invite Mr. Amon, President and CEO of Qualcomm, to the stage. Please give a round of applause.

Good afternoon, everyone. Very, very happy and privileged to be here. I’m incredibly excited and energized about what’s happening here in India with AI and I think what’s happening with AI in general. What I’d like to talk to you today is about the next chapter of AI. And this is something that’s very near and dear to Qualcomm. We’ve been talking about this because I think we’re really entering now the next phase of AI. As AI gets developed, it’s going to be part of everything that we do. And especially… the interaction that we have with computers and with digital… So intelligent is now shifting for something that we kind of started and we all experience going to, you know, a chat box and asking questions into something that is going to be all around us and everywhere all the time, especially with the devices.

I actually love the presentation right before from my friend Jitu from Cisco when he talked about the traffic change from chat box to agents. And this is important. You know, I’ve been often talking about this, how we should be thinking about AI in a much broader sense. And it’s easier for a company like Qualcomm to talk about this because we build a lot of the chips that go into devices where the humans are. So as you create AI in the data center and you train and create those models, all this data. And you deploy this, you’re starting to see that this gets utilized in different ways. One fundamental thing that AI is doing for us.

it is changing the human computer interface because we don’t have to now learn how to use a computer if you know i’ve been uh often talking about this in different presentations we learn how to use an s2 keyboard and we still use that on a laptop then we use like to touch a screen but now the ai understands what we see what we hear what we say what we write so in itself it’s changing computers it’s changing the devices we interact with and uh it’s becoming a pervasive technology that is going to be everywhere and i think that’s the mission i think of qualcomm when i think about uh what we’re going to do is the same way that what we did with mobile communications and the creation of of the computer that fits in the palm of your hand is the ability to take that intelligence everywhere so we’re going to be creating a number of important shifts in the industry and i want to start talking about the mobile industry we may have had the privilege as a company to be part of every single transition of wireless technologies and let’s talk today I’m going to talk about the next one that is coming as well and what we saw with the transition of wireless technology that fundamentally at every generation of wireless you saw big shifts not only in devices and companies and because of the transition especially for example when you went to the ability to have a phone that you carry with you all the way to connect the phone or the internet all of a sudden that phone became a computer and it started to drive the future of the internet like a country like India that leapfrogged I think the internet and went straight to the mobile internet and that’s going to be true again when you think about AI for example in the mobile ecosystem AI is constantly changing and it’s changing and it’s changing and it’s changing and it’s changing going to fundamentally change how we think about the mobile device All of you today, and me included, I think we look at our smartphone, our inseparable device, most of our digital life is.

And the smartphone today is at the center of everything that we do. But now that’s going to get replaced by an agent. Now, when you think about the entire value chain that got created, for example, for the mobile industry, there’s an enormous amount of value on things like OSs and application stores. And that becomes like the platform when you’re going to develop an application that you’re going to do different things into the platform. An agent that now understands human intentions because, you know, you just need to tell him what you want. Or he’s going to see what you see and make a decision for you, assuming you will authorize it. it. When that happens, that’s where the value is because then the agent is free.

It can go to the internet and do things. It can go to your phone and do things. And you’re no longer bound by constructs of your hardware or your apps in the application. So as a result, we expect the AI is going to have a fundamental shift in the mobile industry where the agent is going to be at the very center. And as the agent is at the very center, everything surrounds the agent. You can access the agent from your mobile phone, but you can also access the agent from your glasses or for a pendant or for anything that you wear. And I think we’re going to look at the mobile ecosystem right now, not only as a single device experience, but you’re going to connect to agents across multiple types of devices.

And I think that’s incredibly exciting. And that’s not only unique to what you’re going to see in consumers. That’s going to happen also with things, because you can also have create AI that’s going to get trained on different things. on physical signals, like physical AI, on sensor data, and you’re going to deploy that in every computer. So what’s exciting about AI, it’s going to very quickly evolve for something you go to a browser and you ask a question. And I think, as my colleague from Cisco said, it’s got train and all the public available data on the Internet. You’re now going to go to a different type of AI experience that’s going to be the fundamental software that is going to run in all the devices around us and how you’re going to have interaction with the devices.

So I also want to basically, you know, as we think about this future, I just want to give you an example. What we saw across the industry is workloads or use cases have shifted. Devices didn’t go anywhere, but their workloads shifted. We used to do a lot of things in the early days of the Internet on your laptop. And forget. For example, e -commerce, you will do it on your laptop. Now, most of the e -commerce in the world is done on a phone. Tomorrow, or it could be like as early as, you know, within the end of this year, as you start to see the proliferation of glasses. If you have a glass that has agents, is connected to the Internet, has camera on those smart glasses, the glass see what you see.

You can just look at something and say, I’d like to buy this. What is, you know, can you check this? For example, check this on Flipkart. Just buy it for me. I’d like to buy this. Integration of payment system. You got a bill, say, pay this, notify me when I’m done, and so forth. So I think we’re going to see this fundamental change of devices. But that’s also going to be true about the revolution that’s happening in robotics and the revolution that exactly happened on industrials. So that’s an incredible opportunity. And we have been incredible. Incredibly focused as a company to basically drive that future of computing. There’s also a big debate, which I believe is the wrong way to look into that, which is about cloud and edge.

There’s a lot of debate about, oh, this is going to be running on the cloud. This is going to be running on the edge. And actually, it does not matter. Think about your device today. Your smartphone today has incredible amount of processing power, and there’s a number of different things that run in your smartphone. If you put it on airplane mode, you probably don’t use it. You just put it back and wait until you get connectivity again. It’s the most cloud -connected device because those things work as a one. And you’re going to have now intelligence that’s going to be incredibly distributed across the cloud, across the near edge, the network in itself, in and on device.

And it’s all going to work similar. There are going to be things that you’re going to be able to do on the device because they’re… They require an instant response or require unique context, unique information that is relevant to you. Something is going to do on the cloud and they’re both going to be growing and it’s going to be transforming how we think about computers. So I like to provide the simple, I think, a description. Let’s say we are all using agents and you’re going to pick the agents that you like and the agents to be useful. It needs to be fast. It needs to be relevant for you. Let’s say, go back to the example I provided on the glasses.

And you have those smart glasses and you’re walking around and you have a camera. Then all of a sudden you see somebody and you ask this glass, like it’s your friend next to you and say, who is this person? And you want to get a response. This is so and so. Or you’re going to say, can you translate this for me? What is this? Can you pay this for me? You want to, this thing has to be similar. Similar is no friction. So certain things are going to be done on your device and the thing’s going to be on the cloud. It’s going to be completely transparent to you. But the interesting thing is those agents, for them to be very useful, they needed to be contextually aware of what is relevant to you.

So over time, the agent I’m going to be using, the agent you’re going to be using, they need to be relevant to me. So you’re going to have a lot of things that are going to be being processed and understood about you. So much so that I believe that in the end game, I think it was said in the prior presentation from Cisco that all this available data that is publicly on the Internet that you train models, it’s a fraction of the data that is going to be generated. If you have, for example, a glass of a camera that sees everything that you see, try to annotate the image, get information about the image and the context, reads what you read.

And so forth, that is an incredible amount of data, and that’s going to be providing a lot of important context for those models that are going to be relevant to you. That is the future, and it’s an incredible transformation. It’s going to transform every industry. No industry is immune to this. And I think what we’re doing at Qualcomm is really creating the future hardware and software that will help enable this future across all the devices. We’re a very unique semiconductor company. I think we’re probably one of the few companies that can be working on chips from sub -2 milliwatts to a smart earbud that you’re going to wear all the way to now 2 ,000 watts per chip on the data center.

But I think that’s the incredible future that AI is going to transform every single computer. And the agents are going to be at the center of the experience. It’s going to replace a lot of the OSs and applications. And that is the new future of technology, including the future of mobility. And that’s why we’re incredibly excited about this. And with that, I want to talk about something that is happening, which is about the next generation of wireless technologies. I would like to provide an example from the past. When you think about telecom networks, and I think we’re probably one of the, you know, American telecom companies that really focus on the evolution of cellular technology.

When you think about the evolution of this sector, when this all started, it was about providing a telephone. I think all of us was an incredible thing. You have a twisted copper pair to get to your home. You pick up. You get a dial tone. You dial. And eventually, you could dial. Anybody in the world of a telephone. Even how cellular started was about making sure all of us had the ability to carry a telephone. That was 2G, that you can call everyone. That’s different today. Now you have a very high performance broadband network for data. Voice is just one application in the many applications that you do with the network. It fundamentally changed the nature of the infrastructure.

The equipment was different. The use case is different. We’re heading to the next big transformation of the telecom sector. So 6G is going to provide an evolution of connectivity, faster speed, lower latency, higher coverage. But that’s not the story. That’s just a piece of the story. It’s just continue to improve the connectivity. The biggest part of 6G is AI, like I said before, is now going to come to the telecom network. And that becomes a large scale. 6G. AI network that is processing and get trained on all of the signals that happens at the network and providing new capabilities. One of the biggest features of 6G is the network, is the sensing network at scale.

I’m going to give an example. The network not only will provide a connectivity between your device and the Internet, but will sense everything that’s around you. We’ll use techniques that you see today in autonomous driving cars, like radars, as an example, to detect your environment. It’s going to provide a map of everything that is happening at scale. And you’re going to have completely different type of services for different industries. It will provide context for your agents. Very important. And the network will have that role. It will provide traffic management systems and some of the use cases that are going to be part of full self -driving cars. It will do drone detection and manage the traffic control.

Off the economy, there’s going to be an aerial in the wide area network and much more. because AI is also going to the network. It’s going to be one of the biggest transitions I think we have, as big as going from voice to data, and it’s all going to be part of this future of AI. And I just want to now make another parallel, I think, to the presentation from my colleague from Cisco. It puts a fine point on the network that needs to be built, the capability of the infrastructure, the security and trust, but that is an incredible future with technology. And as I get to the end of the presentation, I want to highlight that India has an incredible opportunity with this transformation.

We have seen that those big shifts in technology creates opportunity, change players. It changed, I think, the role of different countries as they provide globally. It’s a global scale for the technology, and that’s an incredible opportunity for India. I look of what happened in mobile in India, and one of the largest data consumption per user in mobile devices in the world is in India. The whole Internet is mobile. When you think about the potential and all of the things that I just discussed about how AI is going to change everything, creates new device, new experiences, new services, that becomes a massive opportunity. And when I look at the ambitions that were set by the AI Summit, I’m going to provide just some examples.

Those are just examples. It can be very broader, but I just want to connect with some of the ambitions of the Summit. There is a process of jumping into a large -scale industrialization. India is becoming a global manufacturing hub as well. And with AI, you… You go from the very beginning. with smart manufacturing and automation with incredible change that is happening in this sector enabled by those technologies. Same thing with smart cities, the ability to continue to evolve the infrastructure, the ability to use AI to increase the scale, the reach, the access for healthcare. How you change education. Those are incredibly powerful learning tools. The ability to actually use some of those technologies to empower people with information and you’re going to have an ongoing learning experience.

Think about those agents with you all the time answering questions, telling you how to do things, especially when you think of context, for example, of those new devices such as smart glasses. And it can fundamentally change industries, for example, such as agriculture. Right. Right. Right. Just a few examples of the potential of connecting this technology with everything, I think, that is going on in India. It’s an incredible and exciting future enabled by AI. And really, it’s about meeting the ambition of democratizing this technology for everyone and actually have an important role in increasing the global welfare. And, you know, as a company that has always been focused on enabling our partners and other industries to innovate, I think the history of Qualcomm, we never believe is the job of one company to be responsible for all the innovation.

It’s really to enable many industries and partner. We’re incredibly excited to play a very small part on this mission. Thank you very much for the opportunity to talk with all of you and

Well, ladies and gentlemen, now it’s my pleasure to invite our next speaker, Mr. Giordano Albertazzi, who is the chief executive officer of Vertiv, a global company that provides digital infrastructure solutions for data centers, communication networks. Under his leadership, Vertiv is advancing its role as a global industry leader by accelerating innovation, strengthening technology leadership, and enabling the digital infrastructure that powers critical applications worldwide. Ladies and gentlemen, please welcome Mr. Giordano Albertazzi.

Thank you very much. The clicker? Oh, yeah, here. Better with the clicker. Good afternoon, everyone. And it’s absolutely a pleasure and an honor being on this stage where so many distinguished presenters. In the last two days, I’ve had the opportunity to talk about AI. An astonishing thing to me is that the majority of the AI conversations, as it should be, are about what AI can do. Very interesting presentation, just finished, tells about all the beautiful things that AI can do and particularly what AI can do here in India. But when we talk AI, we also talk about data centers. But let me go then to the physical part of AI, not just what AI can do for us.

There is an important, very important physical part of AI that sometimes is overlooked. And it shouldn’t, because it’s that physical part that makes AI actually possible. So I’ll talk about the physical part today. I will talk about the power. The cooling, the data center infrastructure. Vertiv and myself, with Vertiv, have been in the industry for decades. Well, Vertiv longer than me. It used to be part of Emerson, Emerson Electric, and we are almost 10 years as an independent company now publicly traded in New York. But what we do is really make sure that that physical part is provided with the best technology that supports the continuous evolution of the AI IT stack as those rapidly, almost exponentially, and I’m talking almost exponentially from a mathematical standpoint, evolve.

