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We make systems and making decisions about who receives public services, who qualifies for a loan, or who is flagged for surveillance. Those who design, train, and deploy these systems will influence not only over individual users, but also the information environment of democracy itself. At the first inter -parliamentary conference on responsible AI last November in Malaysia, members of parliament raised cases that brought this risk into sharp focus. In Amsterdam, an automated traffic management system inadvertently routed congestion, through low -income neighborhoods because the algorithm had learnt that those communities lacked the political influence to object. Examples like this will scale rapidly if governance does not keep pace, perpetuating harms against those historically excluded from decision -making. Yet, democratic governance is not keeping pace.

Power is accumulating rapidly in the hands of those at the forefront of AI development. A handful of technology corporations now command market capitalizations, exceeding the entire equity markets of major industrialized nations, while millions of workers in the global south are paid little to annotate the data sets on which the system stands. The benefits of AI are increasingly concentrated. while many of the costs fall on those with the least power to shape the technology. This is not merely an economic concern. It is a democratic concern. When the systems that govern aspects of people’s daily lives, their access to information services and economic opportunity are controlled by a small number of actors without meaningful public oversight, then the social contract itself is under strain.

That is why we must frame this not simply as technology policy, but as democratic governance. The choices made today about how AI is developed, deployed and regulated involve trade -offs. Between innovation and safety, efficiency and equity, profit and the public interest. In any healthy democracy, those trade -offs are debated openly, decided transparently and subject to accountability. The parliamentary community declared in Malaysia that we do not accept the concentration of power in the hands of a few actors. They called on all stakeholders to agree upon red lines that this technology cannot cross. They insisted on an equal voice for the global south. And they called on all parliaments to engage actively with AI governance efforts at every level.

The principle that elected legislatures shape the rules governing society is the cornerstone of democracy. But the contribution of parliaments to AI governance goes beyond that basic principle. Parliaments are where the real world impact of AI meets political accountability. Members of parliament hear directly from workers affected by automation, from communities concerned with algorithmic decision making, from parents navigating their children’s relationship with technology. This connects governance to lived experience and informs the AI debate through the values of the people. Parliaments can and must support this. I want to stimulate that broader societal conversation through hearing the voices of the people. consultations, and multi -stakeholder dialogues. I believe you heard what the Deputy Speaker of Hungary said about the practices in his country, which I believe is the path down which we would want to travel.

This brings me to the international dimension. AI is a truly global challenge whose effects transcend national borders. As we would say, AI doesn’t have a national passport. While the risks are real, from job displacement to environmental costs, so too are the opportunities. AI has genuine potential to improve healthcare, expand access to education, and accelerate progress on the sustainable development goals. But those benefits will not be shared equitably by default. That requires deliberate collective action and it requires that the countries with the most to gain are not shut out of the conversation. Yet international AI governance remains fragmented and short on binding commitments. Geopolitical competition risks fracturing governance efforts further. That is why this summit, I say this summit and those which will follow, must embody the inclusive participatory approach that the equitable governance of AI demands.

Parliaments are pivotal to ensuring coherence between domestic legislation, established human rights, and evolving international standards. and to holding their governments accountable for the commitments made at summits like this one. The Inter -Parliamentary Union is committed to supporting that engagement. In the past two years, over 60 parliaments have taken action on AI, from comprehensive legislation to oversight inquiries. Across the world, parliaments are forming cross -party groups, establishing specialized committees, and building capacity. The foundations are being laid, but they need to be built on faster, with increased coordination across borders. Parliaments are also beginning to explore how AI can support their own work. And those that experience its promise… and limitations firsthand will bring far greater understanding to the task of governing it.

So, let me return to the principle at the heart of what I have said today. Democracy cannot be automated. It must be shaped by every one of us through our democratic institutions, through open debate, through laws made transparently and enforced fairly, and through international cooperation in which every, every nation can participate. The choices we make will determine whether AI furthers democracy or erodes it. If we succeed, AI can become independent. AI is a tool for inclusion, participation, human rights, and better governance. If we fail, it risks becoming a force which concentrates power, weakens accountability, and erodes trust in public institutions, including parliaments. The task before us is to embed democratic accountability, human rights, and the rule of law at the heart of how AI is designed, deployed, and governed.

This summit is a critical opportunity to advance that mission. Let us make the most of it together. Thank you very much.

Thank you. Thank you, Mr. Tiongong. And now, in this momentous occasion, it’s our great honor and pleasure, as today we have with us as chief guests, Honourable Mr. Om Birlaji, Speaker of Parliament of India When democracy meets AI, what are the opportunities for that, for deliberation Please put your hands together and we invite Honourable Om Birlaji

Thank you IPU’s Secretary General, IPU is an organisation of more than 190 countries in the world where in the institutions all over the world, how we can make new innovations, technology and international institutions responsible for the people. For this, all the parliaments of the world discuss this at regular intervals. I would like to welcome the Secretary -General of the IPU, Martin Csuk -Ok. I would like to welcome him. I would also like to welcome the Deputy Chairman of the Parliament of Hungary, Legos Alaw, as well. I would also like to welcome Miss Jimena Soto, Miss Maria Ramos, Miss Maria Ramos, Miss Maria Ramos, Miss Maria Ramos, Miss Maria Ramos, Miss Maria Ramos, Miss Maria Ramos, Miss Maria Ramos, Miss Maria Ramos, and especially those in whose presence this work is being carried out, the culture of India, the political value of India, the spiritual value of India, how can we bring the knowledge of the spiritual culture of India to the world?

For this, for a long time, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, the Vishgayati family, Thank you very much. Thank you very much. Thank you very much. is working to reach this spiritual and moral values. And along with this, here, the Dev Sanskriti Vidhyalaya, which is amazing, where in Dev Sanskriti Vidhyalaya, the moral and spiritual values are taught, but at the same time, in modernity, technology, whatever the new education system of the world is, that education system is also given by the Indian moral and spiritual values by the education system of the Indian for the creation of a society, for the creation of a for the creation of a for the creation of a for the creation of a for the creation of a for the creation of a for the creation of a society, for the creation of a society, for the creation of a society, for the creation of a society, for the creation of a for the creation of a for the creation of a society, society, In the school where you will go, you will see that there is Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, there is also Vedic value, and in the future, in the international organizations, in the international community, we will continue to develop the traditions of Sabwadur and use technology in a way that will answer the people of these international organizations.

We will continue to develop the traditions of Sabwadur and use technology in a way that will answer the people of these international organizations. to contribute to the development of the country. And I am happy that institutions all over the world are working on their own level. The Congress of the Commonwealth of Nations is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting.

The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. The Congress is here to attend this meeting. that how we can use AI in international organizations, use a answer -based technology, use a answer -based technology so that we can use all international organizations in the country, their work culture, their conversation, their discussion, to make it better.

And for this, the Indian parliament is also working on a large scale. With the Indian parliament, our state’s constitution, that too is working on technology. And within India, the constitution, the constitution, all the constitution, the constitution, today, the paper says, This is for all of us, because India is the world’s largest democratic country. Demography is also amazing for us. Our language is different. Our culture is also different. Even after having such a diverse country, we have tried to use AI to answer questions, answer questions, and be special. And that’s why, in this direction, India’s security is very important. The Digital Assembly has implemented the rules of all states and the implementation of our Lok Sabha Rajya Sabha.

You can see that on one platform. And by 2026, all the proceedings, debates, discussions of the Constitutional Assembly will be on one platform. And that is why we have started working on a large scale. Today, most of our Constitutional Assembly, not all of them, have been paperless. All the debates, discussions, discussions, budget, issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, all the issues of the state, of the central government, With this, we will give such a model to the country that all the councils in the world, from the Constitutional Assembly of the State of India to the council’s functions, can be seen on one platform and there will be new innovation in it.

With that innovation, we have also tried to use AI in it. Because when you go to the subject, the topic, the discussion on metadata, then how can you search in all those debates? So, using AI technology, the Constitutional Assembly of the State of India and the Council of the People will get a platform and you will be able to see and read all the subjects and issues of the state through metadata. This will increase the capacity of our people in our democratic institutions. debate will be of high importance and while making the law we will make the law by summarizing the thoughts of the people and while making the law the discussion will be good in the parliament for this India technically I can say that in the form of AI India will become a new model of technical knowledge for the world’s parliament I am happy that under the leadership of the Prime Minister today the world’s largest AI community will be able to do this and I am happy Jisme 100 se jada desho ke log aayen, prathiniti aayen, rashtra dyaksh aayen, sansat ke sadarshi aayen.

Aur yeh sab yaha par kis tarike se badalti duniya ke andar, hum AI ka upyog karte hue, kis tarike se logon ke shamta nirman, industries ho, agriculture sector ho, chayani sector ho, unki utpadakta ko badana, aur duniya ke andar sabse nojawan desh Bharat hai. Aaj Bharat ka nojawan takni ki roop se nahi navachar kar raha hai, aur duniya ke andar sabse nojawan desh Bharat hai. Aur isi liye Bharat ki yeh yuwa jan sankhya hi Bharat ki satsi badi tagat hai. Aur isi liye isi tagat ka upyog… In the right direction Be positive Be in the form of new culture So that the challenges of the world Can be solved by India And in this direction We are moving forward I hope That our talent Is abundant in the world Our young people’s Power Concentration New culture Self -confidence Because it has spiritual And political value And Dev Sanskriti Vidyalay Where technology In technical knowledge Is giving youth Vedic education Along with that They are getting modern technology Education But that education should be on political value For everyone’s development It should be trusted It should be trustworthy It should be trustworthy It should be Because It is the only thing Because while using technology, if we do not use all the technology, then its direction can also be wrong.

And that is why a student who studies in the political fields of spiritual, religion and culture can use AI technology with answer and response. And in this direction, India is definitely working because India has power, we are growing rapidly in the world of clean energy. We have young people with political values. And their thinking is amazing. And their belief and self -confidence is also amazing. And that is why our speed and scale is growing rapidly. This world is looking at India. You must have seen that the attention of all state leaders is also on India. And they have also said that definitely India is doing a good job in the technology, in the AI sector.

India is doing a good job in the technology, in the AI sector. And the speed at which it is working, the scale at which it is working, will definitely move forward. Our thinking and thinking is always about the creation, the realization and the happiness. We consider the world as a family. Vasudev Kutamkam is our culture. And our thinking is about the creation, the realization and the happiness. That is why I hope that the AI technology conference will definitely give a new direction. And we will use it with confidence and with responsibility. We will be able to do it with confidence. And the use of technology is used in machines. But our human resources will work in the right direction.

I again give a lot of appreciation to all the people who have come here. And with this discussion and discussion, we will get a new direction. And we will continue to develop in India based on political values. And with the help of international development and international

Thank you. After the wonderful speech of Honourable Speaker, we are privileged to have Dr. Fadi Dao here. He is the Chairman of the Globe Ethics. And there is one single question that I wanted to ask you, Dr. Dao, that you just listened to the excellent deliberation by Honourable Speaker and the variety of voices here. And India is a country with 27 official languages, 19 ,500 dialects. We have got more than 400 documented cultures. And we go with the belief and value of Vasudhaiva Kutumbakam. So how do you see the way forward from here? If I can hear from you in one minute, please.

the largest nation in the world, for reminding us that through this summit and the purpose of AI democratization is not people’s manipulation or domination. India is reminding us also today that the purpose of AI is the social empowerment and participation of all people. To conclude, ladies and gentlemen, I would like to say on behalf of Globe Ethics, my organization that is based in Geneva, that we are committed to capitalize on the outcomes of this summit and this panel in the perspective of the 2027 summit in Geneva, where we would like to welcome you all. Thank you.

