Driving Indias AI Future Growth Innovation and Impact

20 Feb 2026 12:00h - 13:00h

Driving Indias AI Future Growth Innovation and Impact

Session at a glance

Summary

This discussion focused on India’s artificial intelligence strategy and the unveiling of Dell Technologies’ blueprint for accelerating India’s AI growth, with emphasis on bridging the global AI divide through public-private partnerships. The conversation was hosted by Mridu Bhandari and featured industry leaders including Dr. Vivek Mohindra from Dell Technologies, government officials, and academic experts discussing India’s path to AI leadership.


Dr. Mohindra presented Dell’s AI blueprint built on three core pillars: invest, innovate, and evolve. The investment pillar emphasizes building compute infrastructure, energy systems, and ensuring access for small and medium enterprises. The innovation pillar focuses on comprehensive skilling programs from schools to workforce development, while the evolution pillar addresses governance frameworks and regulatory balance between innovation and responsibility. The blueprint positions sovereign AI development as fundamentally dependent on effective public-private partnerships that marry public resources with private innovation.


Industry representatives highlighted significant challenges and opportunities in India’s AI landscape. A.S. Rajgopal from NextGen Cloud Technologies noted that India currently has 40,000-50,000 GPUs but needs approximately 200,000, requiring substantial investment increases. He advocated for policy interventions like GST waivers on server imports and income tax benefits to reduce infrastructure costs. The discussion revealed that India’s AI workloads are growing at over 30% compound annual growth rate, with compute demand expected to exceed 10 exaflops.


Professor Bhaskar Chakravarti emphasized the critical importance of trust infrastructure beyond technical capabilities, noting that India starts from a strong position with high citizen trust in digital systems and AI adoption. However, he stressed the need for stronger institutional frameworks around data governance, privacy, and security, while highlighting the challenge of implementing union-level policies at the district level across India’s diverse geography.


Minister Jayant Chaudhary outlined the government’s approach to AI democratization, highlighting the India AI mission’s success in providing compute access at 65 rupees per hour through educational institutions. He emphasized India’s strategy of maintaining open innovation while developing appropriate guardrails, positioning the country to leverage its second-mover advantage. The minister stressed the importance of human-centric AI development and the need for verifiable, auditable AI systems with proper citizen awareness and education programs.


Keypoints

Major Discussion Points:

Dell Technologies AI Blueprint Launch: The unveiling of a comprehensive blueprint for India’s AI growth built on three pillars – Invest (in sovereign compute infrastructure and energy), Innovate (through collaboration and workforce development), and Evolve (responsible governance frameworks)


Public-Private Partnership Models: Extensive discussion on how PPP frameworks can accelerate AI infrastructure development, with emphasis on combining public resources with private innovation to achieve sovereign AI capabilities at scale


Infrastructure and Access Challenges: Addressing barriers to AI adoption for startups and MSMEs, including the need for affordable compute access, policy interventions like GST waivers, and distributed data center development across Indian states


Trust and Governance Frameworks: The critical importance of building institutional trust infrastructure, including data privacy, security, transparency, and explainability of AI systems, while balancing innovation speed with regulatory safeguards


Skills Development and Job Impact: Strategies for transitioning from “1 billion users to 1 million developers,” addressing concerns about AI’s impact on employment, and creating inclusive skilling programs that extend beyond metros to tier-2 and tier-3 cities


Overall Purpose:

This discussion was part of an AI summit focused on “bridging the global AI divide” and positioning India as a leader in AI adoption and innovation. The primary goal was to present actionable strategies for scaling AI infrastructure, fostering public-private partnerships, and ensuring inclusive growth while maintaining India’s competitive edge globally. The conversation aimed to translate high-level AI ambitions into practical, executable plans for national-scale implementation.