And it’s no easy task, a task that we would do very well because we know the space a lot. We have a lot of innovation. But there are several dimensions. to this. One is the extreme densification. Now, we all know what GPUs are. Probably two years ago, majority of people didn’t have any clue about what a GPU is. But now, GPU, NVIDIA is absolutely central to everything, all the conversation about AI. Well, that phenomenal evolution from a technology standpoint is changing the DNA of a data center. What used to be a rack with IT inside 10, 15, 20 kilowatt per rack is rapidly becoming more dense and with more power and heat to dissipate in it. This is going to 30, 50, 150 kilowatts per rack all the way in the possible future.

One megawatt per rack. That’s a lot of power in a single rack. The design of a data center is changing dramatically. As this design changes, of course, also the technology that supports it needs to change. But let me go back to AI, artificial intelligence. Let me go back to, and let me draw a parallel. Human intelligence. Human intelligence happen in the brain. But the brain doesn’t survive without a body. What we are, what we do at Vertiv, make that body, provide that technology for that body so that the brain can function, and that brain is the IT stack. But not only that brain can function, but also can produce intelligence. And that’s what an AI does.

That’s what an AI factory, an AI data center is doing. But just like the body, historically, data centers and data center engineering was viewed as disparate systems coming together. Now, we cannot think about a human body or anybody as individual parts, a chiller, a liquid cooling unit, an interruptible power supply, or whatever else in the powertrain or thermal chain you can think of. Everything needs to be orchestrated. Everything needs to be interoperable. Everything must be thought of as one thing. And that’s what we do in a world that is extraordinarily challenging, but it’s a challenge that we, of course, respond to very successfully, challenging in terms of time of deployment and in terms of scale of deployment.

Okay. Data centers need to be. developed faster and faster and are becoming bigger and bigger. You heard that. It is about data centers. India is certainly privileged from an AI standpoint also because there is a lot of power available that can be harnessed for more and more powerful and larger data centers. Now, as that happens, again, if you think, go back to my analogy of the body, you think about a system, you think about everything that is the body of artificial intelligence, then it is about changing the way we build that body from one piece at a time with a lot of activity happening on site, laborious, hard from a quality standpoint, to most integrated at factory level and deployment.

As NVIDIA continues to lead the world in terms of technology, in terms of IT stack, but also in terms of thought process for the infrastructure. And it’s something that we do a lot together. Well, then it is not just about the infrastructure and the speed and the size and the scale. It’s also about optimizing the infrastructure with reference designs that exactly target that type of application. So we, of course, are thrilled and always honored to partner with NVIDIA in this adventure and venture and lead the market in this respect. So here you have an example of what we call one vertigo, one core. There’s an example of a fully pre -engineered, defined data center. But when we talk about the body, the body of AI, the data center, then let’s talk very simply.

We talk about three fundamental, fundamental elements of that body. One is the powertrain. So everything that goes from the grid, if you will, for your utility, takes that power all the way to the chip. That power infrastructure is changing, is evolving as the power density changes. And the current architectures are migrating towards, over time, what is an 800 -volt DC power infrastructure. I’m going technical on you. Some of you I know are very technical, so I’m not afraid about that. But I will not go deep. So everything you see on the left side of this is exactly a representation of that powertrain. So bring the energy, take that energy to the chip. Then the chip and all the electronic components in a server generate heat.

And that heat can be very dense. And require very, very advanced… cooling mechanisms. And that’s the beginning of what we like to call a thermal chain that starts, and it’s what you see on the right side of this chart from the chip all the way to the heat extraction, the heat then rejection, or even more importantly, and more extensively so, is the heat reuse. So this is the system, the fundamental systems of this body. But again, it’s not just about the components of the system, it’s how the entire system works. And more and more, we see that when we think about the AI IT infrastructure, what used to be thought of as a server at a time is becoming really an AI, AI.

pod, an AI unit at a time. The unit of compute is no more the server. It is the pod. Unit of compute is not even the pod. It’s the entire data center operating as one single computer. A unit of compute that can go all the way to gigawatts. So it is about making sure, and I believe we do it very well, that I say uniquely well, but of course I root for ourselves. It is about making that infrastructure available at scale and in a very easy modular to deploy fashion. And that’s what we do. So a repeatable converged infrastructure major. So we have a lot of building blocks that can go from a 12 .5 megawatts all the way to gigawatts, all the way to gigawatts.

So clearly it’s not just about building that infrastructure, but that infrastructure over time needs to be, like we like to say, future resilient. Some people, like myself, have been in the industry of data center for quite some time, and it’s fascinating the speed at which things happen. And this speed is also enabled by new solutions that make prefabricated and very fast to deploy part of data centers that used to be very, very laborious. Take a data. It’s empty when the building is new. You have to fill it with power, with cooling, with cables. You have to put the racks. very laborious and time consuming. Time to token is of the essence. A prefabrication, for example, with what we do with Verti Smart Run, reduces the time to deploy almost 85%, almost an order of magnitude.

So the industry is changing, not only in scale and in density, but also in the way things are done and deployed. Let me take a different angle now and focus on India. India clearly central to the AI evolution revolution and central certainly in terms of the infrastructure that is being built and the infrastructure that will be built in the future and in the coming years. This infrastructure and the speed at which this infrastructure will be built, of course, will depend, as I was saying, by the ability of the likes of Vertiv, but certainly Vertiv, given our prominent position also in India, to really enable this at scale and at speed in the ways that I explained.

So Vertiv in India has a long tradition. We’ve been here for decades. We have what I believe is an awesome team and awesome partnerships. And now this forum, these sessions, these few days convinced me even more of the importance of India as a place. A place to invest. And invest we will. We are expanding our capacity and will continue to expand capacity. We see India certainly as an extremely promising market. as a hub for AI, not only for India, but globally. So it has got the power availability. Certainly India has got the right demographics. So I couldn’t be more excited about the business in India. I couldn’t be more excited about what we’re doing in India and what our partners are doing in India.

So with that, I’m extremely optimistic. I’m a big optimist about what AI will bring, as we heard. And with that, thank you very much. Thank you.

Thank you so much, Mr. El -Battazi, for your impactful address and also for…

Data centers have, up until now, been usually constructed in one of two ways. Traditional data center build follows a sequential process, materials and equipment arriving individually on site, with the build progressing from the ground up. Alternatively, prefabricated modular construction can offer many advantages, such as quicker deployments and risk reduction. Vertiv offers many solutions in this space. However, in the age of increasing IT loads powered by artificial intelligence, there’s another option that combines the advantages of both. Vertiv OneCore. Vertiv power and thermal infrastructure building blocks are inserted into a brand new Vertiv -supplied steel building shell, or an existing building. Infrastructure building blocks are made in controlled factory environments and tested before construction. The system is also equipped with a new, more efficient, more efficient, more efficient, more efficient, more efficient, more efficient, more efficient, more efficient, more efficient, more efficient, more efficient, more efficient,

accessibility, language accessibility and language inclusivity. We are a country of 1 .4 billion people. More importantly, a country of 1 .4 billion voices. We all think differently, we all speak differently, and we all dream differently. But whenever we go online, everything is available only in one language. Majorly English.

To break the language barrier that exists in our country. And we have different solutions and different integrations that we have. One of them is Pashni translation plugin, which is already sitting on top of more than 500 websites, if I’m not wrong, the exact number. And we are enabling people, we are enabling citizens of India who are essentially not being able to understand in English and Hindi because most of the digital content that you see, primarily the website, maximum you’ll see is a website which is sitting in a state. The default language would be there or English primarily. But then what about rest of the languages? Imagine a scenario that I’m someone from north and I’m living there in Maharashtra.

Mostly you will see the content in Marathi or English. But then what about having the same content? I don’t know English. But I really want to understand what is there. And I want to convert it, the different policies at the state level, different guidelines, different content, maybe creative content, etc. You need to know in my language. So, Bhajani Translation Plugin is one of the engineered solution using all the models that you might already be aware of. 350 plus models from our platform. We have this solution as Peksa Swati.

So, as Shailendra Pal mentioned, last year a farmer wanted to apply for the PM Kisan Samman Nidhi. It’s basically a very simple form that the farmer has to fill. But the form was in English. The farmer literally had to travel 40 kilometers only to find somebody who can actually help him out filling the form. This is the language divide. This is the barrier that we are trying to avoid. Eliminate. 800 plus million people. are not fluent in English. And 95 % of the content which is available, it is in English. This is where Bhashni comes into picture. The National Language Translation Mission of India. We are trying to transcend the language barrier. We are creating a unified multilingual layer for India’s digital ecosystem.

We are not just providing language as a feature. We are providing language as an infrastructure. We are encouraging language as the foundation for digital inclusion. Next slide, please. So, like sir introduced, the Bhashni Translation Plugin. It’s a powerful product through which you can have any website being translated into multiple languages, being accessible to all the people in the last mile. And this happens in matter of minutes. Not days. Or months. Or just minutes. This is the power of the product that we are talking about. And you don’t have to rebuild the entire website. You don’t have to redesign it. There is no back -end overhaul. Just one liner, very lightweight, simple code that you can copy and paste onto the website and you will have your website speaking multiple Indian languages.

This is how accessibility is made effortless, inclusion is made scalable, and the last mile reach is made real. So I just want anybody to see. Anybody who can copy and paste. Like we don’t need a developer or a person who knows JavaScript or the entire back -end. just somebody knows copy and paste and we’ll see how with the help of that you can have the entire website multilingual. So anybody who would like to do that? Yes, sir, please.

Maybe, you want to open a website first and show what exactly VashuCast is.

So this is the Vashni’s website and here is the plugin that has been integrated on the website. This plugin will help us have the entire website available in all 22 Indian languages. All right, so while we just give a quick glimpse of what Bhajani translation plugin is, it is basically a very lightweight utility, though. you find it very simple but the content that we have on this website primarily is in English and there are other challenges that you that we would like to discuss later on as how this translation plugin brings in though it looks very easy just you clicked on a button and then you do a translation all together but then we’ll discuss more about what are the different challenges we come across not from the fact the engineering side of it but on the language side of it how we cater and have this challenge taken care so this is just a plugin we just wanted to tell you this is how it works but you know if you go back to English then and then you know we will just talk about what you wanted to we’ll continue with that so I just wanted to have a quick demo of how you can integrate this plug -in onto the website so I think some if yes you can come we’ll just see how with the help of just the knowledge of copy and paste we can have the entire code implemented and you’ll have the entire website translated into multiple different languages.

For the purpose of this demo we had created this dummy website and the code for this website is here. So this is the code that none of us would most of us would not understand. And I would like to request sir to just copy and paste the plug -in code that we have. So we want to tell that this website content is only in English and you want to add multi -lingual flavor to it using Bhashini. You can integrate the solution that we have on the top of

That’s what you’ve meant.

Yes. So if you can sir just copy and paste this code. The code which is written here. Yes.

Anywhere here.

If you can just add a hyphen between translation and plug -in.

Yes.

Can you go back to the website? Refresh it. So you can see that the plug -in is added. And we can now have this website available in all 22 Indian Schedule languages. So that’s the power of this code. We’ve taken care of everything that is happening at the back -end. and you just have to copy and paste the code that we’ve created for you. It’s as simple as that.

So Swati, so let’s say I’ve embedded this particular thing on this particular website. Now it is available. There is the icon. What about if I go to next pages, right? Will the system understand that there’s a link in the I chose and I go to any page? It will reflect Hindi or I have to select every time I go to any page as my language, which I chosen as Hindi.

So you don’t have to apply this code on every page. The pages of the website will automatically understand that the multilingual feature has to be embedded on all the pages. So if you move on to any other page of the website. So this was just a dummy website that we had created. Let me. Go to Bajni translation plugin. in Bhajani’s website. So if you go to any of the pages, the plugin will remain there. And you will have the multilingual feature added on all the pages of the website, not just the home page. So let’s go back to the slides now. So like we just demonstrated, the code that we have for the plugin that we are talking about is a one -liner, very lightweight, simple integrated, simply integrated code, which you can use to have your website available in all 22 Indian Schedule languages.

It is DBM compliant and framework agnostic. So if you have your website, in different, made in different languages, it’s irrespective of that, the code will be applied to your website and you can use the same code.

So Swati, can you just give some light on what is DBM compliant as how the website is DBM compliant? If I, let’s say I have a government website and I want to include the Bhasini translation plugin onto it, what is this DBM compliant that you talked about?

So these are the compliances mentioned in the digital brand identity management compliance book that is available. So for everybody to have an accessible website, the DBM compliance have to be followed. And we have the DBM compliant code with us wherein all the accessibility features like, you know, that happens in the backend, you know, for, any person who is a visually special person who wants to access the website. is able to do that with the help of the technical integrities that we’ve incorporated into the plug -in code that we have. So this is a glimpse of the impact that we’ve already created. We have approximately more than 400 plus websites that are already integrated with Pashni translation plug -in.