Thank you, Dr. Dow. And very shortly, Lord Rawal is with us from House of Lords, also a devout member of the Gayatri Parivar. If you could kindly shed a light on the way that India should take now for democracy.

Thank you, Paiya. Ladies and gentlemen, one of the tenets of Gayatri Parivar that I grew up in is the adaptability to change. Change is such an intrinsic part of the entire fraternity. And that is, I think, a real advantage, because what will happen, the big cost of AI, is the speed with which technology is advancing, which can really make people unsettled. And the uncertainty, as a politician, I need to contain people’s uncertainty. And I think this preparedness for change, Chimabaya, which is a cardinal value of your organization, will really help people. There’s other things I could say, but I’ll leave it at that, because we’re pressed for time. Thank you.

Thank you. Now it’s time for felicitations. On behalf of India AI Mission, Government of India, and all world Gayatri Parivaar, Dev Sanskriti Vishwadhyayalaya please put your hands together for wonderful session and we express our gratitude towards our honorable chief guest honorable guest of honors and Dev Sanskriti Vishwadhyayalaya, all world Gayatri Parivaar in itself started a very wonderful program like when we are integrating artificial intelligence with spirituality we are talking about future of faith in interfaith dialogues worldwide Dr. Chidambi Pandya is representing the thought and today on this very wonderful gathering we once again thank our honorable guest of honors, honorable distinguished speakers and all the participants thank you, thank you once again do visit Shantikunj Haridwar, Dev Sanskriti Vishwadhyayalaya and you can scan the QR code on the screen so that you can get a very wonderful gift afterwards once you scan and you put your please put your hands together once again we express our gratitude to our honorable speaker of Lok Sabha, Adar Nishri Om Birla Ji and our honorable guests once again a big round of applause thank you all thank you the next stage is beginning all of you please be there for the co -operation thank you thank you QR code which you can see in front of you, scan it so that you can get a special gift for this program.

Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. you you Thank you. Thank you. Thank you. with

each of you in the room to really tell you what we’re doing over at OpenAI. And today we’ve got an incredible lineup of show. You will hear from our chief economist. You will hear from our chief of global affairs. You’ll see a lot of the work we’re doing in education, in social impact. And we are tremendously excited to have you here. The energy this week has been palpable. And we cannot wait to continue to build in India, with India, with each of you. So with that, I’m going to invite our incredible chief economist, Ronnie Chatterjee, on the stage to kick off the show. Well,

As we think about the panel discussion, I want to start and invite Iqbal Dhaliwal. Iqbal, where are you? Come on. Iqbal is the Global Executive Director of J -PAL. Okay. And Rupa, do you want to come up here too? Rupa, thank you. Rupa is the Chief Economist and Head of Policy Advocacy at Tata Sons. Very exciting to have you here, Rupa. And Sanjeev Bikachand, the founder of InfoEdge. Sanjeev, please take a seat. All right, Sanjeev. So let’s get started and think about – and the great thing about this, I get to ask the questions. Yes. You know? When I do the other things, I’m always in the hot seat, and I get to ask you the questions about the future of work, jobs, AI, the economy.

Okay. Iqbal, let’s start with you. Thank you. How are you using data, perhaps like signals and other kinds of data sets, to understand how AI is affecting the economy? What are the most interesting things you’re seeing? Hey, can you? Yeah, perfect.

Yeah, thanks for having us. Super exciting. I think for us, data means a lot of things. One is absolutely to understand what the problems on the ground are. But for us, the most important use of data is to understand how the applications of AI are making an impact on the ground, which is so important also because that is the topic of today’s conversation. And it’s giving us so many incredible insights about how things are working. For instance, just in the field of AI and the intersection with development, wearing my hat now as a researcher but earlier as a policymaker, one of the things that we have been worried a lot about is early childhood. And we think about early childhood education and say, hey, how can we get more kids to come?

into early childhood education, how can we get to ages one to three? And it was an impossible task despite rigorous evidence that this works. And now what we are able to do with AI, you know, Rocket Learning, OpenAI has a partnership with them, I’ve been on their founding board, Rocket Learning is able to now democratize with data the application of early childhood education for children. It’s able to do that by reaching millions of Anganwadi workers who could not be trained earlier, and it is generating so much data which is telling us how children learn, what exactly customized messages to send to these children, and what to send to their parents. And so for me, that’s kind of a really cool application of the data which is being collected and being generated by using an AI application.

Oh, I want to return to education in a second, but first let me go to my other chief economist, Rupa Purshatanam. Rupa, first of all, what does Tata want with a chief economist? I understand now why OpenAI is one. I understand now why OpenAI is one. Tell us what the chief economist at Tata does, and also what are some of the most interesting things you’re seeing in the enterprise, one that’s using AI a lot, from what I can tell.

So that is a very good question. I think when you’re part of a group like the Tata’s that’s sort of spanning all sectors of the economy, you do a little bit of everything. I would say probably 30 % of the job is looking at how do we do classical economic forecasting, GDP, interest rates, inflation for the geographies we’re in. But I think we get to do a lot of interesting things, really trying to connect macro approaches that we take in macro to things that we’re seeing in sectors or solving big impact nation -scale problems. So I get to do a lot of work on sustainability, on health. I’m the head of social impact as well. So those things sort of cross over.

This is interesting. I mean, right, it’s interesting. As much as we’re asking about economics, we’re hearing about education. We’re hearing about social impact. I want to return to these themes as well. Sanjeev, how about you at InfoEdge? How are you? Thinking about the most important uses of AI. What are you tracking?

Okay, so the first thing is, we are a job site primarily, Nocri .com. So roughly about 70 % of our revenue and 140 % of our profits come from Nocri. So when we hear this talk that AI will eat jobs, and AI will eat programming jobs, and 50 % of our revenue comes from the IT services sector, we get worried. So the first thing you want to do is actually understand what is AI going to do to jobs, specifically what is AI going to do to jobs in the IT sector. Now that’s important for us to understand. But let me tell you, thus far at least, there is no evidence on the ground that hiring is going down. In fact, it is steady.

So thus far at least, there has been no impact on jobs or no impact on Nocri business. So now we are waiting and watching because, you know, we are worried when the whole world is saying jobs are going to vanish, we get worried. But, you know, I console myself by going back into history. And, you know, when a new technology comes, there is a disruption often. And, yes, some people, some jobs may get replaced, but many more are often created. So in 1985, I recall I was 22 years old and my first job. And the government then announced that we’re going to introduce computers in banks in India. In those days, most banks were public sector banks.

The bank trade union went ballistic. But the government introduced the computers anyway. And for a while, they didn’t get used, the computers. But when they began to get used, nobody lost jobs. People got more productive, right? They were servicing their customers better. They were doing different things. They were doing more things. They were doing things faster. So technology. Technology may disrupt and may replace, but it will also create new jobs and new opportunities. opportunities. Now, if it happens in six months’ time that you’re disrupting, there may be a problem. But if it takes five years, you’ll have enough time to create new jobs, do new stuff, and on an aggregate basis, there won’t be crazy disruption.

Rupa, do you want to follow up on this?

Yeah, I was going to say, I think that there’s two areas for India in particular where we could see new jobs, meaningful jobs being created. One has to do with the fact that we are in a very different situation compared to countries like the U .S., regions like Europe. In places like that, you have efficient markets and you have a plethora of specialists, whether we’re talking about doctors, lawyers, whatever it might be. In India, we don’t have enough doctors per thousand people. We don’t have enough nurses. That’s an even worse, more acute problem. Educators and so on. And the resources that we have that are… Specialists are stretched to the max. So we did some work some years ago and looked at doctors at Ames, not far from here.

and we found that 50 % of their time was spent on not specialist work. With AI and the tools that we have, that work can move to a whole new set of workers that can take that on, and that leads to two things. One, doctors can do what they should be doing for more people. You have a new class of workers that mediates technology but also helps new patients navigate the system that is very difficult to navigate. And when you have, let’s say, people in rural India who are experiencing health care for the first time, now all of a sudden you need medicines to reach people and so on, right? So entire supply chains start. And so I’ve talked about health, but this is the same for education, for financial services, logistics.

And so I think there’s tens of millions of jobs. We estimated it to be about 30 million that come from these sort of rigid workers. The second one is entrepreneurship, and I think there’s something mind -boggling about the fact that literacy, and I advocate for literacy, but literacy is not the obstacle that we saw in the past. because of two things. When you have voice activated and you have local language models, all of a sudden, and we’ve seen this in our social impact work, you have entrepreneurs that can now understand, you know, price information more quickly. They can access markets, access financial resources. So things that you could only do in urban systems now can be elsewhere.

And I think for us, if we see this, and right now it’s still very nascent, but if what we’re seeing in our social impact work really goes bigger, entrepreneurship, which are small and medium -sized businesses, which now account for like 10 % of private sector employment, it can go to what we see in other places, which is closer to 40%. So for me, those are two big opportunities.

Iqbal, how about you?

Yeah, thanks. Okay. I think I agree with everybody, you know, like about the potential of AI to try and transform our lives for the positive. I do want to, you know, put in a word of caution on the labor front. I think I agree with you, for instance, that, you know, like when computers came, people thought… it was disruptive. But think about how expensive the first computers were, right? Like there was this one massive computer in the office, five people would go and share it. You know, like the bosses got a laptop, everybody else got a hard, you know, like a thing, a desktop, thank you. Most of them were not connected to the internet.

Then we slowly connected them to the internet. I think the speed and the pace of AI is unprecedented. It’s a general purpose technology. The price is, the price point for the marginal user is very low. The penetration is incredible. Think about it, right? Like every single one of you who has a smartphone in your pocket has AI in your pocket. That was not the case for computers and technology. And finally, the multimodality of it. The fact that, oh, I can’t process text, but then I can process it as a voice or I can process it as video. That is phenomenally different from all of these technologies. So I think what I would say is, you know, the following.

I think I agree with you that in the medium and the long term, job markets will adjust. The pace at which – I just wish I had a dial which could kind of slow things down. So, you know, and I think the speed at which this thing is going, the labor markets will have a very hard time. The second reason that the labor markets are going to have a really hard time is because we are completely biased towards capital investment versus labor. This is true for the United States. We have Social Security taxes, Medicare, Medicare, like the entire thing. I mean, you know, in India, we have ESI, cratchity, public provident, retirement. And on the other hand, the government gives breaks on investment in capital.

So the playing field is not leveled here for each one of us in the labor market to compete against AI. So I’m all for AI. It’s going to be a product -enhancing technology. It’s going to be an augmenting thing. But for it not to turn into an automation and a human replacement thing, we need to dial down the speed, and we need to make sure. We need to make sure that the policy infrastructure keeps up with it.

Look. AI is now relentless, the genie’s out of the bottle, you can’t dial it down, it’s not going to slow down just because somebody said so. It’s going to happen. Now, you can either do it or have it done to you. And what I tell people, individuals, I say, listen, you worry about your job, don’t worry about jobs in the system, national level jobs. Is your job safe and what can you do to make sure your job is safe? Or get your job, if you’re a student. And I go back to 1989, I had just finished business school, I had worked for three years prior to business school, I had finished business school, I joined a company, a consumer company, and I was working in the marketing department.

And yes, as Iqbal says, there were two computers, 15 people, we were sharing it. Now, the thing was that I was the only guy who was PC literate because I was the most recent graduate, I was the youngest. I had used computers in business school, the others had not. They were senior to me, they were my bosses, they were getting paid more than me. And they had more powers than me. but they couldn’t use a PC. I could. If there’s somebody getting sacked in that department, I was the last guy getting sacked because I was the only guy who could use a PC at least for the first few months. My point is simple. AI platforms are easy to use, easy to learn.