Overall Tone:

The discussion maintained an optimistic and forward-looking tone throughout, characterized by enthusiasm for India’s AI potential and collaborative problem-solving. Speakers demonstrated confidence in India’s capabilities while acknowledging challenges pragmatically. The tone was professional yet accessible, with participants building on each other’s ideas constructively. There was a consistent emphasis on inclusivity and ensuring AI benefits reach all segments of society, reflecting a responsible approach to technological advancement. The conversation remained solution-oriented, with concrete recommendations and actionable insights rather than abstract theorizing.


Speakers

Speakers from the provided list:


Mridu Bhandari – Senior anchor and consulting editor at Network 18 with brands like CNBC and Forbes India; Host/Moderator


Dr. Vivek Mohindra – Special advisor to the vice chairman and COO of Dell Technologies Global


A. S. Rajgopal – Managing Director and Chief Executive Officer of NextGen Cloud Technologies


Manish Gupta – President and Managing Director of Dell Technologies India


Bhaskar Chakravarti – Dean of Global Business, the Fletcher School of Law and Diplomacy at Tufts University


Shri Jayant Chaudhary Ji – Minister of State for Education and Minister of Skill Development and Entrepreneurship, Independent Charge, Government of India


Additional speakers:


No additional speakers were identified beyond those in the provided speakers names list.


Full session report

This comprehensive discussion at an AI summit focused on bridging the global AI divide through India’s artificial intelligence strategy, featuring the unveiling of Dell Technologies’ blueprint for accelerating India’s AI growth. The conversation, hosted by Mridu Bhandari, brought together prominent industry leaders, government officials, and academic experts to explore India’s path towards AI leadership through strategic public-private partnerships.


Dell Technologies AI Blueprint: A Three-Pillar Framework

Dr. Vivek Mohindra from Dell Technologies presented a comprehensive AI blueprint built upon three foundational pillars designed to position India as a global AI leader. The investment pillar emphasizes building robust compute infrastructure, energy systems, and ensuring equitable access for small and medium enterprises (MSMEs). This pillar recognizes that without substantial infrastructure investment, particularly in energy systems supporting compute-intensive AI workloads, India cannot achieve its AI ambitions.


The innovation pillar centers on comprehensive skilling programs spanning from primary education through workforce development and employer-led training initiatives. This approach encompasses online learning, in-person training, and incubation programs, ensuring AI skills development reaches beyond metropolitan centers to tier-2 and tier-3 cities where significant untapped talent resides.


The evolution pillar addresses governance frameworks that balance innovation with responsibility. Dr. Mohindra emphasized the need for agile regulatory frameworks that can adapt to rapid AI technological advancement while maintaining appropriate safeguards and anchoring development to responsibility.


Infrastructure Challenges and Investment Requirements

A.S. Rajgopal from NextGen Cloud Technologies highlighted significant infrastructure gaps, noting that while India currently possesses approximately 40,000-50,000 GPUs, the country requires around 200,000 GPUs to meet its AI compute demands. India currently invests roughly one-hundredth of what the United States invests in AI infrastructure, according to Rajgopal’s estimates. AI workloads in India are expanding at over 30% compound annual growth rate, with compute demand expected to exceed 10 exaflops.


To address these challenges, Rajgopal proposed several policy interventions including GST waivers on server imports, which could reduce upfront infrastructure costs by approximately 18%, and income tax benefits for Indian AI service providers. He outlined plans for deploying 100 megawatts of data center capacity across six states, demonstrating a distributed approach that brings compute capacity closer to users and ensures AI benefits reach regional populations.


Rajgopal emphasized the importance of open-source technologies in reducing AI implementation costs, making advanced capabilities accessible to broader populations and supporting India’s digital inclusion philosophy.


Trust Infrastructure and Institutional Frameworks

Professor Bhaskar Chakravarti introduced the concept of “trust infrastructure” as equally important as technical infrastructure. Drawing from research across 125 countries studying digital evolution, Chakravarti argued that trust is the single most important determinant of whether a country maintains momentum in digital growth. Trust infrastructure encompasses institutional safeguards, transparency mechanisms, explainability of AI systems, and effective grievance redressal systems.