From those websites, we get approximately 24 million plus inferences. And we’ve created 1 .5 million plus glossaries. So glossary is something that I will take at a later section during the session only. But just for a short description, glossary enhances the translation in such a manner that the end citizen who is actually consuming the content from the website is able to understand the content. And also, these are the 22 Indian scheduled languages in which the plug -in is available. available. Next slide, please. So while we were creating the plugin, we had to create something that, you know, one size fits all product and which is something very difficult to create because everybody has different requirements and to cater to all those requirements, we had to make one product that can simply be accessed by everybody.

So these are some of the use cases that I will be discussing that our plugin has the capability to resolve to. The first one is that generally what happens in, you know, a product like this, you translate, you know, from English to the target language. But here in our plugin, what we’ve done is that even if your website is, let’s say, created in a language other than English, let’s say Marathi. That can also be translated to the targeted language directly. So you don’t have to first translate the website to English and then move on to the targeted language. You can have the source and target language as per your requirement. So that’s how we’ve not, you know, you don’t have to get into the bridge of creating English as an intermediary to move from one language to another.

Next slide, please. Okay, so when I talk about a website, there are different sections of the website. And not all these sections would you want to translate. For example, the calendar, if there’s a calendar, you would not want it to be translated into, you know, the target language. Including email IDs and, you know, there are certain sections that a lot of people didn’t want to be translated. So there is one class that you can embed that is the skip translation class. Embedding that will help you. Navigate to the, navigate the sections that you don’t want to be translated. So, that’s also one feature that we have with our plugin. Next slide, please. Okay, so, you know, you saw the plugin, right?

There were languages listed in a certain manner in the plugin. So, what happens is at, you know, many regional places, we want the plugin to have the regional languages on top. So, for example, after English, people don’t want to go alphabetically like Assamese, Bengali. They would want their regional language. In this case, they wanted Hindi to come in the, you know, to change the order of the languages that are appearing. And that is also possible. So, if you want your regional language to come on top, you can have that with our plugin. So, you know, majorly what we say is that we would want to… We want to display our website in a certain language.

So for example, if you created the website in let’s say English, but you would want all the users to have the language to be displayed as Hindi first. And probably then they can navigate to their own targeted language. So even if your website, the source language of your website is English, you can, there is a possibility of adding the parameter which can have the source language as Hindi or Marathi or Punjabi as the user requires for all your websites. Next slide please. Okay, so what if your… Your website has the, you know, has been created in two languages. So for example, you’ve created your website in English and Marathi also. So that was the use case that we had with finance department Maharashtra.

So they did not want translation to happen in the Marathi language and the English language, though their source language of, you know, so basically the source language of the website was English and Marathi. So if you want to skip translation for different languages also, you can do that. So in this case, what happens is that the user selects a language. If the language of the source is selected, let’s say, you know, English or Marathi, it will go redirected to the English or the Marathi page of the website. And if the user has selected any other language, it will move on to the normal process of translating it into the target language. Next slide, please. So, you know, sometimes we have portals also.

Yes. So, you know, because we would want to have websites available in all 22 Indian scheduled languages so that we try and reach out to the maximum people. But if that is your use case wherein you would want just three or four languages to be displayed for every user to be seen, you can have that also. So the drop -down will only display four languages in that case? Yes. But it’s always encouraged to have all the languages so that everybody, you know, who’s accessing your website can have the website available. Thank you, then. So, talking about this use case, what happens is that in most of the cases, we also have portals. And in portals, we have forms or, you know, we basically ask input from the user who is using the portal.

So if they apply Bhajani translation plugin and they, you know, move on from one language to another, it will reload the entire page. If it reloads the entire page, whatever the user has filled in, like their details, their name, their email IDs, all that information was lost. So what we did to capture this was that now plugin can also have the portals without the reload picture. So if you don’t want the plugin to reload every time a user selects a language from the drop -down, you can have that. Next slide, please. So this was a very interesting use case. You know, you can see this is how the website was displayed. So the source language of the website is English.

But like we can see, after every English, below every English word. there is a different language. So Haryana written in English, then Haryana written in Hindi. Puducherry written in English and some other language. So here this was use case of handling mixed languages. So what we did here was that whenever the plugin sees that the source language of the plugin is different from what characters it is getting, like here in Haryana, it is getting Hindi characters also, it will skip this translation automatically. So you would not have to skip it at your end. We’ve done it and we’ve created it, we’ve designed the plugin in such a manner that if the source language of the website is, you know, if the contents going for the translation are different from the source language of the website, it will automatically skip the translation.

Next slide, please. So… With certain use cases, what happened was… that there was a lot of dynamic content on the website. So, static content can easily be translated. Like, it is also difficult, but it’s not as difficult as handling the dynamic content. But for certain, like for State Bank of India and for MyBharat Hotel, the dynamic content was changing so rapidly that it was making too many API calls and the response time was getting delayed. So, what we did there was that we intelligently had the code running in such a manner that the dynamic content was, the translation of dynamic content was handled in batches. And that’s how the, you know, API calls, the increased API calls reduced and the response time was stabilized.

Next slide, please. Okay, so now… We all can, you know, navigate to the website. select the target language on the website and have the website available in the target language. But what if somebody cannot navigate, cannot select a language from the drop -down? We also, with Rail Madad, you know, if you go to the Rail Madad’s website, there is a mic button. So you just say out your language. So for example, if you say out Gujarati, the entire website will turn into Gujarati. So that’s the capability of it. Next slide, please. Okay, so this is a very recent use case that we’ve handled. So like you can see here, there is the MSD website. And there is also another domain name, which is Hindi, which is in Hindi.

So what the client wanted was that, you know, once the user selects Hindi as the drop -down, the translation happens, but it also redirects to the… Hindi domain of the website. So that mapping of which language to which domain, that is also something that we have done at our end and you can have URL redirection also. Next slide please. Okay, so what happens, so let me just ask, I hope everybody here understands Hindi, right? What is the translation of home in Hindi? Ghar, Ghray, that’s right, right? But the home tab on the website, if it is getting translated to Ghar, it’s not the correct translation. It should be translated to Mukhya Prash. So these kind of use cases wherein the translation which is being given by the model is correct but you would want a specific different translation for a specific word or phrases that can also be handled through glossary.

So, the way that we have done this is that we have website. So, we have a lot of information in the information in the Just now, after we complete this, next slide please. So, this is the future roadmap for plugin that we have. We have expanded it to 36 languages, 36 more Indian languages. So, you can go to Vashni Pavilion which is right here in this hall only. We have a demo of the plugin which is available in 36 languages. We are also incorporating the 35 international languages. We have done that for certain use cases which are displayed here today at Bhaat Mandapam. Secondly, we will be talking about glossary but the glossary in, you know, traditionally the glossaries were sent to us through emails and there was a process to, you know, process the glossaries and then ingest it.

But now, we are also planning to get it automated wherein, you can just simply upload the glossary from your onboarding portal. and third, we are also adding the accessibility bar to the plugin. So if you want to have text -to -speech also integrated or screen reader also integrated with the plugin that we just showed, that is also something that we are going to do in some time. So technology for dignity, Bajni Translation plugin would help. It is a powerful tool that will empower you to actually disseminate whatever information you want to, to actually reach the last mile. Moving on to the next segment, which is the glossary. So, you know, we all of us here, we would have some application, some website developed for…

the ease of the user. We would want a person, a student who is registering for a form who can actually do we would want the person to do it in their own preferred language. We would want a farmer to listen to the schemes that are available for him in his preferred language. We would want an Angadwari worker to have the schemes that are available for her told to her in her own language. So that is all what we are working for. We are working for inclusivity and we are working for accessibility. Next slide please. So while we do that we also add Bhashni’s layer to all our solutions or websites to have the actual information reach the last mile.

But generally what happens is that you know we get a remark that the translation is not correct. It is wrong. And after doing analysis with most of our customers we realize that the audience, that the users who are trying to actually use our product, they are not looking for accurate translations. They are looking for understanding the content, the intent of the content which is there on the solution on the website that they have created. And this is not the result, you know, for this we don’t have to focus on getting the accuracy of the translation. We actually have to focus on the context of the translation, use case of the translation, domain of the translation.

So when we realize that, we understood the concept of glossary and that’s how glossary was formed. Next slide please. Now you all would be, you know, waiting for, to understand what glossary is all about. So glossary saw… It involves two kinds of use cases. One is the post -translation that I just told you before. That, you know, home being translated to ghar in Hindi is absolutely right. But home being translated to home tab being translated to ghar is probably not correct. So post translation wherein you would want home to appear as Mukhya Prasht on the tab, home tab, that is something that we cater through with glossary. The second use case is like in the example, there is a bridge called Vakil Saab Bridge in Gujarat.

So Vakil Saab Bridge if translated to English would become something like Lawyer Bridge or something. We wouldn’t want that. Vakil Saab Bridge is our coined terminology and we would want it to retain its identity. We would want Vakil Saab Bridge to be written as Vakil Saab Bridge only in English. And this is the use case of transliteration. So these two kind of use cases are solved through glossary. What we do is we create. We create these glossaries with our customers and we ingest it to the customer’s specific API. Next slide, please. So, you know, like I told you the meaning of glossary, all of us here have different glossaries. Like, you know, the science domain glossaries are different.

Gen Z has a different glossary altogether. You know, any region would have a specific kind of a glossary. So all of these glossaries have to be created with us. And, you know, the customers who created those glossaries have got the translation, which are accepted by the end user, which are understood by the end user. Like you can see, Ministry of Panchayati Raj gave us 15 lakh words of the Panchayat. Survey of India has given us 16 lakh words. So if we create glossaries together, we can have the translation barrier completely eliminated. So I will now walk you through certain. Use cases wherein we faced problems with our customers, but they were not. translation issues, they were actually issues that could have been easily resolved through glossary.

So if you can read this sentence here. So this use case, you know, this problem was reported to us by Ministry of Home Affairs where Honourable Home Minister Sir’s profile was not reflecting correctly. So this was the English sentence. Okay. And this was the translation that we were getting. So if anybody can tell me what is the problem here? So because of this full stop, Srimati full stop, SMT full stop, what the model thought was that the sentence has ended here. And that is why the formation of the sentence is entirely incorrect. But the solution was very simple. What we had to do was just add SMT dot to the glossary or just remove the dot from the SMT.

And we could have the correct output. So it’s as simple as that. It’s not the translations problem. It’s the understanding of glossary problem. Next slide, please. So, okay. Can anybody tell me what’s the difference between this and this puzzle? To these two puzzle pieces, what is the difference? Yes. So one of them has a hyphen and the other one does not have a hyphen. So when we received the glossary from MSME, there was a hyphen in between PMS and dashboard. but actually on the website it was displayed without the hyphen. So glossary is that sensitive. If you give me PMS hyphen dashboard, it will only recognize that and translate that. But if there is no hyphen, it will not recognize that and it will not give you the translated output which you have given us in glossary.

So that’s the, and again, here there was a singular and plural problem. So street vendors was mentioned in the glossary sheet that we received. But actually street vendor was mentioned on the website. So if there is a singular and plural difference in the glossary sheet that you are giving to us and what is actually reflected on the website or your solution, it will create a difference. So this is one thing that you can also do through glossary. So, you know, we received. A requirement wherein they wanted, you know, the animal husbandry department wanted that the entire sentence should not be translated. the, you know, abbreviations should be skipped from translation. So if you just give me this sentence and this sentence as glossary pairs in English and Hindi, this can easily be achieved.

Next slide, please. Okay, so in one of the glossaries that we received was authorized officer, so they wanted us to write authorized officer as newt adhikari. But actually, newt adhikari means appointed officer. So this is also something that we have to be careful of. Because, you know, for the end user, so there are two kind of users that we have in this case, the English users and the Hindi users. So the English user would read it as authorized officer, but since we have added glossary and changed it to, newt adhikari in Hindi, the Hindi user would read it, would understand it as appointed officer. So we have to be very careful while drafting the glossaries.

Next slide please. Okay. So if I can just ask what is the full form of BN? Normally what do we consider as the full form of BN? Billion, right? We would not consider BN as battalion. But in BSF’s case, this was a huge problem. So BN for BSF means battalion and not billion. The entire context changes. So for BSF, we have created glossaries for all the abbreviations. So it is always suggested that you know whatever abbreviations that you are displaying on your website or your solution, just give it to us as glossary so that the correct one can be displayed. So okay. So, So can you tell me if PS to Minister being translated as Maanenya Vastra Mantri Ji ki Iji Sacheev, is there a difference or like what would be the problem here?

Fine, let me tell you. So this is also correct, this is also correct. But this is not the actual translation of PS to Minister. If we want to have Maanenya written in the Hindi translation of it, we should always have it in the English version of it also. Glossaries are supposed to be equal. They are supposed to be equally weighted. You cannot expect the model to add or delete words as their own. So basically what we did here was we went back to the customer and said, that if you want to add Maanenya, you can add Maanenya to the text. at the output, please add respected or honourable in the input. Only then it will be balanced out.