For everybody, I’m saying if you are a person in your company or in your department or even if you’re a student who knows how to use seven or eight or ten AI platforms, believe me, you’re highly employable. Because not everybody will learn it. If you learn it and are good at it, you’ll be okay. So it’s in your hands to protect your employment and your employability. Just learn AI.

This is one of the things people say that your job is more likely to be taken by someone who knows AI better than you than by AI. Ruba, inside an organization, how do you help power users who are using it a lot, kind of the example he gave of being the PC user where no one else was, how do you help those people diffuse their best practices, their learning to the other folks? And we see this in our data. There’s a big spread between the power users and the median users in most organizations. We call it capability overhang. Do you see that at Tata, and how do you think about solving these kind of issues to help more people learn how to use AI?

I mean, I think for us, even working across the group, the different companies, is something where a lot more collaboration, what we’re working on are platforms for us to be speaking to each other about best practices, what works and what doesn’t. So right now when we learn, for example, that in a lot of our manufacturing businesses on the shop floors using AI for safety, how could we use those best practices in other parts of our companies? So even just within us and across companies, can we share what’s working and what’s not? So it’s not. I think we have companies where, let’s say like a TCS that sees a broad section of sectors and what’s worked.

So you kind of learn that sectors like life sciences, you see these huge changes in drug discovery. But across all sectors, you see things like customer service, marketing, those things really being changed by AI. So I think right now at this stage, having those conversations about what seems to be working on the ground seems to be the most important. As we are going through the very difficult process of taking what are legacy systems, sort of lumbering systems, and trying to get the data that is in very different silos to even start talking to each other. So I think we acknowledge that that process is still going to take a lot of time, but we can see these sort of new cases where it’s actually taking time.

I just want to get Iqbal in here for a second, and then I’ll get back. Iqbal, what parallels are there to the development literature where we’ve found ways in education to teach people how to do new things, teach them how to start businesses, teach them sets of skills? Iqbal, what parallels are there to the development literature where we’ve found ways in education to teach people how to do new things, Iqbal, what parallels are there to the development literature where we’ve found ways in education to teach people how to do new things, and what parallels are there to the development literature where we’ve found ways in education to teach people how to do new things, can we teach people AI?

Can global institutions teach people AI? Can J -PAL do work in that area? Because it seems like an analog to sort of working with people inside enterprises, but maybe a different challenge.

Yeah, great question. I think we can. The question is, can we do it correctly, and who benefits from that teaching? So let me give you an example. You know, we are well aware of this literature in the business processing outsourcing, where, you know, once you provided AI tools to people, like the lowest performing BPOs, call center employees, they leveled up. And they leveled up to the higher skilled workers. Fantastic example of leveling of skills, and which is a win -win. We did a study in Kenya where we provided AI, chat GPT actually, tools to micro -entrepreneurs. Don’t think of these as sophisticated users. Think of these as grocery stores, a neighborhood stationary shop. And then what we see is that the average treatment impacts, this is early stages of chat GPT, the average treatment effects were zero.

So then you dig deeper into the data, and then you actually see something super interesting. The top entrepreneurs, the ones who are performing well, actually take chat GPT, and they do really well. Because they run with it, they understand how to do it, and they know what to do with it, and then, you know, the recommendations that come out of it. On the other hand, those who were lower performing earlier, you know, like chat GPT will give recommendations. They will say, oh, nobody’s coming. The demand for my product is low. It goes to Econ 101 and says, maybe you want to think about lowering your prices. Maybe you want to think about increasing your marketing budget.

And then, but how much should I lower the price? Should I lower it today? Should I just lower it for Diwali or whatever? You don’t know. So I think you raise a really important question. I think there are going to be some folks who are going to take these tools, run with it, and there are some of the folks who are going to need a little bit of hand -holding. And I think you’re absolutely right. We can do a much better job of helping them human integrate. This can be through originally the old models of teaching. But I actually think now that the tool is so powerful. It can teach people itself with these nuances.

And we’re releasing products to do that. We have a new jobs and certification platform coming that’s connected to that. Sanjeev, thanks for being patient.

I want to give a couple of examples of real things in our office. So we also invest in startups. So we’ve invested in about 130, 140 startups now. Now, every month, every quarter, the MIS is coming from these startups. Now, we’ve got very smart people in the investing team, in the portfolio management team. They’re all MBAs and CAs and things. So you know their stuff, right? But you just put in chat GBT, and you first of all do it yourself and chat GBT supplements. And then you say, okay, have I missed something? Is there a perspective here? Here, it’s helping them do their jobs better. It’s doing stuff that they might have missed, number one. So therefore, it’s enhancing the productivity.

And you can do many more analyses because you can ask 100 questions to chat GBT, and you may only ask yourself 10 questions, right? The second example I want to give is, so our marketing team in Jeevan Sati, a matrimony site, decided around Father’s Day. Let’s produce a film. father -daughter relationship. What my father means to me, and so on. Now, under normal circumstances, this film would not have been made. It would have taken six weeks and 60 lakhs to make. So it would not have been done. And life would have gone on. But it took about two days, using AI, for novices who never used that platform earlier, and now they’re saying they can do it in three hours, to actually make a film purely on AI and put it out on digital media, and it worked.

It was a big hit. Now this is stuff that’s not been done. Another example, there’s a surfeit of content. There are so many podcasts, there are so many interviews, this panel will also go to YouTube. So, you know, I can’t follow all of them. I’d love to, but I can’t. I just get a summary of a video from AI. I can do it in about three minutes. So I’m doing stuff I would not have done otherwise, and I would not have employed somebody. Nobody’s lost their jobs. for example, Nocatee has got about 130 ,000 to 150 ,000 clients. The top 20 % there’s a sales team that calls on them. The next 30 % there’s a tele -sales team that calls on them.

The bottom 50 % you don’t interface with because they don’t pay you enough. The sales channel is not worth it. Now we’ve got voice bots calling the bottom 50%. So we are serving an underserved market. Nobody’s lost a job yet. Now I’m not saying it won’t happen. Maybe it will. I don’t know. But thus far it’s not happened. And life is going on. And every quarter Nocatee is still growing. We are worried. We are concerned. We are apprehensive given the noise in the system about job losses. But it’s not happened yet. And we’re taking it quarter on quarter. And we are keeping our fingers crossed and hoping and praying it doesn’t happen. I don’t have the answers.

No, no. Look, none of us have the answers, I think. And I’ll close the panel on this. I did promise the team I would end this on time. I could talk to these guys for about three hours just to let you know. So none of us have the answers, right? At the end of the day, from our vantage points, we’re trying to solve these questions, right? Sanjeeva, you did a great job from a person who’s an investor who’s building things, trying to explain what you think is happening. And the idea of you making the movie, I think, is a good example of how you can do things you never did before with AI. Rupa, you’re in a large conglomerate with lots of different businesses, lots of different exciting things going on.

How AI diffuses across the organization, I think that’s something all of us should watch. It’s not easy for large organizations to adopt AI and implement it, and the ones that do it, I think, are going to be advantaged. And Iqbal, I think you leave us all something to think about, which is if we’re going to educate the world on AI, if we’re going to democratize AI, we’ve got to make sure we do it well. We can’t just talk about it. And I hope in all these cases, enterprise adoption, learning and teaching AI, and helping the cutting edge, that OpenAI can be your partner. So with that, I want to thank our amazing panelists and thank everyone in the audience.

I’ve got one last thing. We’re done. I’m so sorry. You’ve got to hear this. They’re giving me this. Does this mean that? Yeah, you’ve got to hear this. I’ve got colleagues coming and negotiating salary with me. I’ve checked on GPD. I’m paid 40 % less than I should be. And he’s saving money on salaries too, getting some more time. I love it. Thank you. Thank you. Thank you. Thank you.

Thank you so much, Rani. Thanks, Sanjeev. Thanks, Rupa. And thanks, Iqbal. We do have more sections coming up. I’d request everyone to stay back before we complete this session. I’m Kavita Gunjikannan from the Global Affairs team at OpenAI. We want to take a moment to celebrate a few education partnerships that we announced just yesterday.

and then we’re going to dive right into my immediate left. I have Alex Reed -Gibbons, who is the CEO of the Center for Democracy and Technology, one of the leading advocacy organizations in the world, working on civil rights, civil liberties all around the world. She’s based in D .C. To her immediate left is Amanda Craig. Amanda is the General Manager for Responsible AI Policy at Microsoft. To Amanda’s left, we have John Edwards. John Edwards is known to many. He is the Information Commissioner of the United Kingdom. And to John’s left, we have Denise Wong, who is the Deputy Commissioner of the PDPC in Singapore, the Privacy and Data Protection Commission. Welcome to our panelists. So we have two regulators, an industry representative and a civil society representative.

And I come from the IAPP. If you don’t know the IAPP, we are a global professional association, a not -for -profit but also policy, and we’re neutral. We’re not just a company. an advocacy or a lobbying body, we bring together the people who do the work. Many of them are in the room right now who do the very hard work of data protection and AI governance all around the world. All right, let’s jump in. The title of the session reflects trust as an engine for growth. Let’s think about that just for a minute. Just a few short years ago, I think it was two and a half, maybe three years ago, this event started in Bletchley Park in England.

And in that iteration of the event, it was named the AI Safety Summit. Right around that time, the EUAI Act was being negotiated. It soon passed after that. But a lot has changed in that two or three years. This event is the AI Impact Summit. The event last year in Paris was the AI Action Summit. More recently, we have seen, the not yet fully implemented EUA. AI Act become subject to an omnibus package where some of the expectations of that original act are being dialed back a little bit. And we’ve seen broad critique of regulatory structures, trust and safety structures that might inhibit growth and innovation in AI. There clearly is a deregulatory mood in the air.

In fact, I think it’s notable that there has not been much discussion of law or regulatory initiatives that might create guardrails to help guide the adoption of AI. So clearly, we’re in an odd moment, and an odd moment for this panel. But as I walked around the campus of this event, this enormous campus, I noted something that was, I think, quite significant. Just about every second banner or poster, just about every large printout, printed word in the show floor, somewhere had trust. safety or privacy as part of the messaging. In fact, the sutras, and we’ll talk about them as we go through the session, the principles announced by the Indian government largely around trust and safety.

And so what gives? What’s the dichotomy here? At one moment we are saying it’s a deregulatory mode, we step back. Well, at the same time, we are actively embracing and discussing trust and safety, risk management, protecting consumers, citizens, human beings as they engage with AI. So do we care or not? Are we actually in a deregulatory moment, or have we just gotten quiet about the need for guardrails and trust and safety in these systems? I would say for business, risk exists regardless of whether there’s a law in place or not, and so businesses have an imperative to respond. I’m going to tell a very, very quick story, and that is that in 1891, when electricity was first being brought into the White House in the United States, then President Benjamin Harrison and his wife, Caroline, were actually terrified of flipping the light switch.

And so they hired the electrician from the Edison Company, a man named Ike Hoover, who went on to become the chief usher of the White House. They hired him to flip the light switch. I think the message of this story is that we won’t use it if we don’t trust it. And so as AI is being pulled through the walls of our world, as it’s creating light and switches and tools for us to use, I think we need to ensure that we’re comfortable flipping those switches. And that is the topic of our panel today. So let’s jump in. And our first question is going to be about just the moment that we find ourselves in.

And I’m going to start with Alex. why are trust and safety important to innovation? And maybe speak to this dichotomy that I’ve highlighted. Why is it in this moment that we can’t talk about regulation, but everywhere it seems we’re talking about trust and safety?