India begins from a position of strength, with citizens demonstrating higher levels of trust in digital systems and AI adoption compared to many developed countries. However, Chakravarti noted that while citizen trust is high, institutional trust infrastructure requires significant development, particularly given India’s federal structure where union-level policies must be implemented at the district level.


The professor highlighted AI’s potential impact on employment as a critical concern that could undermine other AI initiatives if not addressed proactively. He noted that trust requirements vary by application – education systems may require different trust mechanisms than healthcare applications.


Government Leadership and Public-Private Partnerships

Minister Jayant Chaudhary outlined the government’s approach to AI democratization, highlighting the India AI mission’s success in exceeding its initial target of 18,000 GPUs, reaching 38,000 GPUs with plans to surpass one lakh (100,000) by year-end. These compute facilities are made available to startups and researchers at just 65 rupees per hour – compared to 300 rupees for a couple of hours at a PVR cinema, as the Minister noted.


The Minister emphasized that effective public-private partnerships should prioritize people as the most important ‘P’ in the partnership equation. This people-centric approach is reflected in housing compute facilities within educational institutions, ensuring real research and development occurs in academic settings while maintaining ecosystem accessibility. He cited Sarvam being incubated by IIT Madras as an example of successful public-private partnership.


The government’s regulatory philosophy reflects a pragmatic approach prioritizing innovation while building appropriate safeguards. The Minister discussed implementing “zero trust” architecture for AI systems, emphasizing verification protocols at every level from data collection through model training to output delivery.


Skills Development and Workforce Transformation

The discussion revealed consensus that India must transition from being primarily a consumer of AI technology to becoming a creator and innovator. Manish Gupta from Dell Technologies India articulated this as moving “from 1 billion users to 1 million or 10 million developers.” This transformation requires comprehensive skills development delivered through multiple mediums – online platforms, in-person training, and incubation programs.


The partnership between Dell Technologies and the Ministry of Skill Development aims to establish AI apprenticeships and future skill labs extending beyond traditional technology hubs to tier-2 and tier-3 cities where significant talent exists but access to AI training remains limited.


Practical Applications and Strategic Vision

The discussion highlighted India’s focus on moving beyond consumer-focused generative AI applications to practical governance and enterprise solutions. Rajgopal provided examples including the Election Commission’s photograph deduplication project and plans for comprehensive AI stacks in education using anonymized datasets to enable customized learning experiences.


An interesting observation emerged regarding adoption patterns: government agencies and startups are leading AI adoption, while large enterprises struggle to move beyond experimental phases. The government’s leadership in AI adoption provides concrete examples of AI’s transformative potential for governance and citizen services.


Manish Gupta introduced the concept of creating a “UPI of AI” – a unified infrastructure layer similar to India’s successful digital payments platform that would democratize AI access through consistent APIs, allowing organizations of all sizes to access AI capabilities without individual infrastructure investments.


Economic Implications and Future Directions

The blueprint positions AI development as fundamental to India’s Viksit Bharat 2047 vision, with AI expected to drive significant GDP growth while creating new employment categories. The strategic autonomy dimension addresses India’s need to develop domestic AI capabilities rather than remaining dependent on foreign technologies, emphasizing the transition from “Made in India” to “Trusted in India.”


The discussion revealed sophisticated understanding of AI development challenges and opportunities, with remarkable consensus across government, industry, and academic perspectives. The emphasis on public-private partnerships, trust infrastructure, and inclusive development reflects a mature approach that goes beyond purely technical considerations.


Key Challenges and Path Forward

Despite comprehensive planning, significant challenges remain, including the substantial funding gap for AI infrastructure and the need for innovative financing mechanisms. The employment impact of AI requires concrete strategies for managing transition and ensuring AI creates more opportunities than it eliminates, particularly critical given India’s demographic profile.


Success will depend on maintaining collaborative approaches demonstrated in this discussion, with sustained commitment to infrastructure investment, skills development, and institutional capacity building. The blueprint provides a roadmap for ensuring India’s AI development serves both national competitiveness and inclusive growth objectives, positioning the country not just to adopt AI technologies but to lead in developing AI solutions that address real-world challenges at population scale.