Next slide, please. So this is one request from our end only. We receive a lot of glossaries that are redundant in nature for us. By that I mean that, you know, for example, we received employment and skill development as the glossary terminology and the translation that we are getting in Hindi. That was the actual output of the model also. So in this case, if you are giving us the glossary, which is actually the output of the model, you are only creating redundancy. So if you can just avoid that and give us translations, give us the post -translations or transliterations that are not recognized by the model, that would be handy. Next slide please. Next slide.

So in the end I would just like to say that language is not just words, it is identity. Let us prepare India’s languages for the future of AI and let us create glossaries, let us have multilingual AI in, multilingual layer in all your solutions so that actually the end user is benefited, actually there is digital inclusion, accessibility, inclusivity. Thank you. Any question?

Can you hear me? So I was saying like the translation thing which you were showing, is it, I know like this has been sponsored by the government and stuff, so can it be also used for commercial purpose like for private or private? Public entity? Can they also use that in their websites also?

So, you know, we have different kind of collaborations with us. So for that collaboration, there is a different agreement altogether that is being created. If you want to know more about it, just go to the Bhajani Pavilion. We have stakeholders who are handling the startups, the private organizations also, and they can help you there.

And one more thing which I wanted to know. So like you were showing for the websites, it was by default we can choose the default language, right? So can it be also extended? Like let’s say in some use cases, we could have someone who is logging in from Delhi. They would want to see it in Hindi and someone who is coming from Maharashtra. So can it change the default languages? Can it change from the region perspective?

So that’s an interesting use case. From what I’ve understood, you want different regions to have websites opened in different default languages. As per my knowledge, I don’t see a technical challenge to it. But again, we will have to look at the use case at our end and see if this can be deferred. It’s a very use case. This is a very interesting use case. We’ll look at it. Thank you.

Hi. Hi. So we all are aware that we have multilingual languages. And apparently, they have been trained on a lot of words also according to their domain knowledge. So if we have glossaries, how do we ensure that each and every glossary according to the domain is maintained and then trained or fine -tuned?

So glossaries are customized. For example, somebody from Ministry of Home Affairs would not want the glossary of, let’s say, CSI. Right. Right. you know the domains are different the contexts are different so glossaries are ingested only are customized and are ingested to the client itself they are not we have general glossaries also that can be applied to all but since glossary does not have the you know one glossary fits all type of a solution so we customize it for a client and then ingest it on to that client solution itself not to other clients or other environments.

Thanks So the glossaries you have do you have them do you use them to fine tune your models or is it just available as documents to infer while using?

So we do that we try to fine tune the models as well but we there are lot of things that we have to look at it look around while doing that because you know we have to classify them into different domains and then apply fine tuning models for the domain space. So it’s a long process, but we do that. Okay. Thank you. If there are any other questions, I will be available at the Bhashni Pavilion here also. And I would request everybody to please come visit us, explore our solutions, explore our services. And thank you so much for being a lovely audience. Thank you.

to be with us, so thank you. We are here because we believe in AI’s transformative potential, and I’m certain you’ve heard a great deal about it over the past few days. Today, this session is about something deeper. The digital divide is rapidly becoming a compute divide. AI today is not constrained by imagination. It is constrained by infrastructure, by who has access to GPUs, to cloud capacity, to scalable compute. And that divide will determine who shapes the future of AI. Democratization in this context is not about catching up. It is about expanding who gets to lead. It is about ensuring that the next generation of AI breakthroughs are not concentrated in a handful of geographies, but are shaped by diverse talent, languages, and lived realities across the world.

And here, India is not waiting for permission. India is not waiting for permission. India is showing that it can be done differently. Through the India AI mission and through the compute capacity plan, mobilizing more than 38 ,000 GPUs as public infrastructure, India is building one of the most ambitious public interest compute ecosystems anywhere in the world. This is not incremental reform. This is infrastructure at scale. This is sovereign capability combined with openness. India is demonstrating that public interest AI infrastructure can be built in the Global South by the Global South and for the Global South. And this leadership matters because equitable access to compute is not just about hardware. It sits alongside access to data, open source models, talent, and institutional capacity.

India is proving that you can design AI ecosystems that are both globally competitive and globally competitive. And locally grounded. At the Rockefeller Foundation, we believe this moment requires moving from diagnosis to action. Philanthropy’s role is to be catalytic, to reduce risk, unlock capital, and convene unlikely partnerships that accelerate progress. Over the next hour, our discussion will unfold in three acts. First, what exactly are we democratizing? That’s an important question. Second, how do South -South partnerships and catalytic financing accelerate progress? And third, what concrete commitments can we land this year? If India’s example shows us anything, it is this. Democratization is not theoretical. It is operational. It is scalable. And it is already underway. The question now is how we accelerate it together.

Before we begin, let me take a moment to acknowledge a few leaders in the room. Shri Abhishek Singh who unfortunately has been pulled into another meeting but his leadership has been amazing his steadfast partnership and support has been something that I am extremely grateful for his vision of guiding this important work with clarity has been just spectacular Dr. Sarabgarg we are honored by your presence you have been in sessions since this morning and thank you for your leadership it’s truly a privilege to have you with us today my colleague Andrew Sweet who has joined us from across the world one of the sharpest lines of the Rockefeller Foundation and truly a force for good thank you for being with us today and supporting this conversation and of course I want to also thank Charu who has been working endlessly and very hard to kind of get us to this place thank you Charu for your leadership Martin, Vilas, Sean and Dr.

Shikoh thank you for lending yourself your voice and expertise to today’s discussion Your perspectives will help ground this dialogue in both ambition and action, and I know all of you are action -oriented folks, so we’re going to have something really cool come out from here. And last but certainly not least, our partners at Kalpa Impact, Sushant, Anish, Jennifer, thank you for being extraordinary collaborators and for helping shape today’s session. It is now my pleasure to hand it over to Dr. Gorf. Please, over to you, sir, or maybe I’ll hand it over. Okay.

Thank you. Thank you, Deepali. When I mentioned the report, I fumbled the name, so I’ll go again. Opening up computational resources for new AI futures, new AI world is possible. And this is something that the team has worked really hard over the last few months. And today is an opportunity when we release a working version of that report and invite inputs, feedback, comments, and suggestions, which we will work through over the next few months. This research helped us think through and work with the Democratizing AI Resources Working Group under the leadership of Dr. Saurabh Garg. And he’s here. So it’s a pleasure and a privilege for us to invite him. And the other panelists to release this report.

Thank you. Thank you. Thank you. Thank you. opening up computational resources or in fact all resources that are necessary for development of AI in public interest and for real world impact. I could think of no better person than Dr. Saurav Garg under whose leadership I think we have come a long way in not just the intellectual thinking but as he will tell you in terms of operationalizing how we can bring this to life for billions in the global south and also the other countries in the world. Dr. Saurabh Garg, please for your keynote.

Thank you and colleagues panelists great to be here and great to see a large kind of attendance that we have seen over the past few days in the AI summit and And there were seven working groups set up under the AI Summit umbrella. And one of them was on democratizing AI resources. I had the privilege to chair that group along with Kenya and Egypt. So I’ll obviously talk a bit on that. But before that, just to say that I think all of us are of the opinion that AI will definitely transform the world. I think the question is whether that transformation would be equitable, would be inclusive and aligned with public interest. And I think that’s really the issue which concerns a lot of people.

The AI Summit itself was built around three guiding sources. Sutras, the people, planet and progress. And therefore, the concept being that AI ultimately must serve human welfare. advance sustainable development and enable shared prosperity. I think these would be key background in the way these sutras were developed. And obviously, democratizing many of these resources would be key to that. During our working group discussions, we had the opportunity to talk to a large number of countries, people from academia, civil society, and other international organizations. And I think one consistent message was that most countries are not really seeking only access to AI, but also seeking agency in AI. And I think that’s key. And how the AI systems need to reflect each country’s own development priorities, languages, and social contexts.

From these discussions, there were six foundational pillars that we had to address. And we thought need to form the backbone of the collective roadmap for the future. computer capability collaboration connectivity compliance and context and I’ll just briefly speak on each one of these a bit compute no doubt is today’s defining barrier the access to GPUs accelerators high -performance clusters is a major issue for all AI ecosystems but the issue is how it can be made distributable affordable and reliable across and not concentrated in a few geographies and this would no doubt require us to look at whether compute can become a shared infrastructure in future or kind of a which supports public interest innovation and to the extent that we are focusing on innovation how that part can be a public interest infrastructure secondly infrastructure structure would not be sufficient there is a widening skills gap.

So how we can consider capability diffusion focusing on joint research, shared standards, open platforms and mutual learning. What needs to be done for this responsible deployment is so that we can link innovators to compute resources and citizens to trustworthy AI enabled services. Equally important would be governance. The governance framework needs to be robust enough to build trust, yet flexible enough to adapt to diverse social and cultural contexts. Open source and maybe modular AI stacks would help in enabling localization without creating dependency. So looking at some of these issues, on what mechanisms can be done to facilitate accessible and affordable computing resources by improving utilization rates and reducing transaction costs and also to lower barriers for access regardless of geography.

The working group looked at how this can be taken forward through a collaborative platform designed to expand shared access to compute data in partnerships. And the platform has been termed as Maitri, which is friendship in Hindi. Maitri, M -A -I -T -R -I, standing for Multi -Stakeholder AI for Trusted and Resilient Infrastructure, to be developed as a digital public good that countries can adopt, customize, and build upon. And obviously, it is a non -binding, voluntary, modular approach. depending on the context of each country, what kind of compute and what kind of methods can be used to have accessible, at least for innovators and researchers looking at data sets that can be put out, which are take care of the national laws and national protocols in place and look at models.

So what which are open source and which can be placed. So this this we envisage would help to at least ensure that portions of AI are a global public good, because we we we are focusing on innovation and research out here. And this would go beyond just a focus on hardware and platforms, but also in skills, institutions and governance capacity. I would just like to mention one area. The other is that the technology is a very important part of the development of the technology. but how perhaps it might proceed in future is also the fact that while infrastructure or compute seems to be the biggest constraint going forward as of now, that’s perhaps also based on the present models requiring large amounts of compute capacity and energy.

Going forward, would models retain this system of algorithms that they have, or would there be obviously small domain -specific niche models? I think yesterday there was a very nice remark made by Vishal Sikka, who mentioned that unlike when we talk of compute infrastructure, we are talking in terms of gigawatts, nothing less than that of whatever. But when you talk of a human being, you talk in terms of only 2 ,000 calories requiring a human being to sustain a computer. Which is not more than a 100 -watt bulb. for a day. So are we missing something out here and I think that’s a very important point that he made yesterday and that’s why the focus I think we need to have much more on the models and that itself might solve a lot of the areas that we are and when we’re talking of democratizing AI perhaps that’s the path forward.

So I’ll stop here and thank you all. Thank you for this opportunity.

We now transition to the panel discussion and may I request Andrew Sweet, VP at the Rockefeller Foundation who is the moderator for the panel. Please join us here on the stage. May I request other panelists, Dr. Shikogitao, Martin Martin Tisney Vilas to join us on stage. Yes and Sean sorry Sorry Andrew over to you

thank you Dr. Garg for those inspiring remarks and for the framing insight and perspective that you bring to this conversation all of the many conversations that you’ve had throughout the course of the week so we’re excited to continue and deepen the conversation today and very excited that we have five of the world’s brightest minds to discuss this topic these are all people that have been in the AI arena for decades, this is not new to them and all people that have deep regional expertise and global perspectives so very excited for this conversation today. We don’t have a lot of time we have about 25 or 30 minutes for the conversation so we’re going to dig in, we’re not going to have a number of speeches, Dr.

Garg’s speech will be the only speech that you’ve heard today but we’ll have a short series of provocations with actionable ideas for how we can move this agenda forward And so hopefully this conversation can be, you know, informal, back -and -forth banter. I think we’ll have one round of questions, but it would be great if we could kind of feed off of each other’s questions and energy because I know we all have a lot to say here on the panel and a lot of expertise to share. So I’ll briefly introduce the panelists, then we’ll dig in. You’ve already met Dr. Garg. He’s the Secretary of the Ministry of Statistics and Program Implementation for the Government of India.

He has been instrumental in shaping India’s AI governance and previously led the technology stack for the Transformative Adhar Initiative. We have Martin Tisné, founder of Current AI and public interest envoy for France’s AI Action Summit. Martin has spent 15 years building multi -stakeholder initiatives like the Open Government Partnership that we talked about earlier today to govern technology based on democratic values. We have Vilas Dhar , president of the Patrick J. McGovern Foundation. Vilas serves on the UN Secretary General’s High -Level Advisory Board on AI and leads one of the world’s largest philanthropic movements to AI for public purpose. my friend Dr. Shikoh Gitau founder and CEO of Kala AI a visionary from Kenya. She established Safaricom Alpha and has been a leading voice in ensuring that digital transformation in Africa solves real problems in education, healthcare and agriculture and finally, Shaun Seow CEO of Philanthropy Asia Alliance.