Yeah, first of all, thank you for convening us, and it’s a pleasure to be here. I think you really hit the nail on the head in your introduction, which is when we think about the long -term success and sustainability of AI, and that is business sustainability for the companies, as well as societal sustainability for all of us. The secret is not just an acceleration, the biggest, fastest, most capable model. The real story is one of adoption, and that has been the overwhelming theme of the summit this year. And for people to adopt this technology, they need to trust it. And that’s trust in multiple different facets, right? Is the tool fit for purpose? Does it work in your language?

Is it appropriate for your culture? Will it protect your privacy? Is your data going to be secured? What is the quality of the information that is grounding that model and those outputs? And I think people are really waking up to this, and they’re demanding more. This is both as individual users and then, of course, for enterprise customers, too, who themselves are saying, we’re on the front lines thinking about how to integrate AI into our business operations. We’re the ones who will likely be sued if this goes wrong. So this is where trust really is the fuel of innovation because it is what’s going to be the economic driver of these tools being adopted. And the other thing that I would add is what we see is not only that trust is important for innovation in the abstract, but this is also where responsible, thoughtful regulation can be fuel for innovation as well.

Because the same way that we want to be able to drive cars without all of us being experts in how a motor works, Product liability and good laws around the creation of these tools help make sure they outsource some of that work for us so that we don’t all have to be doing the individual labor of deciding whether we can trust. So many times people will create this false framing of regulation versus innovation, as opposed to thoughtful being regulation being the fuel that actually allows us to sell, buy, and use these tools.

Excellent. Fascinating. John, I’m going to jump to you, and Amanda, I will come right back. But I’m going to jump to you. The U .K. doesn’t have an AI law in place. It has lots of laws that will apply to AI. I think data protection and the GDPR Act in the U .K. is a great example of that. But talk to us a little bit about regulating in the absence of an AI law. What does that look like in the U .K.? And do you see organizations exhibiting behavior that demonstrates that they’re focused on the ideas that Alex suggested, that trust and safety matter regardless of the relationship? What is the regulatory structure that sits over them?

Yeah, absolutely. Absolutely. Absolutely.

There it is.

No, very much so. I mean, the data protection laws apply across the board wherever technology touches personal data. So we have a de facto regulatory regime under the UK GDPR. Coming back to your comment about trust, it’s so important, and there is a role for regulation actually in assisting businesses because businesses are trying to deliver that trust proposition to consumers. But by what metric? Right. And that’s, I think, where regulation can provide a common standard. So, you know, we require, it’s a regulatory tool, that you have to do data protection by design. You have to do data protection impact assessment. You know, we expect. Privacy by design. We expect. respect risk assessment. So all of these things are regulatory requirements, but they are also tools that help intermediate between businesses and the consumers to demonstrate that there is a basis for trust.

And an organization like the ICO is there for both sides to see, well, there’s someone actually overseeing that. And that’s a role that we do discharge. Judge, to your point about the absence of prescriptive regulation in the UK on AI, we don’t see that particularly as a deficit. I mean, I think there’s a lot of policy work going on in areas where policymakers and regulators do need to step in. That’s ongoing, and I won’t comment on that. But, you know, there are ongoing issues about the distribution of proceeds from the use of creative materials and the like. That carries on. But… In the absence of an explicit rule, it’s incumbent on my office to deliver safety and confidence and metrics for industry and to deliver certainty over what can be seen as an uncertain law.

So we’ve gone out and said, well, here’s how we see the technology -neutral general principles of the GDPR apply when you train a model, for example. We see, for example, the EU AI Act in Article 10 talks about the need for fairness. Well, we’ve been able to articulate those obligations by way of guidance, linking it back already to the GDPR principles. So, you know, there’s a mapping. I don’t think at the moment for the available applications of artificial intelligence technologies that there is a lacuna. It’s there. With the GDPR. And we are there to provide. confidence and certainty about how you apply that, how you improve your products with it, and how by doing so you engender that trust that you described at the outset.

Excellent. Okay, so Amanda, tell us, do you agree that there’s not a need for additional rails, traffic indicators in AI? Is John right that the existing regulatory structure is really providing enough guidance or is it the case that Microsoft is using internal principles, frameworks, standards that you might adopt to build programs and services that you think meet the expectations of trust and safety of the marketplace?

Thank you. From a Microsoft perspective, we are focused on implementing our responsible AI governance program and see opportunity for lots of different governance models that governments could pursue in terms of implementing existing regulation, developing additional regulation that complements that existing regulation. I think the through line for us, the bottom line, is very much what Alex started us off with, that we do very much see, we’ve seen through multiple generations of technology, we’re not going to have adoption, we’re not going to have use of this technology without trust. And we need to have governance programs at technology companies. We need to have governance efforts by governments that are ensuring that we have an evolving conversation about trust.

Because if I pull the thread on the analogy you started us with, like how do you flip on a light switch and that can be scary when you’ve not done it before, I think the other thing that is very challenging, true about this technology, is that it is also very dynamic. It is evolving very quickly. quickly. And people might even be scared that, like, they won’t know where to find the light switch next week. And that brings a whole different set of challenges. And so that requires not just confidence in how you are able to sort of trust the technology today, but also that there’s trust in a governance process that will continue to iterate and evolve alongside the technology.

Excellent. Denise, help us then here. I know Singapore has released guidelines, standards around AI. Tell us about the Singaporean experience in thinking about regulating trust and safety in AI.

Thanks so much, Trevor. And thank you to the IAPP for putting this together and for having us. Maybe I’ll answer that question by linking some of the concepts that we’ve talked about. And that sort of underpins our philosophy. Trust and safety is the outcome that we want. You know, we want to create the necessary conditions for the society. to thrive for the public and the enterprises to use the technology with confidence. So AI for that public good. To do that, we need governance. We need a framework of thinking about how we can govern the technology, and we’ve been doing this for all sorts of technology. AI is but one. Regulations are a mechanism, a type of governance mechanism that you use when the necessary and correct conditions exist.

And so that map of that concept informs how we think about our governance approach. So on issues that are very clear, where there are clear harms, we have stepped in to regulate. An example of this is elections regulations that we put in place where we prohibited the use of AI deepfakes to represent candidates. It was time -limited. It was for the period of elections, but we stepped in and put a law in place for that. We also have laws for AI creating online harms, as well as AI in scam situations. So that is the part where we regulate for clear and present harms. For the rest of it, a lot of it we leave to sectoral regulations where there’s already a web of existing regulations, and on specific issues as well.

John and I and many of us are in the data protection field where, as John has said, there are already existing laws that can be tacked on, updated, reviewed in order to deal with this new technology that has come about. So where we have done AI governance frameworks and tools that you’ve mentioned is where we’ve seen a need to create some sort of horizontal principles and platforms to think about the sector agnostic general issues on transparency, on what model governance for corporates could look like. We haven’t seen the need to regulate that horizontal layer just yet, but certainly a need to articulate some of these principles. And that also allows us to create more certainty for the market, to give them some direction that actually this can be a market -driven assurance system that has demand, has supply, and has what we’ll call them proto -standards, early days of standards about what good looks like.

So that’s the work that we’ve been doing and trying to create and simplify. We have a seed and an assurance ecosystem that sits, I would say, adjacent and complementary to regulations where they’re needed.

Fantastic. Please, please.

So just to comment on that, one area that I think is proving very important, and people are discovering this across jurisdictions, is even where existing laws apply, there is a problem where AI systems make it hard to know whether or not those laws are being broken. So this is where that transparency layer you were articulating really becomes important.

Give us an example.

Yeah, and I’m going to make it U .S.-centric just because it’s the one that’s top of mind, so forgive the bias here. So in the U .S., we have equal employment laws. It is against the law to discriminate in the course of hiring. So in theory, a piece of software that perpetuates discrimination against particular candidates, for example, not considering the resumes of people over a certain age, is violating an existing law. So people will say we don’t need any further regulation. We’re done. The problem is you can tell in a human run system where it was just a bad apple in the HR department, it’s been historically easier to prove that case. Now, when it’s AI powered software making that decision, it is really hard as a worker who’s just put in a resume and never got an answer back to know if something was going wrong.

If you actually get up your courage and file a case, it is really hard to prove your case if there is discrimination. And so without some type of disclosure regime that requires transparency in these high risk scenarios, high impact scenarios, to have transparency and disclosure about the system that is being used, impact assessments to make sure that discrimination isn’t happening, you actually don’t get the remedy that people really need under existing law. And so that’s where I think this horizontal piece can complement the sector specific vertical laws in a light touch way, but actually gives meaning to the laws on the books.

So I think that’s a great example of the harm trigger that Denise described, that we identify a clear harm and that may be a place where additional regulatory structure might be helpful. I think we heard pretty significant consensus across our panel. Trust and safety is good. That’s good that we’re there. That’s a great consensus to achieve. And not complete consensus on the idea that additional regulation is needed yet. With the exception perhaps of a few scenarios in which we can identify high risk or harm. Let’s go to our audience for a second. Help us describe the relationship between innovation and regulation in AI. If you think it’s a great relationship, thumbs up. If you think it’s a bad relationship, thumbs down.

If you think it’s complicated, make it complicated. What do we think? Oh, I see a lot of content. What does our panel think? I think it’s a good relationship between innovation and appropriate regulation. Fascinating. We have a very strongly opinioned audience here. That’s great. Let’s talk about regulation again and dive in just a bit deeper. I think one of the things that’s tremendously challenging is prescriptive regulation, trying to understand harms that might occur before technology is fully adopted broadly in the marketplace. I’m a veteran of the privacy world going back to 1995, 1996. And in the late 1990s, we were talking extensively about cookies and how do we regulate cookies and the privacy issues associated with cookies.

Guess what? We’re still talking about cookies often. And I know for many of the privacy and data protection, they’re nodding already. They’re crying a little bit because it’s so, so painful to implement. implement many of the cookie banners and cookie consent mechanisms that we have. And I’m not entirely sure, we might get John to admit this even, that, you know, those cookie banners are actually driving the outcomes that we hope for. We identified the biggest and worst harm or concern and dedicated resources appropriately to that. Amanda, I’m going to jump right to you. Talk to us a little bit about identifying those harms. Alex gave us one, which is perhaps AI reviewing HR submissions, resumes, CVs, and language in those CVs may actually create results that were not intended, that create bias, that, you know, in a human -driven system would be easier to find, in an AI -driven system just much, much harder to find.

That’s a great example. How do we identify those prescriptive harms, those harms that we’re not quite sure about yet, that may emerge? Do we do it through principles, through ethics, through what?

I think all of the above to some extent. Part of why we start with principles in our governance program is I think it’s helpful to orient towards what do we care about, right, as we then try to build a program that realizes those outcomes. I think we also can look at existing law that reflects where there are harms, like in the employment context, where people could be mistreated or treated unfairly that we know we care about. And there’s been a lot of effort and regulation to define high risk, high impact. At Microsoft, we have something called the sensitive uses sort of scenarios where, you know, we have three categories where technology could have like an impact on someone’s life opportunity or consequential life impact of something like employment or education opportunities, for example, or how someone’s treated under the law otherwise, all sort of fit in that context.

We have the second big category of harm that we have defined as around sort of the risk for psychological or physical harm. So think about vulnerable populations there. Think about the use of AI in critical infrastructure. And then the third category is the use of AI that impacts human rights. So, you know, we have our way of defining what is really high impact. You know, a lot of governments, again, have taken different routes. I think the other thing that we’ve seen is the kind of emergence of a conversation around sort of technology itself that poses specific high risks. For example, highly capable models that have a whole other set of risks that are the risks that are being defined.