Session transcript

Mridu Bhandari

So this conversation here and the couple of conversations we are going to have over the next one hour or so are aligned with the summit’s goal of bridging the global AI divide. So AI drives economic growth, social empowerment, and of course, global leadership for India. This is not just a presentation, it is a call to action. I’m your host, Mridu Bhandari, senior anchor and consulting editor at Network 18 with brands like CNBC and Forbes India, and I’ll be guiding you through this next 55 -minute journey that we’re on. To set the tone of this morning, we’re going to begin with framing the execution pathway of AI adoption and scaling it up from an industry vantage point.

Our leadership keynote theme today is architecting India’s AI leadership, a blueprint for transformation. To deliver this knowledge, we’re going to be talking about the key points of AI adoption and scaling it up from an Please join me in welcoming on stage Dr. Vivek Mohindra, special advisor to the vice chairman and COO of Dell Technologies Global. Dr. Mohindra, please join us here.

Dr. Vivek Mohindra

Thank you, Mridu, and thank you, everyone, for joining us for the unveiling of this important blueprint. As we have heard over the most of this week, India is at the cusp of very significant changes and progress on the back of AI with very bold aspirations, which are not only bold for India, but they’re very bold when you put it in the context of global aspirations that lots of other countries have. Dell has had a presence in India for over 30 years. We have partnered very closely with several government agencies as well as companies and the broader ecosystem to bring the broader set of capabilities that we have, which are across the board. We have a board covering server storage, networking, PCs.

and we are the number one AI infrastructure provider to enterprises globally. So leveraging our global presence and leveraging our deep knowledge of India, we have put all of that thought into putting forth, as Milu described, an AI blueprint, which is a practical guide for what we think not only the country but also companies need to do to be able to take advantage of this particular opportunity. The growth in terms of compute expected on the back of AI in India is expected to be well greater than 10 exo -flops, and that is a significant amount of growth. And the AI workloads in India are growing at over 30 % compound annual growth rate over the next few years, which is extremely significant.

So as we step back and look at what are the key elements of what a country and companies need to do, there really are three key elements. The first element is investments. And the investment really goes at the heart of the compute infrastructure that a country needs to put in place to ensure that everybody has access to that infrastructure, including MSMEs who sometimes do not have the capacity to be able to put their infrastructure in place. Investment also includes energy infrastructure, because without energy, there is really no compute infrastructure you can put in place which can run on that. So those are some of the key areas of the invest pillar of that. And there are other several other areas that I will encourage you to read through our blueprint that you will see both from a policy perspective and practical perspective that we think needs to get done.

The innovate side really comes down to. Areas like skilling, which I know when Minister Chaudhry joins us, we will get into that in quite some detail. But innovating around how the skilling occurs all the way from schools to colleges to workforce entering employment and employers themselves, what role they play across a whole spectrum of mediums to deliver that skilling is a key part of the innovate pillar. And then the last one, evolve, revolves all around governance aspects of that. And governance covers multiple areas. One of the key areas within governance is fundamentally the regulatory framework that needs to exist and that countries need to put in place. The pace of change is so significant with AI and how rapidly the technologies are moving that one of the fundamental balances that countries need to strike vis -a -vis, regulations, is striking the balance between innovation and responsibility while anchoring it to responsibility.

That is one of the key. regulatory principles that needs to be in place. And the regulations have to be agile because the technology is moving at such a fast pace that you cannot anchor the regulatory framework to yesterday’s technologies. And at the heart of it, I hope what you will take away from our blueprint is realizing sovereign AI potential for any country, including India, is really about the public -private partnership. And it’s really about marrying the public resources with private innovation. And that really is what the key to unlocking the full potential of AI and sovereign AI is in this country. So, again, I would encourage you to read through the blueprint, and we look forward to your feedback, and we look forward to partnering closely with Indian ecosystem to help India realize its aspirations with AI.