Sean is working to catalyze collaborative philanthropy across Asia, leveraging deep expertise from his time at Temasek and is CEO of Mediacorp so we’ll continue the conversation first question will go to Dr. Gerg India has launched the India AI mission with a target of 38 ,000 GPUs if we view compute as a public utility, much as we do with water and electricity what is the governance model that India is envisioning and should compute access be rationed or priced differently for public interest applications

so I would say that the focus is not on rationing but on intelligent prioritization I think that’s going to be the focus, that the impute capacity is an enabling platform, and as I mentioned, as a digital public good, at least that’s where innovation and research is going. So that we focus, and I think that’s where a lot of the philanthropic organizations would have a large role to play, given that their focus is also on ensuring that AI benefits all. So with that focus in view, how governments, philanthropic organizations, and the private sector can collaborate to ensure that affordable compute capacities are accessible to all. I think that’s the models that we are looking at, and that will ensure experimentation going forward.

Thanks, Dr. Garg. Martin, I’ll go over to you. Through current… AI and the Paris Charter, you’ve convened governments to discuss public interest AI. How do we move nations from being consumers to genuine co -creators? And quickly, you’ve also spoken about this looming data bottleneck. What do we do to unlock data sets for training without compromising privacy?

Okay, two big questions. Thank you. So, as you mentioned, we launched Current AI last year. We’ll be launching just this afternoon our first product, which is an open hardware product looking at linguistic diversity. I think I’ll be a little bit provocative to maybe start our session. I think compute is critical for obvious reasons. I think that from a financial, from an innovation, and from a sovereignty perspective, it is also possible to overplay it. I’ll tell you what my worry is, and I’d love to know what the panel thinks. I do have a worry that we could end up in a few years’ time in a world where we succeed in having compute capacity in inverted commas, in a number of countries, including in the global south, but where effectively the data centers are not used.

We’ve been talking to colleagues around the world. You do also have data centers that are effectively kind of white elephants and that are not used anywhere close to full capacity. And so I think for countries to be able to exercise sovereignty, they need to have contextual AI. They need to have contextual data in their languages with all of the diversity and the incredible richness that typifies their cultures available in order to create contextual localized AI that actually serves outcomes that people care about. And so while the compute piece is important, I think it’s one part of the issue. We need to talk about the data piece and we need to talk about the second part is the open source one.

So briefly, I think throughout the event, people talk about open source AI, that it’s a really good thing, that we’re all pro it. I think we also need to talk about how, from a philanthropic perspective, we resource the open source ecosystem. The reality of open source software is it’s mostly the top tier of open software is funded by large companies that are using it, right? Linux is partly funded effectively by volunteers working for SpaceX that are using it. There’s a bottom tier of dependencies in open source that are run on a shoestring, you know, by a few like critical, amazing people working overnight as volunteers. And there’s very few organizations, one of them, which is a part of the current AI roost, which looks at robust open source trust and safety, that are funding those critical dependencies.

So I think that for states across the world, in the global south and the north, to really be able to exercise sovereignty, and I’d love to talk about this a bit before, but I don’t want to hog the mic, we need to talk about compute, but also we need to be realistic about what the compute is going to be used for. So I think the data piece and the open source piece are really important. I think I’ve probably run out of time to talk about the data bottleneck.

Go for it.

Well, so the number… There are people in the room I’ve worked with for a long time on this issue. Velas, you’re one of them. Sushant, you’re another. I won’t name check everyone. I think it’s fantastic that there’s been so much innovation in compute and we’ve seen such change over the past 10 years. In contrast, I think it’s a complete tragedy that we haven’t seen as much, anywhere near as much innovation when it comes to data and specifically the ability for people to be able to share personal data in ways that both respect privacy and contribute to outcomes. And that’s effectively it. I think we need a huge amount more resources and thought, both when it comes to the technical side of the issue, and here, other than the side, I think that partly it’s solved, but enterprise users of AI have access to these kind of technical safeguards in a way that private users don’t.

And there’s a story that we can talk about if we have time. And then on the governance side, so for example, Velas, you and I have talked for a long time about different, and now there’s different forms of data stewardships, whether data trusts or others. To the day… I haven’t seen one that really scales to the level that we would want to see it scale. to and that I think we need a lot more resources, a lot more thinking there’s been work done but if we could harness even 20 % of the sort of like brain capacity of the world that’s going into compute right now I think we would be in a very different place. Thank you.

Excellent, thanks Martin. Actually Vilas I’ll go next to you because I think this reminds me a little bit about a recent article you wrote about the Indian Premier League as a model for how India builds world class institutions I re -read it this week in preparation for this conversation is there a similar IPL playbook for public interest compute or is the window for building these public institutions closing as commercial consolidation accelerates?

Well I can’t think of a more controversial topic to spend our time here in this conversation than cricket. It’s been a good week all around but I think many of the people in this room probably know. Before I start I just want to say Dr. Garg I want to acknowledge in particular your leadership on this work. I spend a lot of time with senior decision makers across governments and the conversations that we have had have really given me great hope for the combination of technological confidence but also an understanding of what this means across an ecosystem. And so I want to acknowledge your leadership in particular. Thank you. Look, this question around the IPL I think is great, right?

I mean, let’s not torture the analogy and take something really fun and then try to, like, tie it to AI. But here’s what I’ll say about it. I think in many ways what we need is a new institutional framework that goes from the elites who are participating in their own places to something that feels deeply participatory. And I think around compute infrastructure in particular, we are stuck in a model where we keep reengaging and renovating old concepts and try to describe a new world. I will tell you sovereignty has been the buzzy word of the moment, right? Everybody wants to talk about sovereignty and diffusion. Sovereignty as a Westphalian concept that goes back a few centuries tries to take the idea that ownership of pieces of silicon somehow magically results in outcomes and impact that transform lives.

Now, there are logical links. And, of course, there are codependents. competencies, but to simply say that we will site compute in a particular geography and so figure out a way to disconnect ourselves from the interdependence of the 21st century doesn’t really bring us to a good outcome. I’ll tell you the second part of this, AI diffusion. If you haven’t heard this already, every tech CEO here, this has been the buzzword of the moment. I spent some time yesterday with the prime minister and a number of tech CEOs who wanted to talk about their investments in India. Those investments in many ways followed the playbook of the PR press release. They were, we’re going to build a new data center, we’re going to invest in a new compute capacity.

But when you dig deep and you ask the next question, who will this really benefit? What value does this create for public impact and outcome? How does putting a large number of servers in a particular place result in that community finding an economic uplift, a benefit in economic opportunity, a sense of dignity? The conversation sometimes falls. flat. So AI diffusion to me in its core concept, the idea that you hyper concentrate technological capacity, compute data, and somehow the rest of the society benefits sounds a little too much like something that as an American I know too intimately as trickle -down economics. The idea that if we made the rich as rich as possible somehow the benefits would filter down to everybody else and it would work.

AI diffusion is a passive concept. It starts on the premise that we build technological capacity for a few and somehow it works out for everybody else. But there’s an alternate model and it ties directly to this report that’s been issued today and the work that we’ve been talking about. For AI to benefit everyone requires a direct and active impact. It requires us to step in and say what are the institutions we have to build that actually physically and metaphorically transform the idea of compute infrastructure to be something that everybody can use. It requires us to build the institutional layers and the capacity that lets a community that’s trying to solve a local problem know that compute isn’t the thing that holds them back.

rather the conceptualization of the problem the aggregation of the full stack of resource sets as Martin described that include compute that include data governance mechanisms that include the political agency of communities to participate and let us then turn that into that final app solution infrastructural development that actually leads to the outcome we’re solving for in many ways I think this is the great role of the institutions that are represented here on the stage and in this room for philanthropies to transform the capital landscape in a way that says great entrepreneurs and leaders like my dear friend Chico here and so many here in India that are building open source public access AI stacks don’t have to worry about the resource constraints of the private capital markets that they know that they can access governmental and substantive structural resources that let them build the tools that they want to and know that they have equitable access to the markets as well as a matter both of policy and as a product that they can go out and get to consumers and creators that they can provide a service that lets them people use it at scale.

And the last part of this, and I have to say this, is this doesn’t happen, as we’ve discussed in the private market, but it also doesn’t happen exclusively by going to frontline nonprofits and saying, now you’re supposed to be the builders. It requires us to innovate a new institutional set of intermediaries. I think of groups like Culpa Impact, which I think is an incredible example of a combination of technical sophistication, policy impact, support for government, that actually sits at the layer that connects these different elements and lets us build on top of it. I think this is the work that’s ahead. If we really think about pragmatic outcomes to this conversation, Andrew, I think one of the questions we might ask is, what are the institutions we need to build in the next 12 months that connect the dots around all of these different pieces and support this transformation at scale?

Dr. Shikha, I came across a recent article that you put out there saying that for the West, AI is a matter of efficiency, but for you, it’s a matter of life or death. You’ve been a champion for AI access. You were very active in this summit. You were very active in the Kigali Summit. We were together at the launch of the first ever AI factory for Africa together in April in Kigali. You’ve also talked about global tech companies. If they want African data, they should provide compute infrastructure in return. How do we formalize these reciprocal agreements, and what does a true India -Africa partnership look like that doesn’t just reciprocate global North -South models, similar to what Vilas was just talking about?

Thank you very much for having me. It’s always fun to listen to everyone here on this. I was hoping somebody was going to preempt some of the work that I was going to talk about, but lucky for me, I have some stuff to talk about. Thank you, Vilas. So when we talk about compute, it’s this amorphous thing. In fact, we launched an AI research lab in Nairobi, and we have some GPUs there. And one of the key things was like a demo showing up what a GPU is. And our PS was like, oh, my God, this is what a GPU is, because he’s never seen a GPU. And then I made sure, like, every time I’m speaking, I’m asking, how many of you actually have seen a GPU, not on the Internet, touched one?

Maybe five people. And this is everywhere. Every single room that we’re talking about compute, we ask the same question, have you ever seen a GPU? And so right now, five to ten people. So it’s this thing that people talk about. We need GPUs, we need compute, we need all of these things. And for us, it is very important, as an African continent, we had, like, our research. Colo came for the Global South a few days ago. Same question, how many of you have seen a GPU? about 10 people had never touched a GPU. How many of you need compute? Everybody raises their hand. But what does that actually mean? In fact, one of the panels, the starting point was like, when it comes to compute, we all need Jesus.

And I thought, how do we quantify this? So we, and I think we have already spoken to Calpa about this. We’re working, I think, on the same time about a framework. So we just released a compute demand index. Because we realized every time we speak about compute, people have ideas, they have thoughts, they have proposals. They don’t have the numbers to say that. We need GPUs. How many? We need megawatts. And the gigawatt, megawatt conversation, what does a gigawatt of compute actually mean? So we went ahead and said, for Africa, we need to, every time we’re having conversations with these governments, this is actually what you need. But you actually need to put money into it.

So we, our first index was, did it demand? And the second one is, is your country ready for this? which we are calling AI Investment Readiness Index. So I’ll give you some numbers. Africa needs 2 .5 million hours of GPU hours a year, 7 .5 million for the next three years to be able to start computing well. This is for training as well as research. That is something that I can work with. So when I come to India and say I need 2 .5 million GPU hours a year, how many of them can you give me? And we have this conversation with the UNDP in Italy, and they said, oh, we have 1 .5 million GPU hours that we can donate. We have 1 .1 million more to go.

Cassava is saying we are putting 2 ,000 GPUs. How many hours of GPUs with those GPUs, hours, not actual physical, how many hours of GPUs without those 2 ,000 GPUs actually provide for the continent? So we need to be. We have to start being very practical rather than being arbitrary on what we. want. Of these 7 million GPU hours we need in the next three years, Africa only has 5 % of that. So we are doing the math. We only have 125 ,000 of these GPU hours a year, which is like times three of that for the next three years. So you’re solving, when I go to villas, I’m saying I need these GPU hours. It’s very practical as I’ll say I can be able to do half a million GPU hours.

So it’s not just going with an arbitrary number. I need GPU hours to be able to put this. And for us, that is important. But for me, it is the conversation about investment, and that’s the conversation that we asked. How do we have this South -South collaboration? How does we have this India collaboration? How does it actually look like? There’s the paradox. Everybody, as he said, as Martin said, everybody wants sovereignty. Everyone wants to talk about diffusion of AI. But what does that actually mean? Do we actually need it? So I’ll give you two examples of my two favorite countries. Hopefully none of them is here, actually in Nigeria. So there’s something you’re calling the Nigerian paradox.

Nigeria is the number one country in the computer demand index. Why? Nigeria is doing very well. It’s 110 million Internet users, a huge population. They’re doing very well in terms of, like, e -commerce, financial services. So they’re up there when it comes to why they need compute. And we’ve seen this in India. India is very high there as well when you’re doing the same exact thing. But what about investment readiness? And investment readiness is are they able or capable of running a compute facility? Do they have power? Do they have the talent? And I love, I think, what Mateen and the minister spoke about, what we think about. When you think about compute, you don’t think about just GPUs.