And that’s one thing that I just want to draw out as we think about this and drawing upon what I feel like, you know, and I didn’t grow up in the privacy world, I grew up in the cybersecurity world. And one of the things that I think a lot about as we work on, you know, defining these harms and figuring out what to do about them, that we can learn from the kind of… decades of work on cybersecurity is the challenge of thinking about how to address risk across the supply chain. And I think it’s a slightly different conversation in AI than it has been traditionally in security with software and cloud technologies. But there is like a common principle or approach that I think we should really look at closely, which is, you know, we are oftentimes in the context of AI thinking about risk and harm where the technology is actually used, right?

And then what’s difficult is figuring out what do we do across the whole supply chain to manage that risk and have that be cohesive. And one of the things that in the cybersecurity context, we know what the risk or harm, it’s much simpler. It’s security risk, that we care about. But we have the same challenge in terms of like, how do we manage that risk across the supply chain? And one of the challenges over decades of work in the cybersecurity context is… Instead of wanting to… put emphasis on one part of the supply chain or the other at any given moment instead of, like, really dealing with the really hard governance challenge that it is everything at once.

And so I think when we, you know, think about the complexity of defining harms in the AI space, that’s important work to do. And also, in the context of managing risk for any of those harms being realized, we also need to think really hard about looking across the whole supply chain at once. Even though it’s hard from a governance perspective, that’s going to be most important for managing the risk ultimately.

Fantastic. And I misspoke. It’s prospective, not prescriptive regulation. But John and Denise, maybe talk to us a little bit about that. And let me frame it for you both. And Denise, we’ll have you start. Clearly, with data protection regulation, we have had the GDPR now for over seven years. And the effect of that stands out. And I think it’s important to think about that. And I think it’s important to on the global policy environment has been enormous. We now have over 120 countries that have privacy laws in place. Many, many, many of them have genealogical lines that point back to the GDPR. And yet we haven’t seen that in AI yet. The EU AI Act has not taken off around the world.

We don’t see a Brussels effect happening on AI. Is it because the challenge of identifying harm, the challenge of prospectively trying to identify what might

You always ask me the tough questions. I think, first of all, the harms question, because I think that’s relevant to the regulation question that you’re asking. I think the starting point must be that every country has a unique context. And it’s the job of the government to figure out how to do that. And I think that’s the challenge of prospectively trying to identify what’s harmful, what’s harmful. harmful to their society. I think there’s going to be a huge amount of overlap, but at the end of the day, what’s harmful in one context that’s harmful in India may not be the same as what’s harmful in the US. And the cultural context matters. That said, I think there’s actually increasing consensus, I feel, about what harms or archetypes of harms there are vis -a -vis AI.

And we see that, for example, the International AI Safety Report is starting to anchor some of this taxonomy and sort of buckets and archetypes of harm, and we also see that beginning to happen at Iceland, for example. Those conversations are happening. How does that link to prescriptive regulation or legislation? I think that if the harms are still being coalesced and formed, it’s quite difficult to be very prescriptive about how you deal with those harms, because that, by definition, is sort of changing and still coalescing. It’s still quite nascent. That’s not to say… we should step back. I think we just probably need a slightly more agile way of thinking about that broader concept of governance.

So in the social media context in Singapore, we did it via codes of practice. So we have a broad sort of umbrella legislation that creates a legislative frame for which these codes of practice apply. But the codes of practice can be updated more easily. Same thing, actually, with our data protection law, the PDPA, which is structured quite differently from the GDPR. Our PDPA is actually very not prescriptive. It’s outcome driven. It’s fairly broad. But most of the guidance that PDPC provides, and these are for compliance, is done in advisory guidelines. So I think there are regulatory mechanisms you can use that are less prescriptive than primary legislation. And that gives you enough levers. It’s tools in a toolkit, basically, to be able to deal with the harms and with the problems that the society is facing.

Excellent.

To dispute you a little bit on the lack of a Brussels effect, I will say, I mean, going actually back to Denise’s point, so not only is there some harmonization happening around the scoping of the harms, I think that certainly is happening, but also on potential points of intervention. So, for example, one of the key elements of the EUAI Act is looking at high -risk scenarios and having their remitigations in place. We have similar laws under consideration in multiple states in the United States, one in the books already in Colorado. They would never say it is a copycat. It came from its own origins. But it is lawmakers thinking what is an appropriate right -scale intervention to that particular risk.

You can look at the recent transparency laws that were passed in California and New York, very similar discussions to the Code of Practice for General Purpose AI models that came out under the EU practice. You can look at the EUAI Act’s provision for regulatory sandboxes and this notion that we want small and medium -sized enterprises, and others to be able to innovate and get a little bit of forgiveness or wiggle room under the laws as they figure out how the regulations apply. That law just got passed in Utah. So there are these glimmers where we are seeing… smart solutions to specific problems and people learning from each other.

I think in the absence of that umbrella AI standard, that interaction with fellow regulators across disciplines and domains becomes really important. Or I will ask you, does it become really important?

Yeah, it is. It’s hugely important that we coordinate. You know, these are new challenges that we’re all facing. On the GROK issue, obviously, it’s under investigation, so I won’t be able to say too much about it. But, you know, we’re interested in what, you know, how models are trained, what data they’re trained on, what output filters are included, what kind of safety mechanisms. I’m interested in what kind of ingestion there is of data when it’s used at that level. But there’s some complexity in that case as well because, you know, you’ve got users using a tool that’s amplified. It’s amplified by social media. I don’t know whether the same functionality is available in any other image generation tool that just hasn’t got the same media because it’s not amplified by a social media platform.

And but, you know, very early on, I think I was back home in New Zealand, actually, on about the 5th of January and started to see this. And I messaged back to the office and said, what are we doing? What’s Ofcom doing? How are we connecting to our international colleagues? And that’s so important. And so we’ve, you know, we’ve messaged into GPA. We’ve coordinated very closely with Ofcom. And, you know, we have to cope with the fact that regulation is a little bit fragmented. So Ofcom is responsible for administering the Online Safety Act in the UK. Now, that is legislation that seeks to regulate the kinds of harmful content that can be delivered to a child’s device, for example.

Right. I see this thing. Is that regulated by online safety? If so, it’s Ofcom. How did that get to me? Well, that depends on how the underlying data was processed. That becomes an ICO, you know, GDPR issue. So we need to be working very, very closely, and we are. But also with the crock issue, one of the very early things we did was to reach out to our colleagues in the GPA, the Global Privacy Assembly, and say, who else is looking at this? Let’s make sure that we’re not sort of treading on each other’s toes, or at least that we’re sharing information, that we’ve got the same ideas, that we think the same way. And that can be tremendously powerful, whether or not you can point to a regulation that that app or that platform is clearly in breach of.

To describe a set of expectations about harm mitigation across a coordinated group of global regulators, I think can be quite powerful. And, you know, just to see how, you know, the alternative for some of these platforms is not necessarily being investigated and trained by the ICO. So it’s like what I noticed the first day that I was here when I went to flip TikTok on and saw this is not available in this country. So if the offering in a particular jurisdiction does not meet the standards and norms of that jurisdiction, these organizations need to understand that they can be switched off, that they are not actually all powerful.

I just have the image of the U .K. Information Commissioner doom -scrolling TikTok in my head now. Let’s do a quick round, and please do keep your answers short, but innovation is not limited to technology, is not limited to business practices. It’s also very powerful in the… The privacy -enhancing, safety -enhancing tools that we use inside organizations. It’s in regulatory structures. Denise has mentioned regulatory sandboxes, or maybe it was Alex, but we’ve heard regulatory sandboxes mentioned. What is the one innovative idea in trust and safety that you think holds real promise? And I’ll let you do one sentence to explain it, but this is a speed round. So we’ll start with Amanda and then work down and come back to Alice.

One sentence. Okay. Is that my sentence? I think about provenance tools as an area of innovation. Again, this is calling upon my cybersecurity background, but I think, you know, something like agentic AI is an area where there’s a lot of interest -concerned governance momentum. And one of the challenges is being able to look at something that is fundamentally not just like one technology. It’s technology. It’s a bunch of very dynamic components, models, platform tools, services, applications all working together. And while that feels like a really new, hard challenge, we actually can draw upon what we know of software to actually be a set of dynamic components as well. And one of the ways that we’ve figured out how to govern that or working towards figuring out how to govern it is with software -built materials, something that really allows you to have the ability to track those dynamic components.

And I think that’s something we can apply to agents.

So it increases transparency. It tells you, you know, which algorithm or which system this might have come from. It helps with accountability broadly. Yeah. Excellent. John, what’s the most promising trust and safety innovation that we have?

Well, you challenged us with one word. So I’m going to go with agency. Agency. And I think it’s, for me, it’s a word that, you know, so much of our world is dominated by consent, which is, I won’t say broken, but it’s under strain as a useful concept. Agency, I think, has capacity to recognize that, the objective is to maintain and to restore and maintain an individual’s agency as it uses any product. And that’s consent. It’s actually making sure that provenance is delivered, for example. You can’t have agency if you don’t know the origin of the data that is delivering this agentic miracle to you. It gives you tools at the other end. And consent is always conceived of as a front -end authorizing concept.

But agency says, okay, I’ve done that now. Where’s my delete everything button? Or my I don’t want to do this anymore button. So I think if developers can be thinking about how they deliver the best possible service in a way that restores and maintains the agency of the consumer, I think that will go a long way to addressing some of the problems that we’re seeing. And I think

Fantastic. I had a lot of… Professor years ago now who described burden -shifting wrenches in the law. And I think consent is a burden -shifting wrench that moved much of the burden to the data subject, to the individual. Agency, it sounds to me, is an idea to move back to those who might be accountable and have them have fiduciary or stewardship responsibilities for that person. Denise?

I would pick privacy -enhancing technology. I think it’s an interesting technological way to deal with at least one part of the problem, which is how do we secure the data, how do we make sure that the personal information is well protected. And it’s advancing so quickly. So two years ago, we were looking at federated learning for training of AI models, and no one could figure it out. I think it’s actually being done in production now. So there is – I’m a lawyer, so I can say this. Sometimes the law cannot solve the problem. But actually, maybe another technology can.

Fantastic. Alex?

Well -staffed, empowered, independent regulatory bodies that can help represent the public interest. Wow. And because in some countries those are under attack right now where that is not available, well -resourced, technically informed, independent civil society that can play that role in the interim.

Fantastic. Yeah, the importance of having watchdogs, yeah, entities that are watching and observing, commenting, enforcing, really powerful. So there are four great innovations, provenance, agency, privacy -enhancing technologies, and well -funded regulators or civil society. Well done. I think that is a great start. Let’s do another audience poll. How many of you here in this audience are responsible for AI or AI governance, AI ethics, AI safety inside your organization? Hands up. It’s almost the whole room. Keep your hand up if you’re also responsible for something else in addition to AI or it’s just AI. It’s more. I think it is a pretty complete overlap, almost a complete overlap. There’s at least a significant percentage that were responsible for more than one thing, and one of those things was AI.

I think that’s an example of the complexity that we see inside organizations today. John described the coordination necessary between Ofcom and the ICO in the Grok investigation, which is ongoing, because there was not a single place where regulatory authority existed to address that concern. This is a really complex environment. The number of harms or issues span from children’s safety to intellectual property. From bias and algorithmic discrimination all the way through deepfakes and other things. Alex, how do we… How do we put that all into a pot and make it something meaningful?