Thank you very much.

Mridu Bhandari

Thank you so much, Dr. Mohindra. I’m going to request you to please stay back on stage. I’d also like to invite Manish Gupta, President and Managing Director of Dell Technologies India, to join us here. This is the big moment, ladies and gentlemen. We are ready for the unveiling of the Dell Technologies Blueprint to accelerate India’s AI growth. Yes, that’s a photo moment for everyone. Thank you. Thank you so much, gentlemen. Thank you, Dr. Mohindra. Thank you, Mr. Gupta. Well, this blueprint advances India’s vision of Vixit Bharat 2047, positioning AI as a foundational engine for productivity, modernized public services, opportunity expansion and strategic autonomy. It centers on three pillars that we’ve been discussing. Invest, invest in sovereign, scalable compute and data foundations, innovate with collaboration and with a future ready workforce and evolve, evolve into a responsible, agile, security first governance structure.

So our next panel today will go inside this blueprint and India’s AI future to unpack how to convert this ambition. into nation scale execution. And that’s quite a mean feat for a country as diverse and as huge as India. So let’s welcome the panelists for all the tough questions this morning. Raj Gopal, AS, Managing Director and Chief Executive Officer of NextGen Cloud Technologies. Bhaskar Chakravarti, Dean of Global Business, the Fletcher School of Law and Diplomacy at Tufts University. Please have a seat, sir. And once again on stage, Manish Gupta, President and Managing Director of Dell Technologies India. And I will be moderating this session for you. Welcome, gentlemen. We are here this morning to really translate the invest, innovate and evolve pillars into very actionable steps that we all can take together to grow India’s AI ecosystem.

So I’ll begin with targeted questions to each of you. And of course, you are free to jump in to add thoughts to each other. It’s a candid, free -flowing conversation. Mr. Rajgopal, if I can start with you. So startups and MSMEs, they are the engine of our economy. They are also the engine of our innovation, especially as far as AI is concerned. But access to very reliable, affordable AI compute and cloud at scale continues to be a barrier for many of the small and medium enterprises. Now, in your opinion, what are some of the policies, some of the infrastructure, some of the market interventions that we need today to really unlock this access at scale?

A. S. Rajgopal:

Yeah, actually, I think across many other countries that we have seen, India has got a much comprehensive approach to this. I mean, in terms of actually they started this India mission, which is across seven pillars. I actually don’t see many startups actually using the facilities that are there in the sense that you could, you know, you apply for GPU infrastructure and you would get it at a subsidized rate. And some of them even got 100 percent of the GPUs that they need. So I think from India’s side, we’ve got a little less number of GPUs compared to what we really need. Maybe we need about 200 ,000 GPUs now, and we have about 40 ,000 to 50 ,000 now.

So we all need to really invest more and then deploy more. But the most important thing that you should see is that there is a good ecosystem. There is a system policy available for MSMEs and startups to leverage this. So there are a lot of innovative AI solutions being built. Most importantly, I also see that government actually setting pace in terms of actually leveraging some of these. We ourselves, I mean, we did one job for government, which is very, very unique. Like we serve Election Commission of India. They came to us and said, can you deduplicate and look at all the photographs that we have? This is like, you know, 90 -year -old pictures, right?

I mean, so. So humanly it was not possible to deduplicate. You can’t check one photograph with 90 crore others. We did that in a matter of 51 hours, and then we responded to them as to whether they had complications and all that.

Mridu Bhandari

Wow, that deserves some applause. That’s a humongous task.