It’s a whole stack of things. It’s talent. It’s governance. It’s all these things. And you’re thinking about. When you think about investment readiness when it comes to compute, you have to look about all these things. Because I can give you GPUs, as he said, and I’ve worked in digital transformation for the last 20 years, and I work for the AFDB as a digital transformation lead, and we’ll buy computers and go three years later, and they have never been powered at all. And that’s the case that is going to be with GPUs, because you’re going to give countries these GPUs. If they don’t have talent, they don’t have the power to run it, they don’t have the data sets, they don’t have models, they don’t have use cases to build on top of it, you’re wasting that money.

And that’s where the investment readiness comes in. So we’re talking to countries, and we’ve had this conversation with African countries, is there’s no point of investing all your dollars in putting a compute facility. Get your talent ready. Get your data sets ready. Have strong use cases that people can back. Then, with all of that, can we then define what are the demands? Can we make money? you need. You don’t need a gigawatt Kenya, you do not need a gigawatt of compute to be able to run. Maybe you need a 200 megawatt facility and that’s where we want. So coming back to the question, how do we interact with India? This is our demand. Burundi might need 50 megawatts of GPU.

Can India be able to facilitate that? But it’s not just about facilitating the GPU, it’s what is the GPU in service of solving for health, education, agriculture. And when you have clear use cases, then the GPU demand becomes an obvious ask. And I think bridging that and convincing governments especially of bridging that gap is what we need to be able to do. And then the governance framework actually comes to play. Thank you. I know that’s a lot.

That’s great. Thank you. Thank you, Dr. Shikha. We’ll go through Sean, and then I want to keep it kind of informal for the remaining ten minutes after Sean speaks, so any reactions to any of the comments, and then we can do a lightning round if we have time, but if we don’t have time, that’s fine as well. Sean, over to you. The Philanthropy Asia Alliance brings together 80 members and partners to address Asia’s interconnected challenges through collaborative philanthropy. Is there an opportunity for Asia’s philanthropic networks to coordinate shared compute and infrastructure, pulling resources from places like India, Indonesia, and other nations, rather than competing, and what would unlock that collaboration?

Thanks, Andrew. The advantage of coming last is that I could say I agree with all of them. Actually, I’m going to add to the much maligned word called compute. Maybe we could end the panel right away. I’m going to join Martin in actually agreeing that compute is actually a bit overrated. The ownership… of compute… So when you think about the stack, I’m going to add another way to frame the conversation, Jensen Huang’s AI stack. When you think about energy, hardware, compute models and applications and the top layer applications is really what will drive and value capture for the economics as well as the impact, social impact. Really the stumbling block is probably energy at a bottom level.

And thankfully for many countries in Asia, the costs have been driven down because of the abundance of hydro, solar and wind. Then when you think about the next layer of hardware, I mean that’s obviously dominated by China, Chinese and American players. And when you think about the compute level, I understand why we fuss over compute because the Americans own 75 % of the GPU cluster performance. The Chinese 15%, Europeans maybe about 4%. and the rest of us are only like 0 .1%. I think even India is just 1 % of that. But I think the issue is actually deeper than just the ownership. I mean, if you think about what it needs to get the work done, it’s more access. So the question you’ve posed me about sharing of compute, for example, between Indonesia and India, I live in Southeast Asia, and that’s why Indonesia is like a couple of hours away from where I live.

And we know the situation in Indonesia quite intimately. There’s data residency requirements, and that’s why there’s a build -out of data centers. Think also of the physical limitations of actually the latency of sharing compute between India and Indonesia. For example, 10 ,000 kilometers apart, when you think about the latency of what, 50 to 100 milliseconds, it’s just not going to work for the sharing of compute between Indonesia. and India. Attractive as the idea is, it doesn’t work. I think they’re just physical limitations, data sovereignty, privacy issues that prevent that from happening. So I just want to look at the positive side of what’s happening. When you think about the cost of compute is coming down, when you think about the emergence of new clouds, GPU for a service, I think these developments are actually going to be good for the unleashing of AI, for social impact, for economic capture.

So the way you can think about it is, how do we then make it a little bit more accessible for startups, for impact organizations? Maybe the way to think about it is really, how do you think about aggregating demand so that you can actually negotiate with the new cloud providers and get a cheaper pricing? How do you then think about philanthropy coming in? And to subsidize some of the compute costs. and I think I kind of agree more with the observation that you really need to go beyond just infrastructure you need to think about the ecosystem you’re building I think the skills gap in Asia is actually huge and that could be really what’s stopping us from really optimizing maximizing the power of AI in what we want to do Is that too long?

No, that’s perfect I’m not sure if anybody wants to react to any of that Martin, I see you scribbling furiously maybe first reaction to you

No, I am scribbling. I’m scribbling because I’m thinking about your points I’m thinking about the points of the panel and I’m thinking about the term sovereignty So my scribbles are to your point about the Westphalian concept of sovereignty that’s about the ability to make law within your territory and it’s a very global north concept and it’s a notion of territory which has physicality and I’m just what I was scribbling was the physicality of the territory, it’s like we’re very focused on the physicality as you were saying on the GPUs of the bricks and mortar, so we’re going to be okay because we’re going to be sovereign on this data centre, the data centre it’s on my territory and what got me thinking is other concepts of sovereignty such as when I was spending a lot of work working on data calamities and data stewardship, thinking about indigenous data sovereignty which is a different type of concept it’s a more relational concept than a territorial concept, right?

It’s about a pre -existing an inherent authority a relational authority over that which makes up a people, and so when we were studying for example indigenous data sovereignty in the Maori context in New Zealand the Maori community any data that in any way involves Maori, the Maori community … legacy is part of the Maori community so I think that there’s something here in thinking about a very in some ways a quite rigid approach to sovereignty which is about control as mentioned and one which is more about agency and which is more relational so that’s what the panel has got me thinking and I’ve been doing some writing and thinking with colleagues and friends around the notion not of a sort of like a controlled national stack but a global open resilient collaborative stack and that’s not one at all and just I’ll finish with that, that doesn’t mean that like all the data is open and anything goes and anyone can extract your personal data and you’re back in a sort of Zuboff you know surveillance capitalism world it’s one where it’s a question of choice and agency and the what you wish to exercise authority over and how.

That’s my scribbles. Thank you.

As you can tell, when we get on a panel with people you love and respect, the conversation just flows. So I want to build off this point and a little bit of what you said, Chico. I want to take a different tack to this question of agency, which is if I had asked any development leader in the world 10 years ago, if you could have your dream of an extra gigawatt of energy capacity in your country, what would you do with it? I can’t imagine that any of them would have said, well, I want to use it to run a bunch of computation on things that may or may not have short -term economic value for my country.

Andrew, your organization has been incredible around the world at building capacity and grids in power production, in ensuring that people can use power for development. And yet somehow, I think for many of us, we are surrounded by conversations where now the question has become, how many megawatts and gigawatts can you put into compute for AI? It is a fundamental challenge when you think about what are our priorities in development. Again, going back to core principles of human rights and dignity and participation in the world, to say governments who have limited capacity should now all of a sudden be focused on this topic. It brings us again from this question and this shift. In many ways, I acknowledge that the traditional conversation around compute is one of breaking over -dependence on the American AI stack, on other international players that are coming in.

But the response to over -dependence isn’t internal dependence, it’s interdependence. It’s saying if there are places that have incredible capacity and even potential to drive, as Shiko said, the availability of compute hours, how do we build interconnectedness that lets that be a mutual value exchange? Not merely, again, clients who have to go to another country and say, please give or let us buy compute, but rather the products of that compute are going to be to build the infrastructure that you can then use in your own country. To allow for centers of excellence that allow for local capacity and local competence to drive what gets built and to allow that to be the new tokens of international trade in a way that leads to a much more connected and shared prosperity rather than descending back to that 200 -year -old concept of how do we make sure that we’re competitive in an adversarial frame.

I recognize that what I’ve just shared with you is maybe not where the dominant private sector conversation is. And to those who would oppose it, the primary critique is, well, that sounds quite naive. And yet we’ve seen it happen. We’re seeing it in the few areas of hope in the multilateral system where we’re actually finding that technology governance is something that brings everybody to the table, that lets people engage in meaningful shared outcomes. We’re seeing the seeds of it. The question is whether we’re going to let them sort of die out in the sun or if we’re actually going to water them, invest in them, in order to grow.

Great. Dr. Garg, any final insights?

I know there’s a little time, but just one thing I would say that perhaps we need to spend a bit more time going forward on the frameworks that will help ensure public interest frameworks looking at things beyond compute, looking at models, looking at talent, looking at data, how that can be shared and interoperable and in a manner which takes care of public interest. So I’ll just stop it out there.

Well, thank you. Thank you to the Indian government. thanks to our partners at CalPA for putting this together especially for the authors this is now officially out there I think you have until March 31st to read the copies are available out there you have until March 31st to review the document and submit your reactions thank you to the panelists, really appreciate it enjoy the rest of the summit, thank you I think the NDIA team wants to hand over some souvenirs from the panel Thank you Thank you. Thank you. you you Thank you. Thank you.

say, you know, it’s yet to unfold. We don’t know what the impact is and it’s yet to unfold. I believe that that contention is actually largely untrue. And let me tell you why. When you talk to companies privately, publicly they will not own up to the potential job disruptions as a result of AI. And partly that is because many of the big companies actually are known to be formal job creators, right? And that is a very important part of their image and their contribution to economies and societies. But when you talk to them privately, in India especially, our research shows that they will own up to anywhere between 30 % to 40 % time saving, right, productivity gains, which then translates into significant workforce cuts.

We already have plenty of empirical evidence that suggests that… that AI systems are enabling surveillance, they’re influencing decisions about who gets work, when, and what entitlements people have access to. We also know that AI systems are grossly exacerbating inequality. If you just look at the market caps of some of the top technology companies, you know, NVIDIA’s $5 trillion market cap, right? So there’s a massive accumulation of capital that really, you know, capital share is growing and labor share of income is getting smaller and smaller. So I guess, you know, this discussion that talks about social empowerment, a key question in that is the question of the impact on jobs. And the question that I, you know, put out there is, so if you even buy the idea that we don’t know, that we don’t know what the impact is, what the impact is going to be.

Can we afford to just wait, right? Or do we need to take every action possible in terms of regulations, in terms of building social institutions, in terms of really working to build systems that can manage this inevitable evolution of AI, whether we like it or not. The last thing I’ll say is just, you know, yes, there have been technologies before. Yes, they’ve had their own forms of inclusion and exclusion. But at the end of the day, this is the first time where you have the very pioneers of that technology, Jeffrey Hinton, Stuart Russell, Dario Amadai, the very pioneers of the technology themselves are ringing alarm bells. And would we not be wise to heed them?

So with that, I hope, provocative context setting, I am really grateful. On behalf of the Just Jobs Network, again, with support from IDR. CNF CDO to welcome our really esteemed panelists. Mr. Anurag Bihar, who is the chief executive officer of the Azeem Premji Foundation, has very graciously agreed to chair this conversation, moderate the discussion. We have Dr. Julie Delhanti, who is the president of IDRC Canada. Thank you, Julie. And Ms. Sandhya Ramachandran Arun, who is the chief technology officer of Wipro Limited. Thank you so much for being here, Sandhya. So, Anurag, over to you.

Thank you. Thank you, Sabina. Good evening, everybody. Thank you. There’s so much. There’s so much investment going into AI. why is it going into a why is so much investment there in AI? We are in the fifth day of the AI summit. So this is like the 42nd kilometer of a marathon. Right? At this stage, such investment has to be justified by some monetization. And where is that monetization going to come from? It’s either going to come from productivity, which comes from labor reduction, or it is going to come from new products and services or both, a combination of both. That’s where it’s going to come from. Right? We will talk more about that. At this moment, my job is easy.

I’m going to just ask Sandhya, because she’s the representative of the technology world here really, that which way is this technology headed? And in very simple terms, what is she seeing its implications on jobs? I mean, what kind of jobs are going to get displaced, destroyed? And what kind of jobs are going to get created? and what’s the underlying dynamic because of which these jobs will be created and the jobs will be destroyed. So how does she see it in the world of technology? Let’s start with that.

Sure, thank you so much. Thanks, Anurag, for the question. So as far as the tech industry is concerned, we are really witnessing a very huge impact of the AI evolution as a disruptor. We’ve had to revisit how job roles are created. We’ve had to revisit how talent has to be reskilled. And we have also revisited the responsibility, not just in terms of security, safety, but also in terms of what does it mean to our colleagues and our hiring. I think initially there was a huge amount of fear that we would not hire from colleges, which is now… despair because we’re broken. continues to hire from colleges, and so do our competitors. But the criteria for hiring has shifted to a more nuanced, a more calibrated way of looking at learnability, looking at whether a person communicates well, technical ideas, looking at whether a person is adaptable.

Because AI is a technology that is changing as we speak. So no one can claim to be an expert in AI and remain that way for the next five days, possibly, because there’s things that’s going on changing every day. With regard to our own talent, we have created role personas, and we have created very specific learning modules on how the role changes with AI. And everybody from the board to the CEO down to the youngest employee is going through a very calibrated learning process. And there is also a very… calibrated way in which services and ways of working are changing. So to that extent, we see a change. We are not seeing a displacement because most of the work that we do is consultative in nature, inspired of the market valuation erosion that we saw some time back because of a news from Anthropic and Palantir.