Well, what if you can’t put it all into a pot? A pot is a common denominator in all of those things, but AI is a tool that touches everything. So I really do think you actually need a nuanced approach that looks at a particular risk, what those mitigations are for that risk, and then goes from there. The privacy considerations when you are sharing your most intimate concerns and questions about the world with a chat bot is very different than these questions about deep fakes and fraud and impersonation. It’s just you need to have a different legal regime. I think some of the common elements that run through, one is that transparency and rigorous approaches to risk mitigation really matter, and that can either be through regulation or through principles and best practices with meaning and standardization and watchdogs reading those disclosures.

And the second is this burden of the user. So when Trevor introduced me, we described my org. organization. We represent users’ rights around the world. I am all for user empowerment. And also, we cannot put the burden solely on users to navigate this moment. Indeed. And that is the major lesson of the cookie example you were saying before. We didn’t misdiagnose the harm. We misdiagnosed the remedy, which was the burden on individual users when we don’t actually have market choice, nor the time or mental energy to just read a whole bunch of disclosures and act alone. And so solutions that acknowledge the harm are tailored, but also take that burden off individual users. So you’re empowering users, but not burdening them or leaving them to essentially defend themselves unprotected.

We have to think about that.

Okay. Sadly, we are at the end of our time, but we have one more pop question for all of you, and we’re going to let this be our close. We have gone through the AI Impact Summit, the AI Action Summit, the AI Safety Summit. Five years from now. What is the AI summit going to be called? What’s the word that’s going to be in the middle there? So this is a one -word answer again. What’s it going to be? I know it’s a tough question. So, Denise, I’ll start with you because you’re able to handle the toughest questions. Ah, the AI Trust Summit. Okay, John?

Nostalgia.

Nostalgia.

Thriving.

Thriving, AI Thriving Summit. Okay.

I’m going to cheat. For the people, by the people. It’s more words. They’re so strange.

Some of the people. It’s hilarious. To get on a poster. Here’s what I know. I know that there is incredibly hard work that needs to be done to bring trust and safety to this ridiculously powerful technology that I think, as Sundar Pichai says, will be more profound than electricity. That hard work happens every single day inside organizations. I’m going to cheat. I’m going to cheat. I’m going to cheat. I’m going to cheat. I’m going to cheat. I’m going to cheat. that are implementing these tools inside civil society, that are watching and guiding that behavior inside regulatory offices that are navigating to ensure that marketplaces around the world, that the digital economy gets this right. I feel better because people like this are doing that work every day, and I hope you’ll join me in thanking them.

Thank you very much. Thank you so very much. Thank you. Thank you. Well done. Well done. You were fantastic as expected. So what is the I am and I fly to London for magic.

And we are delighted to note that many of the activities that were outlined in the MOU have already kick -started. So I think to that extent, it’s fantastic that Dr. Padhya is here today and will share with us his ideas on where an Indo -German collaboration in innovation and AI can have a value proposition for both countries. I’m also delighted to have my own colleague, Dr. Thomas Kuhn, who has come all the way from Germany from the Fraunhofer Institute of Experimental Software. He will be sharing some of our experiences and competencies in AI, particularly with focus on workplace changing, which is, of course, one of the biggest topics we have today, but also manufacturing, agriculture, and health.

And I think one of the key topics that Fraunhofer can present to the Indian audience is data space. We have created a secure data space in the cloud for many years where challenges remain. Industries are brought in and discussed. So you will hear a lot from him. I’m particularly delighted that four Bangalorean CEOs are here today. We have with us Sindhu Gangadharan, who needs no introduction, I think. Anyone knows that, and who doesn’t know is coming under a stone. So we also have Anshuman Eversi for CTO of Mercedes -Benz Research and Development Center in Bangladesh. And we have Dattatri Salagame from Robert Bosch Software Solutions. And, of course, Prashant, he’s sitting at the back, Doris Rami from Averior, which was earlier Continental.

So as you can see, a power -packed industry captain session, which will follow after the panel. And last but not the least, we have Murali Nair from Bretelsmann Stiftung, which is a think tank, which has been producing products. A lot of knowledge papers around India and Germany, and actually positioning India as a positive partner for Germany long before Germany even started looking at us so seriously. So thank you for that, Murali. I’m standing here in front of you today as the head of Raunafa in India for the last 18 years extremely proud that Raunafa is one of the largest applied research ecosystems in the world we have 76 institutes in Germany we are present in more than 80 countries we produce two patents for every working day we are also the inventors of the LP3 white LEDs and many such inventions but what really singles us out in this innovation corridor is the fact that we take research from lab to the market in the shortest period of time so this is an area where India really needs support and we are delighted that we were one of the first movers coming into India 18 years ago and even today there are very few international R &D organizations that are active in India you We have been earning more than 70 million euros in the last 10 years from research contracts with Indian industry, government and research organizations.

And we have an absolutely amazing network of partners in India, including organizations like CDOT, but also with Indian industry, which is really gearing up for innovation. So I think we came in at the right time and took advantage of this, you know, innovation trajectory that has picked up in India. And it’s exciting to see what’s unfolding before us. I have to say that today what we are bringing together is really a thought -provoking session. We only have 15 minutes, so I would like to get on with business immediately. And we hope that some of the thoughts that we are leaving behind with you today will resonate and will actually trigger a long -term dialogue and engagement so that we can take the Indo -German innovation collaboration to greater heights.

I would now like to invite Mr. Georg Enzweiler. To kindly deliver his special address.

Good morning, ladies and gentlemen. I don’t know whether it’s maybe that we are just as crazy as you are sometimes that we are big fans of Karnova here in India. It’s always delightful to be invited by you here in India. You do amazing work here in the country. I wish we’d have several Anandi ears here in the country to multiply your efforts. So thank you very much for the invitation. And I’m impressed by the distinguished panel of speakers today and of panelists here. Thank you. All of the panelists and speakers here today are drivers of technology, and so it makes sense that you are also at the forefront of innovative AI technology. From increasing crop revenue, improving diagnostics, or minimizing errors in manufacturing, AI undoubtedly has huge potential for social and economic good.

AI is predicted to contribute between $5 and $15 trillion to the global GDP by 2030. But there are also questions, of course. How can we ensure that this growth is inclusive? How do we minimize negative effects for people and the planet? What kind of effect would this have on labor markets, for example? After all, the motto of this summit is actually welfare for all. India, for one, has ambitious goals to create massive computing infrastructure in big parts powered by green energy. Germany is investing in so -called AI lighthouses, which foster AI innovations for climate and environmental protection. Since 2020, Germany has funded over 60 projects leveraging AI for sustainability. The topics range from wildfire prevention and renewable energy to biodiversity monitoring.

And circular economy. that government, academia, and industry work hand -in -hand. By promoting research and development, creating a clear regulatory environment, and investing in the training of skilled workers, we can unlock a broader potential for AI. India shows huge potential here. It accounts for 15 % of the global AI talent pool and has the highest AI skill penetration rate. In terms of research and development in AI, India ranks third in the world after the U .S. and China. India and Germany, with their longstanding scientific partnership, are natural partners in creating solutions that are sustainable and inclusive for all. Hence, only two days left. Ministers Vaishnaw and Wildberger launched the India -Germany IA Pact, a new partnership focused on implementation -driven collaboration across government, industry, research, skill development, and innovation.

It will include aspects as AI for industry and manufacturing, talent, skills, and mobility, joint research, innovation, and infrastructure, and overall AI for social good. So I very much look forward to the discussion of today’s session, that it can give fruit for thought on this most relevant topic. Thank you again for the invitation, and I wish all of us an interesting discussion on, many of these pertinent topics. Thank you.

Thank you for your kind words, Mr. Ensweiler. We now have the pleasure of hearing Dr. Thomas Kuhn, who heads the Division of Embedded Systems in Fraunhofer IESE. As you all know, we talk artificial intelligence. In Fraunhofer, we call it augmented intelligence, which means that human intelligence is still at the core of what we’re talking in terms of AI. It’s a pleasure to bring Dr. Thomas Kuhn to India. The floor is all yours.

Yes, thank you for inviting me. Yeah, what is Fraunhofer? doing in the field of AI. You all know AI is on one hand large language models, mostly driven by the US. We have huge open source models coming from China. So what is Fraunhofer contributing? And our goal is to bridge industry and academia to support the industry in creating new products and bringing ideas into practice. And I’ve chosen here a few highlights on what we can do as Fraunhofer. So for example, when you speak about AI, the question is, how reliable is the result of AI? Do you get concrete, do you get reliable information? That’s one topic, trustworthiness. How can we achieve trustworthy responses? That’s something that Fraunhofer is researching on.

For sustainable growth, it’s also very important to preserve knowledge that is available in the company, especially if people retire. Small and medium -sized enterprises have exactly this problem that knowledge is leaving, and you cannot get this knowledge back because the new employees simply don’t have this knowledge anymore. So how can you preserve knowledge? We call this industrial AI, so bringing AI models into practice, creating specific, specialized AI models based on company data, based on sensitive data, not personal data of persons, but sensitive data for business, sensitive data for companies, so that they can support these companies with these needs that the AI can provide. For example, it creates a colleague that is an expert for a specific device and that can help.

humans to work more efficiently with this device. And last but not least, as my colleague already did introduce, data spaces are a key technology for that because for AI, we need to be able to train AI and therefore we need a reliable access to data and a way to share data, but based on rules. And that’s what data spaces are meant for. With Raumhofer, we have the ability to instantiate data spaces that scale up to 10 ,000 transactions per second, and that’s quite a lot. So Raumhofer is structured into alliances, and the most important one is the big data and the artificial intelligence. Alliances, so more than 30 institutes that team up with AI, in creating AI strategies, each institute with its own field of competence, but all of us support best practices, studies, expert opinions, and we support all companies in creating new solutions.

And as you can see, we have a lot of fields that our institutes are working on, life sciences, healthcare when it comes to diagnostics, to personalized medication, logistics and mobility for optimizing supply chains, making them more resilient, production and industry for that all, very important. We also need to keep production in Europe to remain resilient, so it must be economic to produce in Europe. Energy and environment. Renewable energies. Smart goods. self -managing grids. That’s very important topics also for AI and big data, business and finance, security, to make sure that our data is well protected. I cannot go into detail for all of these topics, so we’ve collected a few samples on web we are doing from embedded AI, image analysis, collaborative transport, cancer diagnostics, swarm intelligence, of course also defense topics.

I cannot go too much into detail here but AI has a huge impact and why the ways regarding general purpose LLMs is not our ways. The applications that can be created with AI, the transformation of industry that is very well our knowledge. to bring AI to use. for industry. And therefore also to use for all, and then we are back at the topic of this event here, welfare for all and happiness for all, because every industrial revolution in the end did yield much more wealthiness for everybody. I have brought some highlights, and I promise I won’t go into details here, no worries. So the virtual colleague is one thing that is important when people leave companies.

We have expert knowledge, we have experience, and this is very hard to preserve. So just imagine a virtual colleague that follows the people in the field, that learns from them, and that keeps the knowledge in the company once this person leaves, so that the company knowledge is retained. That’s one project. One project that Fraunhofer is doing in the field of AI. to support industry and also smaller medium -sized industries. Trustworthiness, that’s very important. We all know that AI is not perfect, and probably some of you have already tweaked the AI maybe a little bit on purpose to see, okay, how far can I go? With that, AI basically is stupid. It has a lot of knowledge, but it has no understanding of that.