A. S. Rajgopal:

So what I see in this country is that I don’t think we will be pure play, this chatbot, I mean, the generative AI, the way it has been envisaged, I mean, will be the primary use case. I think we are going far beyond what AI can be applied in terms of actually improving the productivity of citizen services and also give use cases that these small and medium enterprises can actually use. In terms of actually enabling more GPUs, I think we have, you see, we need a lot of money. I mean, we are investing about one hundredth of what U.S. is investing or even less. so for us to do more I think we need to remove certain bottlenecks that are there one of the things I believe that can be done is I’m sure everybody is familiar we all pay GST but basically when we import servers when we pay GST on it and then when we deliver service I get that as an input and we do the I mean we only pay the value -added piece back to the government but the government gets the GST either way and I get an input so the way to one thing that we could look at is whether we can wave off the GST up front and just take the GST when the services are being delivered what that would do is it will deliver it will reduce my upfront infrastructure cost to by about you know you know 18 percent you know I don’t have to fund that up front and then pay interest on it you know or raise equity and you know pay more expectation on that I mean these things could really help I mean there are some of these things that government should look at and I think that’s a good point.

Thank you. The last point is that they’ve given tax holiday for delivering services to the world, but I think India has got a lot more to do within India than just look at world market. And I believe that we should get, Indian service providers should get the same benefits as the global providers would get when they host services in India. So maybe a GST waiver and some income tax benefits could be good.

Mridu Bhandari

Okay. GST waiver, income tax benefits, demands from the industry coming in. Professor Bhaskar, if I can come to you now. You’ve often argued that nations need to compete not just on technology, but on trust, on institutional strength, and of course, very, very inclusive digital participation. And that’s very critical for a country like India because we are a country of many countries and the rich -poor divide is quite huge. There are a lot of bridges. to gap here. There are a lot of gaps to bridge here. My apologies. Now, as India accelerates AI, what do you think are some of the biggest non -technical bottlenecks that we should be looking at addressing, which you believe could really, you know, limit this momentum that we are on currently?

And if we need larger societal good, what are some of the non -technical barriers that we need to immediately resolve?

Bhaskar Chakravarti

Yeah, so thank you. Thank you for the question, and thank you for the invitation to join this terrific panel. I think the issue of what are the non -technical elements, it’s great that you have included that question in this discussion, because in all the excitement around the technical infrastructure, which is, of course, enormous that’s happening right here in India, it’s no secret that, you know, this is one of the biggest talent pools. In the world, growing very, very fast. In two years, one in three developers are going to be in India, the largest mobile data pool anywhere in the world, the third largest data pool, you know, anywhere in the world once you take mobile and everything else together.

Growth in compute, growth in energy, growth in workloads, all that is happening, you know, which is fantastic. Now, when you think about what are the other elements that drive demand, what we have found, we study 125 countries and try to understand the role of technology in shaping lives and livelihoods across 145 countries. The single most important determinant of what keeps a country on trajectory in terms of both the momentum of growth and also the state of their digital evolution is the demand side. So when I think about the demand side, obviously the core infrastructure, which has been talked about, is enormously important and is going to continue. So the demand side is going to continue to be a major contributor.

A second part, which has been talked about a lot in the Indian context, is the distribution infrastructure. With DPI and all the different platforms associated with it, we know that there’s a very powerful distribution system. Now, there’s a third infrastructure, which is the non -technical part, and that is what I would call the trust infrastructure. Now, when you think about trust, it’s a bit of a slippery concept. It’s very hard to define. Each one of us in our heads has an idea of what trust is. But if you force somebody to define it, we’ll struggle. The best thing about trust is I know what trust is when it is not there, when it is missing.

And then you have to ask the question from a human perspective, what really is trust? And how do I bake that into the policy systems, into the technical systems, into the marketing systems, into the narrative around the India ecosystem, which will then keep moving the system. And trust ultimately has to do with the the Do people have confidence, the people who are the grantors of trust, do they have confidence that this invisible transaction that I’m engaging in, whether I’m putting my data into a system or whether I have entered my financial information and I’m expecting something on the other side, that this whole thing is going to be completed and it’s going to be completed in a way that is reliable, that is repeatable and will not take advantage of me.