The insiders in the technology world were already aware of the transformative nature of these solutions coming up. And we have already been using these solutions significantly for over a year. So from a market sentiment point of view, possibly there was an erosion, but from a technology impact perspective, we have been bracing ourselves for the change and our journey of transformation continues.

I just have a follow -up on that, and then I’ll move to Julie. I’ll put it very, I mean, let’s say, a very, very simple, commonsensical question. Which is that, we are hearing about these tools where coding has become so much more easier, right? So, and this is not just about Wipro, it’s about the IT industry in general. So if coding is becoming so much easier, and 50 % or 70 % of coding can be done by these AI tools, then isn’t it inevitable that IT sector jobs will be lost? Or if there’s business or volume growth, much less hiring will happen. So that’s part one to my question. Part two is, if you move away from the IT world, and if you go to let’s say design and marketing, or, I mean, let’s say my world of the academy, the world of research, so many of research assistants and those of you who have used research assistants or work with research assistants, so much of that job is being done easily by AI.

So part one of my question, if coding is becoming so much more efficient, isn’t it inevitable jobs will be lost? so much hiding will not happen, whichever way. And aside from that, in the outside world, in other industries, what is it that you’re seeing?

Sure. Let me just address the coding part of it. I think for over 15 years, the industry has been trying to explain to the outside world and as well as to the talent aspiring for careers with us that we do not have coding roles primarily. Coding is a very small task in what a software engineer does or a software developer does. There is the need to understand business outcomes. There’s a need to understand customer experience. There’s a need to understand architecture and what is a well -engineered code, right? So this is not new today. This has been in existence. I mean, I’ve been doing digital transformation for the last 15 years, and we’ve been trying to change how the world thinks about these roles.

Yes, the day is here when coding can be completely handed off to an AI agent. And that is indeed a fact, right? But the fact that supports the success of this code in business is really the ability to have a human oversee the design, the engineering, the architecture, the security, as well as delegating the coding work to an agent. So the role of a junior developer really becomes that of a little manager of AI, as opposed to saying, you’re displacing my job. The person’s actually going up if the person really is aware and aligns to what the organization needs in terms of figuring out what is required. And those are the trainings that are happening.

That’s what’s happening in terms of selection. We now have COEs inside engineering colleges where we are talking to universities about this as well. And what about other industries? whatever you’re seeing? So other industries we work with, there is a variation. So if you think about it, marketing, there’s a lot of work that gets offloaded. The strategy, the planning, the oversight on execution, the ROI on marketing still remains a strategic thinking job that remains with humans. But you can generate a lot of good quality visual, audio, and video content using AI today. And probably it’s making marketing a whole lot more efficient. Now, if you take finance, for example, again, a lot of processing gets taken over by AI, but it still needs a human to bring in wisdom in terms of how the data gets interpreted, how decisions are being made, and also to make sure that the AI aligns to human values in some sense.

So those kind of changes are happening in these functions. And that’s why I’m so excited to talk to you today. And I’m so excited to talk to you today. And I’m so excited to talk to you today. And I’m so excited to talk to you today. And I’m so excited to talk to you today. Industry -wise, there is a lot happening positive, I would say, in, say, healthcare, for example, even in banking, for example, where we are able to fight financial crimes a whole lot better. In healthcare, we are augmenting technicians, clinicians, and doctors with more intelligent input for decision -making. And while AI can make the decision, you don’t allow it to make

So, Sandhya, just put a pin on something that you said, and I’ll come back in the second round. You used the word human and wisdom. So just put a pin on that, and I’m going to come back to that in my second round. Julie, if Sandhya was less than as optimistic as she is, she wouldn’t be representing the tech world, you know. So one should expect that she’s as optimistic. she is. But what I wanted to ask you was that, you know, eventually, and, you know, from your vantage point, you know, you’re seeing how governments are dealing with this evolving situation, and not just an AI safety and, you know, all the other things, but particularly on labor markets.

So how can governments and institutions govern AI responsibly, such that any disruption in labor markets is sort of minimized or handled well, or the transition happens well? So let’s assume this picture that Sandhya has painted, that, of course, there’s something that’s going on, the reproductive, like she talked about the marketing and advising. So some people are going to lose jobs there. So what should government institutions do? How does one govern this situation, such that the benefits are maximized? And I’m talking particularly about labor. markets, not the other stuff, while harms are minimized.

Yeah, thank you so much. I’m going to answer that question, but the last question just made me think about two things. One was, you know, I’m old enough to remember when computers first came around in the 70s and, you know, what we thought would happen with computers and the job losses that we anticipated. And, of course, we did lose jobs. There was a lot of labor disruption related to, you know, typing pools and different kinds of ways. But at the time, even home computers, nobody could even fathom what you would do with a home computer. The conversation then was that home computers would be used to develop recipes and that you’d have recipes because homes were only where homemakers were.

People couldn’t even, there’s such gendered ideas that people just could not understand what you would do with a home computer. So I think in the same way, some of what… is going to happen with AI in the labor market, we may not be able to anticipate just yet. So just as a reminder of where we came from with other important technologies. But when it comes to governance, I think the important issue is that it’s not really only about the technology, it’s really about institutions, it’s about workers, and it’s also about research. So when it comes to institutions, really without the kind of strong institutions in countries, regulatory institutions, labor institutions, strong research ecosystems that are able to really understand what’s happening in the labor market, I think it’s very difficult to end up having a strong regulation of what’s happening in the labor market.

So just those institutions are incredibly important to understanding where job losses might be, where biases might happen, and really investing in people and institutions is something that has to go hand in hand with our thinking around technologies. Another… Another area is around making sure that when we’re thinking about new technologies, that we’re making it very human -centric. And one of the things that the AI4D program does when we think, what do we mean by human -centric? It’s really about making sure that we’re co -creating new technologies with the co -creation of workers, of communities, of employers, so that we can understand how to enhance job quality, how to enhance productivity, rather than increasing inequalities or changing who benefits.

So really understanding who benefits, who’s going to face the kinds of disruptions is really important so that we’re not thinking about that as an afterthought. That we’re really shaping AI systems using that knowledge. And similarly… I think the importance of research in, and I’ll just give an example from our AI4D work is we’ve done a big research program with partners in sub -Saharan Africa that’s looking at, that’s collecting household data, firm level data, worker data, to understand what the real world impacts of AI are on labor markets. And it’s that kind of tracking, who’s going to benefit, understanding who’s going to be displaced, and how the tasks and skills are really changing that’s going to allow governments to better design and think about what kind of skills development they need, what kind of social protections they need, and how to support labor rights.

So really, I think growing AI responsibly doesn’t mean avoiding innovation or avoiding change, but it’s really about shaping AI so that it, it does strengthen labor markets and supports workers and creates more opportunities.

Thanks, Judy. Thank you so much. I’ll move to Sabina Sabina I mean since you are the labor market expert here amongst us and the researcher what is it that you see what is it that I mean there’s so much of news we have had this five days of this grand summer what is really going on what do we understand what we don’t understand in the context of the impact of AI on jobs how do you stack up

so just a little tongue -in -cheek we go back to the 1600s we’d asked chat GPT then if Galileo was correct it would have said no way right so this technology you know for all the possibilities that it brings notwithstanding it is not just a technology we can’t just at AI as machine learning, large language models. It is a system, it is an instrument that is being utilized for social, political, and economic engineering. And my job is to look at the impact of that in labor markets. So if we limit ourselves just to the question of how many jobs will be lost, how many jobs will be gained, that’s A, not even an appropriate question.

Two, I agree with my fellow panelists that we don’t necessarily know what sort of new possibilities there might be. But what we do know, what we already see, is also something that Sundar talked about, which is the efficiency gains. And any time there are efficiency gains, there are layoffs. And please, you do the research, right? Like, I do my job. But look at the newspapers. Companies are laying off thousands of workers already. All the big tech companies have in recent years been laying off workers. Now, sure, they can say that this is a confluence of many factors. It’s not just AI, and most of them will not just ascribe it to AI. They might ascribe it to macroeconomic conditions, to the confluence of various other forces like the pandemic or trade shocks, all of which is true.

But AI is one really big disruption that comes on top of all the other disruptions, and there’s already plenty of evidence that is suggesting that these disruptions are not just changing the quantity of jobs in terms of how many companies are already laying off workers. Again, I mean, we’ve heard also projections from the, tech companies themselves, right, what the possible projections are. of disruptions and layoffs are going to be. But we also already have evidence of people being laid off. But then on top of that, I would say let’s look beyond just how many jobs are lost and how many jobs are gained to actually look at, I mean, take the gig economy, for example, and algorithmic management of gig workers.

That is a labor market issue. If a gig worker is wronged, the platform just, you know, they just get kicked off the platform. There’s no mechanism for redressal because it’s an algorithm that’s managing the worker. So who do you talk to? I mean, I can go on and on and on. Now, we might be separating out platforms from AI, but actually the algorithms are AI, and it’s embedded in a platform economy that is increasingly becoming the architecture for transactions, and it’s deeply troubling. And then the last thing I’ll say is, so I’ve already said… like in terms of quantity of jobs, we are already seeing evidence of layoffs, right? We’re already seeing the evidence of layoffs.

It’s just that people aren’t necessarily able to pinpoint and ascribe it to AI. That’s point number one. Two, we need to go beyond the question of quantity of jobs and also look at the impact of this technology on quality of jobs. And third, we need to really deeply think about, again, to Julie’s point, the architectures that can help mitigate some of the potential adverse effects of this technology, both on the quantity and the quality of jobs. And we don’t have the luxury to sit and wait and say, hey, let’s get the empirical evidence and then we’ll figure out what to do. That will be way too late, right? So what do we need? We need countries to think about competition policy.

We need to look. We need to look very closely at tax policy. We need to look very closely at how labor laws need to change. We need to look at social protection systems. We need to look at skill systems, everything that Julie just mentioned, right? But we have to start from an urgency about this is having a huge impact already. It is likely to be, you know, even bigger, and we don’t have the luxury of time to just sit back and wait and say, hey, we need more empirical evidence before we figure out how to mitigate the negative or potentially negative circumstances. So that is what I think is, you know, really, really urgent, that everyone get on that bandwagon and say we need to create these systems and ask for them and do it in our work and do it in our advocacy.

Yeah, thank you. I’ll just follow up. I’ll just follow up with it. So, and Julie, please. Pardon. for saying this. I’m saying this tongue in cheek and all my friends and colleagues here who are not from India please pardon me for what I’m going to say. So, you know, we Indians, why should we care about all this? And the reason I’m saying that is because, you know, well just about 9 or 10 % of our employment is in the formal sector. So even if there is huge disruption in labour markets, maybe 2 % of these people are going to lose their jobs, right? So why should we care about all this stuff? Do you have any comments?

I do. You can be sure I do. You can be sure I have a comment about that. So if you look at the numbers, we are more than 90 % in India in formal employment. So Anurag’s exactly right. He knows his numbers. So, you know, essentially what you’re saying is 1 out of every 10 people stands to be potentially affected, right? That’s one way. of looking at it. The other way of looking at it is we have such few good jobs, right? We have such few jobs in the formal labor market. Only one in 10 people get to have a formal sector job. And now you’re taking that away as well, right? That stands to be disrupted. So again, we’re moving to a world of work that is much more precarious, much more insecure, much more uncertain, where workers don’t, they’re not even called workers anymore.

We call them self -employed contractors. They have no health insurance. They have, you know, this is the precaritization of the labor market. So not only do you have, you know, pandemic, climate change, energy transition, trade shocks, and AI destruction, but you have a world of work that is much more precarious, disrupting everything, but you also now are moving to a place where work is becoming more and more informal. Formal jobs are jobs are being, you know, gotten rid of in the name of, please apologize, in the name of efficiency gains, right? And so, yeah, so that’s why in India we should be really scared because we have such few formal jobs. And then imagine if you have these jobs in the IT sector in Bangalore disappearing, all the workers that used to go to bars and restaurants and get loans to buy houses and cars, that starts to disappear and it has cascading effects across the economy.

So, you know, so the impact of this is definitely in the global south. It is definitely beyond the few formal sector jobs. And it’s deeply disturbing. And we need to actually work to understand from technologists very clearly, you know, how these efficiency gains are going to happen and how they’re going to, how. What can different governments. and so on, and for architecture, public architecture, manage some of these changes. So we do need to care. Definitely need to care. We need to care urgently.

All right. So I’m going to come to Julie on this and come back to you, Sandhya, because I put a pin on something that you said, right? So, Julie, I mean, let’s assume that the alarm that Sabina is raising is at least half true, right? It’s more than half. You know, I have a deep conflict of interest, and I’ll tell you once I’m sort of done with this. So, Julie, how can, you know, what are the lessons that you’re seeing across countries, you know? You’re seeing the vast landscape, right, and IDRC has a view across the continents. So what lessons can be learned? From across the continent. such that AI is able to create opportunities, right, part of what Sandhya talked about, and doesn’t really deepen inequality or it minimizes it.

What are you seeing across the countries? Something, some good stuff.