Trustworthy AI means we observe, for example, the field of use for a specific AI model and track whether we are leaving this field. We have an uncertainty wrapper that gives you, with each response of the AI, an uncertainty value that says, okay, how trustworthy is this particular response? And that’s very important when we want to apply AI also in safety -relevant environments, for medical. for medical diagnostics, for image recognition, and also in traffic. That’s very important then to know how safe is the assumption of the AI here. Moderated AI training is very important when we want to work together. For example, a robot that can grab something that every human can do, grab into a box and take something out.

For a robot, that’s a very big challenge. We can train robots to do that, but it’s much more effective when we can do a federated training where everybody can contribute data, and in the end we receive one model, but one model where you cannot derive any sensitive data back because nobody wants to share their sequence. So that’s where data stays. That’s where data stays. That’s what data spaces are meant for. We share data based on rules. We can provide data just for training of AI. Everybody will see the raw data for that. We just use it for training of AI. And here, Fraunhofer is researching both government policies and efficient implementations of cross -company data spaces.

So that’s my pitch, my motivation. I hope I was able to give you some ideas, some insights in what we can do with AI besides of open AI. I think Sam Altman is not here, so I can’t say that. They’re doing great things, but there are also great things that can be done in addition to that. Thank you.

Thank you very much, Thomas. This was just an appetizer. As you know, we have a lot of time. We have very limited time, but anybody who’s interested can visit our stand, where we’ve also demonstrated some of the use cases in AI and agriculture, and also AI and medicine, and AI in manufacturing. You’re welcome to visit us at the German Pavilion. I would now like to invite the CEO of Center for Development of Telematics to share a few thoughts on where India and Germany can work together. Thank you so much.

Thank you so much, Anandi. Thank you, Anandi. Quite pervasive, it is being applied to almost all the sectors. And where it is not applied, it will be applied sooner or later. because it underpins the competitiveness, productivity, and societal resilience. And for India and Germany together, Germany being the precision engineering expert, you know, for years, and India bringing the scale, I think there is a very good way that India and Germany work together and align how the AI is taken forward in a very responsible, ethical way, so that not only it helps us economic progress, but also meets the social good challenges. As we know, India’s AI system is quite vibrant, ranking third globally for its competitiveness and developer activities.

Our AI mission is already in progress. We are investing more than $2 billion. We have given to all the startups 38 ,000 GPUs, and this will be further increased. It could bring $1 .7 trillion value to India’s economy by 2035. And the tech sector particularly is productive to contribute $280 billion revenue this year itself. Germany’s AI market is also rapidly expanding, driven by strong industrial integration, particularly in manufacturing and substantial public -private investment. Germany’s AI market is expected to be nearly 30 billion euros by 2030. I wrote that there are more than 600 startups in Germany, and we also have a lot of startups. I think together, how the startup ecosystem could work together for the benefit of not only both the countries for bringing global good to this sector.

So there will be a fair win -and -win situation for India and Germany, partnership between two great countries given our diplomatic relations, given the, as I said, the precision in engineering which Germany is famous, and the scale what we bring in India. I think there are many areas where we can contribute, but I thought I will focus on two, three. One, I think it was mentioned in smart manufacturing. Germany’s leadership in industry 4 .0 and India’s expanding manufacturing. I think this government has invested heavily on the manufacturing under various schemes like PLI and DLI schemes, and it is going to go forward. And therefore, how do we use AI in smart manufacturing? We would like to work with Germany in terms of developing smart manufacturing standards, cross -border industrial data flows and safeguards, enable energy efficiency.

And since we arrived late in this manufacturing space, a lot of our manufacturing plants have actually started using AI. In fact, recently there was a science evenistic wherein the Tata’s, how they are going to bring down the cost. using AI in manufacturing. The second point I think Anandi brought up for agriculture, I think Parnasar is already working in India for agriculture, and agriculture being one of the key partners for economy, the service sector and the economy, agriculture sector, I think it also makes us a good partner for how do we improve the productivity, the yield, the income for farmers, the efficiency gain, productivity gain, and that will be another area we can work together. I would also like to say that, you know, given, especially given the economic progress we are making, and we see a lot of challenges coming from the cyber security area.

We get millions of attacks in our country, millions of attacks. and I’m not sure what is happening in that part. So, therefore, AI in cybersecurity is very, very important, which we use to some extent and we would like to learn from Germany, whatever, and we would like to share these, what we are doing, how we are using AI to, you know, come to a pattern because the kind of data flow, for example, in India, we receive data rate at 10 terabyte per second. 10 terabyte per second is the data rate. And how do you process this data in real time and be able to tell that, that how and where the attacks are happening? As far as the CDOT is concerned, we work in various areas.

I don’t know, some of you may be aware. I would like to place it on record that India made its own 4G and 5G, and it was launched by Honorable Prime Minister in September 25. And this was a journey we took alone. For 6G, we would like this journey to be together with the world because we were actually not. We never developed 1G, 2G, 3G. Suddenly, our Honorable Prime Minister said, you know, 4G and 5G, and we did in two, three years, and it is launched today. Thank you. close to 170 ,000 base stations are radiating, generating a data rate of 5 petabyte. And we’ll go further. So 5G, 6G is one area. The second area is quantum because as all we know, many of the leading economies have put a sunset date to the current level of encryptions which will be broken because of the quantum computers.

And therefore, there is a need to work on quantum together. We, particularly in CBOX, we work only in quantum communication. In fact, I am the chair of the National Quantum Communication Hub along with IIT Madras. We fund a lot of research into quantum to start up. So this could be another area where we could work together. The third thing is I’m not aware what kind of frauds happen in Germany or happen or don’t happen. In India, there were, you know, there were a series of frauds, cyber fraud calls or this type of calls. So we developed the system at India scale and are successful today in blockage. For example, I’m just giving example of we used to get 1 million regular international calls, and we were getting 15 million spoofed calls, 15 million spoofed calls per day.

And at my gateway, in 5 milliseconds, I have to decide whether this call is a real call or spoof call. Today we have deployed the system in India, which in 5 milliseconds tells you this is a spoof call, drop it, and this is a real call, send it. So at India scale. We have developed a platform called SanchalSathi .gov .in. I don’t know how many of you are aware. This is, again, a digital intelligence platform. One side is exposed to the customers, the citizens, and in back it is connected to all kinds of stakeholders. What used to happen is, as Telecom, I have declared one, let me first check the time. So, yeah, I think it’s happening.

So one is that, you know, in telecom I have said this number is turbulent. Bank doesn’t know. Police doesn’t know. So what we did, we integrated all of the stakeholders in one single platform, which we called digital intelligence platform. Once I declared this number to be a turbulent number, for any reason, it is flagged in bank, police, everywhere, so that everybody takes care. Today, when the actual financial transition happened between A to B, there is a module which we have developed called FRI, financial risk indicator. Before the money is transferred, the bank digs through the database and says, this money is going to be, is it safe? So my system tells me, no, it is not safe.

This guy is not a safe guy. So the transition is stopped. So we have done the fraud management at scale. We would like to work with Germany to further enhance it and how to use AI into this. And going forward, I would only say my time is over. AI should not widen inequalities. It should strengthen inclusion. Productivity and resilience. Let us ensure AI becomes a pillar of sustainable economic growth and social good. As Anandi said, we already have an MOU with Fraunhofer, which we are working together. I would be very happy that, you know, if this partnership goes, must go on. And we together are going forward, given the geopolitical changes happening, we’re going forward together.

We will go and develop the technologies together for the wider social good. Thank you so much.

Thank you, Dr. Upadhyay. I can only underscore that it has been an absolute privilege working with you. And it is not something where German technology is being given to CDOT, but we are actually co -creating. There are India -specific challenges for which teams from CDOT, who are extremely qualified and they have amazing infrastructure, I would really appreciate at some point, Dr. Upadhyay, some of our German delegates, maybe not this time, but next time, come and see the kind of work you’re doing. Because I think that brings a different level of understanding inside and trust. Without wasting any time, I’m sure all of you are waiting to listen to Professor Dr. Sinemus. We had her yesterday, and I must tell you that it’s really amazing to see, first of all, a woman as a head of innovation and digitalization in Germany.

to get the leg between research and then bringing it really to society and to economy growth. And at the end of the day, I’m coming out of the biotechnology area, and I think we really have lost a lot of chances in coming from the research really bringing to economic growth. And I think AI has the opportunity, and as well quantum, as you mentioned, to do this. And, Your Excellencies and gentlemen, ladies, it is a great honor for me to deliver a special address. I never have given a special address, and try to do this in the German National Technology Forum. I’m coming out of a region where we are working together. struggle together with Fraunhofer.

Even in my hometown, we have two, and one of this is the Fraunhofer Cyber Security, where we have the digital hub of cybersecurity as well. So we built a startup ecosystem in the area on the interface of cybersecurity and digital and AI. So many of this could be an anchor point afterwards. As Minister for Digitalization and Innovation, I very much welcome the opportunity to discuss how we can turn AI into concrete public value in manufacturing, in agriculture, in healthcare, and through trustworthy AI, as Thomas Kuhn has pointed it out. The AI Summit, all is about AI and we see how dynamic the process is going on. AI is already transforming how we produce, how we heal, how we grow food, and how we govern.

And the key question therefore is not if AI will shape our society. The question is how. And whether economic development and social growth move forward together rather than separate directions. And looking at the dynamic AI is going forward, we really have to be careful that we balance these economic development and the social part. And I think this is something which has to do with trust or trust as well. In my region developed a clear strategy. And our digital strategy and AI strategy are based. On a simple but a helpful demanding principle. That is. technology must serve people, not the other way around. Our AI -made Innocent Agenda combines innovation with responsibility. And we want AI that strengthens competitiveness, but also social coherence, fairness, and trust.

And this is the way we invest not only in research and infrastructure, but also in practical projects that deliver tangible benefits for citizens and business. So we have a funding program. It’s over $60 billion. And we invest in people who have a vision in how AI can benefit humans. So we invest in 170 startups till now, and I want to give you some examples so that you get an idea. And I start with healthcare, because healthcare is one of the most promising fields for social good. We support innovative projects that would be very difficult to realize without our public backing. And we fund, for instance, one project, it’s called DEMO, and DEMO, it’s not DEMO, it’s DEMO, and it’s a small group, three people who are working on PhD at the Technical University of Darmstadt by Alma Mater, and I was happy to give them 1 .8 million euro, because they are developing a load -bearing roboting wheelchair.

A session to a walking robot. that can safely and autonomously overcome barriers such as stairs or uneven ground using advanced robotics and AI. This project will help for the people with mobility impairments. That is no science fiction. It is a difference between dependence and independence participation on everyday life. And I think that is really a good example of how we invest our funds. Another example is RISCA, Risk Certification in Cardiology Using AI. The goal is to build a clinic decision support system that analyzes patterns in ECG recordings with AI and detects cardiovascular diseases at an early stage. so you can go through prevention. That are two of a lot of AI and healthcare startups we are giving our funds in.

And a second field I would like to highlight is agriculture. Even for India, agriculture is a very important area and a rich and crucial pillar of economic development and food security. Again, we support a project and there is only one of a lot on the early detection of plant diseases. Using automated analysis of satellite data, so we even connected with the space, AI models can recognize signs of plant stress and disease at every early stage of growth circles. So, you can really reduce pesticides and analyze before. So, you can really reduce pesticides and analyze before. So, you can really reduce pesticides and analyze before. AI can, at the end of the day, make agriculture more sustainable and more productive at the same time, and I think this is something where we have to go through.

Another example in our strategic agenda in the future of AI is that we set up an AI innovation lab at Hessian AI, co -funded by my ministry, and we provide a high -performance computing environment specifically for AI applications, along with advisory services for science, business, and public administration. So this is a particular focus in the startup and SME. We want to enable smaller players to develop innovation AI solutions, and we want to be, at the end of the day, in a position that trustworthy AI is not a slogan but a practical challenge. And this is why we established an AI quality and testing hub in Hess, a public -private company that develops methods and tools to test.

AI systems. So at the end of the day, the aim is to translate the idea of trustworthy AI into testable criteria and practical producers that business can use real deployments. So you can say that is trust for AI and we give it as trustworthy criteria. To the end of the day, I think, looking on the discussions we have had in the last days, that the inner German dimension is particularly important. India has shown with initiatives like other UPI and IndiaStack how digital public infrastructure can scale and enable innovation at massive levels. Thank you. Germany can bring expertise in regulation, not too much, data protection. and quality assurance. Together we can build bridges between scale and safeguard, between innovation and rights, between economy, development, and social good.

Because we are thinking grounded on the same values, democratic values. And let me close with a conviction that guides our work. AI will not automatically lead to better outcomes. It depends on the choices we make, what we fund, how we regulate, which ecosystems we build, and whom we include. So let us proceed on our democratic values and go forward with a union, German collaboration in AI. Thank you.

Thank you so much, Dr. Sinemus. It was really a thought -provoking and a very concrete talk on what we can do with AI. There is the scope for the German collaboration. Without much ado, let me take forward what we all on the topics that you have mentioned by inviting the four CEOs to please come on stage. Can I request Atatürk, Sinemus, Anshuman and Prashant to kindly come up? We have a few minutes to really get into the topics. And I would like to just start by very quickly saying that In the last few days, I’ve attended quite a few of the sessions here, and there are so many nomenclatures for AI. It’s been defined as a transformational technology that comes once in several years.

People give examples of the steam engine and of the Internet revelation and Industry 4 .0, which dramatically changed the way we live and work. We are now riding an AI transformation, but complete with anxiety, with speculations, with admiration, but also with excitement. So let me take the audience through to what makes a CEO stay up at night. And let me start with Pratatvi. Vosh has been a pioneer in India. You know, you came here a century ago, and you’ve stayed on, resilient to the Indian market, which has not always been very easy. And you’ve been a pioneer in India. You cover a lot of base, signing across manufacturing, software, medical, and many more sectors. How does the vision board of Bosch lead in terms of the AI development and deployment?

Maybe the first question not keeps me awake in the night. The fact that you are 100 -year -old and, you know, if you are 100 -year -old, the effort that you make changes also higher. So that keeps me awake at night. As Bosch, I think we are working on two sides of it. So one side, you know, if you see we are deploying AI into the market. You know, if you see autonomous driving, AI cockpit, or AI in healthcare. So these are our products which go into the market. So fundamentally, we are, you know, navigating. We are navigating a new business model, new behavior of consumer, and developing new products for that. on the other side now we are using AI to disrupt long held beliefs of how we build software how we do engineering so this is my say while we are huge excitement to position our AI centric products in the market I would be dishonest if I say we don’t have anxiety how to get over the paradigm shift of engineering and software development using AI so these are the two edges at which we are operating now thank you so much

Sindhu let me come to you you are the world’s leader in enterprise application software one time I remember you telling me that 97 % of companies worldwide use SAP it is already embedded in your workflows even before people are talking and wondering how to go about it but how do you see you have a responsibility because you have a captive client base How do you see these innovative technologies being rolled out to companies while they still have anxieties about their business? And how do you go about assuaging those fears and building in a model that works on layers that are already set and, like he said, very difficult to move right now in retrospect?

Well, first of all, Anandi, it’s great to be here. It’s an absolute honor to be amongst this very distinguished panelist and you and all the previous speakers who have been saved. Yeah, I think, like Zazadeh said, we live in times which are changing, particularly in technology, right? I mean, literally every day you have to open up and see what is the new LLM that’s out there, right? So that’s the way it’s been and which we are now in the open. So in that sense, this is… The assurance, when we say, when we are talking about the world’s largest enterprise application, the failure from SAP, it’s a lot of… The cost that our customers place on us, many of them sitting right here at those tables, are too hard.

And so then when we say 87 % of the world’s business partners, such as an SAP system, it’s a matter of lack of responsibility and a lack of trust, which we continue to take forward, given the 1226 industries and across 12 portfolios that we have, right? But at the same time, given the pace at which technology is changing, our responsibility also from an AI perspective is embedding AI across the core of our business processes. So if you take a process like a lead to cash or a total risk force management or a design to upgrade, strategic sourcing or procurement, this is where we show our customers that we’re not helping you to make your best, we’re doing the most compliant and the most ethical and the most responsible manner, right?

And so I want to make me say, to make sure and protect the choice. Customers also have the choice to run in a secure manner, in a compliant manner, in an ethical manner, and in a responsible manner, and which means also translating from several of our speakers. You need to be able to explain to this decision that you are making or asking the user to make, giving that explainability, the transparency, the fairness of that decision -making, the auditability, right? And that becomes even more important because today when we are talking about urgentity, we are talking about autonomous workflows, we are talking about decision -making. Yes, the human is in the room, but part of the workflow is completely autonomous, right?

And so that’s a lot of the work that’s keeping us busy at night and mornings, I would say, and really making sure that when we say customers can run at their best, in a compliant, in a secure manner, in a responsible manner? How do we make sure that they are to go through the research?

Tim, what is the holdup and failure for customers to run in a safe manner? Whether you’re up at night or not, you’re certainly on your toes. The only person who’s been up at night for several months now, and I can personally vouch for this, Krishan, Continental is now a Romeo. It has not only changed in name, but also in terms of its core processes, some of its business sectors. So innovation is not only in the name, it’s also what you do. What is a Romeo now up to and what is happening?

First of all, thank you, Amandeep and Neera. I’m sure all the panelists agree. I think in India, we have moved from support apps to center of excellence. I think in Aumavio, we’re focusing on two things. One is certainty of allocation for our customers, for the audience, innovation for the stakeholders. And the second was customer -centric innovation. Just to let you know, one year I think we’ve been able to gain efficiency improvement in R &D in excess of 20%. How did we do this? Actually, there are cool things. I think we do with one strength and core technology, be it in the plants in terms of how do we enhance the quality using air, be it in terms of, you know, any product that’s done in products, there’s always a lot of false calls, which requires a lot of efforts for you to redo that.

So that’s where the implementation of AI in terms of enhancing the quality. And the second is certainly government development, I think using the GitHub, what I was talking about, where this work is coding and also for developing different test scenarios and a test situation. So these are the things that we are really focusing on. One, as I said, how do we really enhance the velocity in engineering and R &D? We’re using this automated technology, and also we do something called ReckNet. In automotive, the biggest challenge today with so much of complexity of technology is coming with the requirements, because always you leave one or two, which is very, very important. Like how do you really enhance this in terms of the ReckNet?

And second is the attention of the agent, which is used in the enterprise in the simple function like finance, controlling, or market analytics. So this helps us in giving a very good summary for the leadership to take decision based on the data. And third is certainly on the product innovations. And we have a place here in the Jammu and Pakistan, where we have a display, which is dollar code here in India, one of the autonomous mobility, which is called Enhanced Light Fusion. We have been able to, with the use of AI, enhance only the… the camera vision for easier detection and the second is something called e -travel companion where you seamlessly interact with the vehicle and the third is like our driver will seamlessly access with the car.

These are the three which was developed innovations which is displayed in the German stand as well. So in summary, I think we continue to drive because of what we call our increase to seamless integration into the operation.

Thank you so much Prashant. Let me go to Mercedes. Mercedes -Benz is a North Star in innovation. I mean you’ve been around several decades. It’s also been a very competitive environment in manufacturing which a lot of people say that Germany has lost its edge. But it’s an old school field. So how do you bring in AI into the second kind of operations, which is reflected in your standardized operations and very, I said, old school procedures? Is it challenging to get AI into it now?

Good morning, everybody, and thank you for having me here. Yes, Mercedes has been one of the most important companies and we plan to remain virtual. We’ve been doing this for many, many years. AI as a technology is not a challenge. We don’t see any technology as a challenge. And we have been using it. I think if I’m not wrong, Mercedes -Benz was the first automotive company to bring AI into the cars in 2019 itself. So if you’re driving cars from 2019, you must have experience AI. It’s a challenge. It’s a challenge. Today our focus is monthly forward works. For example, we want to offer technology to our customers. That means you drive the car and you experience a lot in the car, how the car behaves, whether you direct the car, etc.

And whether the parts are behaving in the seat. And the second part that we also are planning to do is tighten our operations using AI. We want to bring operational efficiency using AI. And many things have been mentioned. One such thing we are trying to implement. Now it can bring benefit to all of us. So, we have technology. It’s very exciting. Because sometimes you feel that it’s making so many things evident. I think that’s now and then has been the same thing and same impact so we are looking further to going ahead with using it.

And ,of course, NBRDI in Bangalore is playing a very big role into this process?

Yes. The 2019 application was largely developed NBRDI that was sent to by our AI experts back then.

It was so good we have another 5 minutes and I’d like to use this for a rapid round talking about India so we’ve always heard about the big combination of manufacturing excellence in Germany, engineering, scale and speed in India Let me ask Dr. Tri, is this heightened German interest in India, is it offensive or defensive?

I would say it was long overdue. I think given the capabilities of the two countries and the common interests that we share, for reasons unknown, we play it very low, while many others play it significantly high. So I think now we can collaborate on many dimensions, not just the precision engineering of Germany, which more than that. Because the pragmatic approach Germany brings in to solve problems from the first principles is what is required now. Because we are in a phase where we have to cut the norms of AI and to marry the substance of AI. So for this, I think we, I mean, we all know in this room… Thank you very much. So it might be a moment generally, you know, how we can remain together.

So it’s an opportunity that I see. Thank you.

That’s very diplomatically put. Sindhu, I come to you. You have just inaugurated a huge campus and your ambitions of taking more than 14 ,000, 15 ,000 people. The controversial question that I’d like to put to you is they say that the cost of the charge is four is to one. That is, for one German, you can get four Indians in it. Not only one. Not even one. Srinu, I will write answers. No, of course. The value of the charge is just the opposite, right? So we are looking at a demographic dividend in India, but there is a huge aspect of inclusion, productivity, skilling, reskilling. How does that play into your vision for India?

You started off by talking about the campus, right? And we were talking only about the campus. What’s the charge point of view? I don’t know. I don’t know. I don’t know. I don’t know. I don’t know. campus, right? So that, and I think we just had Chancellor Max coming to campus as well, and looking at India as a constant detachment, it’s clear of my hands. I don’t see it in any of these conversations. Let’s not be, today when we look at, and we mentioned GitHub for pilot, and if I just look at CloudCode, the latest SOPAs that we have, the speed at which you can do things is of course changing, but what it means is experts can focus on high -value SOPAs, right, and high -value domain use cases, and this is the beauty of the pace at which innovation is being, and this is also why companies like SAP or anybody else here in SOPA are invested in India, because they want to build a system of countries and research question is always the right.

So we really want to bring this and the pace at which our people here in India are able to take forward, understand the context, drive forward things, drive that leadership, creation of IC, it’s the change we’re talking about.

Thank you, Sindhu. I have actually two more provocative questions for my other two, but due to the paucity of time, we’ll have to take a break. Because we still have Morley talking about his study, but I think the fact is clear that the opportunity you have in India has been an opportunity like never before. It’s an influential point. It is an opportunity for cooperation and working together in a very, like Mr. said, with shared values, a very clear agenda. And I thank you for being here and for sharing your thoughts. And can we really quickly come and talk about your study, please? because we just have two minutes and we have to close so quickly come Thank you so much.