What is that? What is that regulation? And I think one of the – we have this AI – the Global Index on Responsible AI that some of you may have heard about. It’s been talked about a lot during the conference, or at least some of the sessions that I’ve been to. And really what that is, it’s the largest global rights -based data set on responsible AI. And what is distinctive about it is that it includes a dedicated focus on labor protection and the right to work. And by providing that country level, that sort of comparable data, it looks at 138 countries. So by providing that comparable data, it’s helping governments to understand what they might need to do better, what some of the issues are, how they can improve.

So really using that information to support governments in understanding what is the regulation, what is the solution that they need, not just – You know, it has to be based on some evidence. And I think the third big thing, which won’t be a surprise to anybody here that I’m saying this, is that we really need to have good evidence, and evidence really matters when it comes to these issues. So tools like the Global Index on Responsible AI really allows policymakers to move beyond kind of the abstract must -fix regulation to assess how governance of AI actually affects people’s rights, affects their jobs, affects their working conditions, and supports more proactive policymaking on labor regulations, again, skills, social protections, et cetera.

And I think equally important is that we’re still learning. There is no standardized, here is the regulation that you need codified. Through the kind of work that we’re doing, I think we’re learning what’s the balance between… supporting innovation… and still supporting regulation and safety. And I think working together across many countries to share that kind of information is what’s going to support us in finding the right tools.

Thanks, Julie. I’m going to come to you, Sandhya. But I just want to disclose something to all of you. That’s my conflict of interest. You know, Sabina is a labor market researcher, and naturally I would think she’s saying what she’s saying. Julie represents IDRC, and therefore she’s saying what she’s saying. Sandhya is the tech person here, so she’s saying what she’s saying. My problem is I’m responsible for this organization, Azeem Premji Foundation. And my problem is the following. My problem is that the foundation owns about 70 % of Wipro. Okay. So whatever is good for a tech company. is good for us, right? On the other hand, my job is not to take care of the technology and this world.

My job is to take care of the most vulnerable people in the country, right? The very poorest, the most marginalized, those who have no recourse to social protection. That’s my job. So I am a deeply conflicted person, right? Very deeply conflicted person. And I wanted to disclose that because I’m going to come to that towards the end. And it has a specific bearing on the question that I’m going to ask Sandhya, which is, you said something fascinating. And I want to put a pin on that. And I’m pulling your leg, you know, which is that rarely do you hear such words from a tech person. She talked about human care and wisdom, right? Didn’t she?

Okay. So, you know, really, my takeaway from what you were saying is that the tech tech stuff, you know, the coding and that kind of stuff, that can get automated. but something that is human understanding people understanding desires how do you work with people that’s what is hard to do and that’s something that you’re already seeing right so would you want to sort of comment on that

yeah so the stereotype of techies aren’t human is a little unfair I think so don’t anchor it in your heads but then yeah so where do I start at the end of the day what does technology consulting and technology services try to do they try to help our client businesses become more successful and our client businesses in turn become more successful when they are innovative when they are creative when they are growing when they are growing when they are making their business and they are doing their business and they are doing their business and they are doing their business profitably and they are doing their business and they are doing their business and they are doing their business and they are doing their business and they are doing their business Or if they have already reached a state of maturity, they are trying to bring in a whole lot of efficiencies as well, right?

So it’s the S curve where you have an idea, you nail it, and then you kind of scale it, and then you kind of start sailing. And when you’re sailing, that’s when you become a big battleship and you have to focus on discipline and efficiency and ensure that you’re making profits just the same even while you’re running this big ship. But then the cycle doesn’t end there. It kind of keeps going. You keep coming up with new ideas, you keep scaling it, and you keep sailing it. And so profitability starts off with an investment, it grows, and then you have to become super efficient to remain profit. And I’m saying this to my boss because every dollar that we earn funds to the tune of about 66 cents whatever efforts the KMG Foundation uses for welfare, right?

And I think it’s a beautiful thing. It’s a beautiful thing. It’s a beautiful thing. model and I don’t think an AI could have thought of it. So therefore I do believe very strongly that creativity, wisdom, vision, foresight, human centricity is core to any technology disruptor that comes about. So if you imagine the days when there were horse carriages all the horses would have been kind of crowding the roads and people would have been going from place to place and at the end of the day you would have had a whole lot of methane which would have kind of ended the year a long time back because of global warming. But yes, vehicles did come and you did have carbon fuel and the evolution continues.

So I don’t think technology is going to stop. So human ingenuity is going to keep bringing technology disruptors. These technology disruptors are going to be more and more exponential in terms of what they can do. And it is up to humans to figure out how to create policy, how to create a governance mechanism, and how to ensure that we derive benefits, mitigate the risks, and at the same time ensure that humanity is at the center of all of this. Right? Now, this is easier said than done, but we’ve done it with nuclear energy. Despite the disasters, the fact that you and I are still alive today and thriving and living a better life than we ever lived in the last 100 years is an example that, yes, you could have accidents that are preventable, but accidents are created by humans.

And it’s up to the leadership to ensure that they put the required guardrails. It could be policy. It could be governance. It could be guidelines, whatever you call it. And you can even hire a leader. And you can even hire a leader. some of the

Yeah, it’s good to hear that, you know. I’m just going to come to one round and then perhaps have the last word, if I may. Yeah, okay. So, Sabina, what’s your take? What should we do? What should we do, really?

So I’ve already kind of said what we should do, but first, Sandhya, everything you said really resonated with me, right? And I fully agree that, you know, that the humans have to take responsibility. I can think of a few very worrying scenarios where there are leaders in the world that have access to, you know, nuclear weapons that perhaps… shouldn’t have access to nuclear weapons, right? So how much confidence do we have in people, and particularly when you look at the overall trend of growing precarity? Again, take India alone. Fifty -eight percent of our employment is now self -employment. It is not, you know, and these are people, workers, that have no coverage of health insurance or any kind of safety net.

Add to that the fact that, like, there’s all these different forces coming that we don’t know, you know, if AI disrupts jobs or pandemics happen. We all saw what happened with migrant workers walking back to their villages, hundreds and thousands of migrant workers, right? There is a lot more precarity in the labor market than there ever has been in the past in modern history. And the problem is that regulation, and the regulation of the labor market, and the regulation of the labor market, and the regulation of the labor market, across the globe are getting weaker and weaker in this respect. And then we don’t have precedent, as Julie said. Like, we’re still trying to figure out exactly what we should do, right?

But I will say, I mean, I’ve said many, and I will say that, you know, in the meantime, AI is different because this is also the first time research is now showing, the first time that the current generation of young people have shown cognitive decline, right? So, I mean, rates of depression, rates of anxiety, cognitive decline. How does cognitive decline affect your ability to operate at work and then be replaced by machines that are more efficient because you’re getting stupider? Like, right? Sorry, but this is a really worrying scenario. So what should we do? I think I’ve said this. Multiple times. Regulation and building of social institutions. institutions, but I’ll take Julie’s challenge and say, okay, let’s go a level deeper.

I think we need to look at competition policy very closely. We need to look at antitrust. We need to look at tax, and within tax, we need to look at, you know, how do we do look at the full gamut from, you know, certain kinds of transaction taxes to what person, like a wealth tax, you know, the whole corporate tax rates, the whole gamut of tax tools that we have at our disposal. We certainly, in an area that I know well, need to look at labor regulations, right? There’s a lot of discussion now about what should happen in the gig economy, but, you know, what about, how do we, how do you distinguish if two people have lost their job, how do you distinguish, you know, between them?

You can’t say, okay, this person lost their job to AI, so we’re going to give them health care and, you know, other kinds of support, but… person we’re not right like you need to have universal systems of support for workers of health care of other forms of Social Security that that enable consumption smoothing as well so the economies keep functioning we need to invest heavily in our skill systems for all the talk I can talk about Indian numbers till I’m blue in the face of all the investment and talk about skills training in India only 4 .1 percent of respondents in our labor force survey acknowledge you know identify as having any kind of formal skills only 4 .1 percent despite you know us saying skill India and talking about investments and skills for the last you know well over a decade and a half skill systems.

There’s also well -documented research about how education, you know, the quality of education is so poor. So how do you take a young person in a remote part of India who can barely read and write, might say that I’ve graduated, I’ve done eighth grade or tenth grade, eighth class, tenth class, you know, even twelfth class, but can barely do foundational reading or math, right? How do you take them and say, I’m going to train you for AI. Yeah, that’s what I’m going to do. Like, it doesn’t work. It doesn’t work. So we need to actually fundamentally think about regulations. We need to very urgently work on our education and skill systems that meet people where they are.

We need to definitely think about universal social protection systems. That enable workers to transition between occupations from one sector to another, from one to another. to another from one occupation to another. And I can go into much more detail because this is something that my organization has worked a great deal on. What kind of systems do we need to enable workers to be better protected and be able

Thanks, Sabina. We’ve got, I think, five minutes or so, so I’m going to try and wrap up. Judy, would you want to comment?

Yeah, I just want to make a fairly random point, I think. And that is, in addition to the Artificial Intelligence for Development program that we have, we also have a Future of Work project. And I think one of the interesting things there that we don’t talk about as much, everybody is very worried about job loss. That’s kind of the big, it’s job loss. But actually, one of the bigger issues that’s happening is rethinking how to work and ways of working and the disruption that’s happening within jobs and within the workplace. And so I think that’s a really good point. institutions and organizations, that’s not necessarily about job losses. It’s about a complete shift in the way that we do our work and how workers are going to adapt to that fundamental shift in the way that they work.

So it was just a random thought.

I don’t think it’s a random thought at all. I think it’s a salient foundational thought, you know, for this discussion. You want to comment on that one line? Because that’s such an important point.

Yeah, no, I mean, just to say that, you know, the Future Works Collective is a global consortium of researchers that IDRC funds that JustJobs is part of that focuses exactly on that. So I agree 100 % that that is a foundational and very important issue.

Sandhya, what about you? How would you want to respond to everything Sabina has said?

Look, I think… Watching and waiting is certainly not an option. I mean, we don’t want to be in a Game of Thrones situation when you’re saying winter is coming for some 22 seasons and then it comes. Nobody’s going to wait for it. So we know what’s coming, and we know what’s coming is also capable of evolving and changing tremendously. So we need to learn to change. And yes, we do need to elect good leaders. We do need to have policy at all levels. We need to have policy embedded in platforms. And of course, we need to have a lot of reimagining work and training of workforce. So yes, I think to some extent, painting doom and gloom is good.

Then we start acting, right? But to some extent, I think it also shouldn’t make you paranoid that you become deer in headlights. So yes, we should act, and we should move forward on all of that that all of us agree on.

It seems so. It seems so, absolutely. No, but, you know, I think that’s, in some senses, a very good summary, what you just now said, right? What I wanted to say was that this phrase that’s used, boomer and doomer, boomer and doomer. So in a sense, my head is the boomer and my heart is the doomer, given my role. I want to take you just for a minute, which is my job is more to do with education. So we run three universities. We work with, at any point in time, we are working with more than 100 ,000 teachers, right? And so I’m an education person. I’m not the labor market or the tech person here, right? And I am deeply concerned by the effect of AI on education, deeply, deeply concerned.

In fact, I feel that AI is attacking the very foundation of education. The very foundation of education. What AI is doing is saying, the phrase artificial intelligence, it suggests what it does, which means you essentially outsource your thinking. So teachers are outsourcing their thinking and students are outsourcing their thinking. So essentially, and that’s what Sabina was referring to, but she was referring to in the context of social media, that for the first time in this round of sort of assessments, we are seeing cognitive declines, or on test measures we are seeing declines in student performance. I cannot tell you how serious the issue is. And it’s impossible to regulate this. It’s impossible to regulate this because it’s everywhere.

So the only way we are able to deal with this, in the universities at least, is that all assessment, examination, is now returning to the old world paper and pencil, in class test. No home assignments, no project planning, no test. No project work, nothing. Just come here and sit. and write the examination. It is truly serious. I mean, we don’t know how to tackle this right now. And the reason I talk about that is I want to go back to what the analogy that Sandhya used. And I’m so glad that she did that, which is that it is as serious as the nuclear technology. It is as serious as the nuclear technology. And in one very deep way, it is far more serious than the nuclear technology because nuclear technology did not reach out and affect every individual human being.

The possibility of policies and governance to be able to circumscribe, to put boundaries, to manage, those possibilities were far greater. And the possibilities here were highly disruptive, not highly, perhaps the most disruptive of technologies is in retail form, right? This is retail transformation of humanity. It is so hard. to do this. But I’m really glad. I’m glad that with the three of you here, we have this sort of reasonable conclusion, if I may say so, that we are really facing something as serious as the nuclear technology. And you can’t run away from it. It’s happening. You can’t run away from it. Job losses will happen. We’ve got to figure a way out of it. And I would want to close on this human note, that eventually, perhaps, those jobs that require wisdom, empathy, care, human understanding, they are going to be the hardest to replace if at all.

And they will stay. And that’s what one can see in the tech world. So with that, I want to thank all three of you. Thank you so much. I want to thank all of you for coming here. Thank you very much. Thank you.