So when you think about that whole trust ecosystem, India starts in a great place. Relative to where I come from, the United States, India is a far more trusting country in terms of trust in digital systems overall and certainly in terms of AI. There’s a tremendous level of enthusiasm in terms of embracing all things AI. And we’ve seen this right here, just the sheer numbers of people who’ve attended the conference. It shows a level of trust that is probably unmatched anywhere in the world. Now, this is a tremendous asset. That’s it to start with. The challenge is that the institutional side of trust is still in the development process in India. So if you think about data governance, privacy, security, we are making progress, but we need to be much further along.

Other aspects of trust has to do with the fact that, say, the India AI mission, that is developed at the union level, at the center level. But the actual exercising of trust, the granting of trust happens at the district level. And the district varies depending on whether I’m in Telangana or I’m in Jharkhand. And at the union level, the principles that I’ve got in place need to be sensitive to how it’s being experienced from the ground up. So there are many different facets of trust that we need to work on and put in place, including transparency, which AI is, you know, we are facing a challenge regarding transparency across the world. So this is an issue.

And then, you know, an approach to having redress and grievance systems and then literacy. You know, people need to be able to understand how to use this exciting technology and also protect themselves. I’ll pause.

Mridu Bhandari

Absolutely. Thank you for that wonderful perspective, Professor. Coming to you, Manish. Now, the Dell Technologies blueprint really calls for tighter alignment between policymakers, between industry, academia, institutional capacity. How can frameworks like this one really ensure growth that is both globally competitive for India, but also locally inclusive? Because there is a lot of regional growth. There is a lot of, you know, geography that needs to be taken into the fold of AI when we are talking about being globally competitive as well.

Manish Gupta

Thank you for that question. And, you know, just before I go in there, I would just add or maybe, you know, speak on a couple of topics. That professor just spoke about your trust, while he spoke more from a non -technical standpoint, you also brought in a technical aspect to it, you know, around the entire governance. And it’s not just non -technical in today’s world. It’s driven by data privacy and all of the things around. But equally on literacy, there’s also explainability. You know, that trust comes really inherently once you have got explainability and people are aware on what outcomes are coming. There’s the is that explainable and do they understand that? Right. So it’s a very, very interesting world that we are in.

Now, back to the blueprint that we were talking about and Vivek articulated that beautifully well across the three pillars of invest, invest in in data centric capacity, invest in energy infrastructure, invest in people, which goes back into the innovate side of it. Because like we like like we just discussed, we’ve possibly got the largest pool of engineers around AI. And that’s that’s killing on innovate part is what’s going to differentiate us, what’s going to make it really real and practically doable within the industry and would differentiate us versus other nations equally make it make it make it more democratized and ubiquitous across the nation. And lastly, it’s really about how do you continue to build in the guardrails?

How do you? Build the trust like we just discussed to ensure that there is that. the ecosystem knows that this entire process can be trusted and can be built upon. We’ve also got to remember the sustainability aspect of it, you know, and which is where, as you look at the blueprint, you will see us talk about the fact that energy efficiency, sustainability on data centers and new architectural models are becoming super important. And that’s something that NextGen under Raj Kapoor’s leadership has demonstrated in building highly sustainable, more energy efficient data centers that will allow us to use our energy resources in the best possible manner while democratizing the access of compute capacity, while democratizing the access of data center capacity to organizations and verticals of various sizes.

So that’s going to be the critical pillars around which we really believe that there’s practicality in adopting and differentiating ourselves on the AI arena. You know, as we go forward. Right.

Mridu Bhandari

I’m going to pick up on that data center piece and come to you, Rajgopal. Now, given the scale that India will really need for competing globally, what would it take to truly build sovereign, cost -efficient AI infrastructure that’s not just available to large enterprises, but is also very, very affordable for the long tail of innovators that we have in this country?

A. S. Rajgopal:

Yeah, if you see the data center industry, it’s been pretty concentrated in Mumbai and a little bit in Chennai. And, you know, the other markets didn’t really take off as much as they should have. So what we are trying to do is to have, I mean, our current plan is to put about 100 megawatts of data centers in about six states. And what I see is that going forward, I mean, this could be the model that can be built on where each state has got a capacity. Because these states itself have got so much of consumption that can happen primarily because. if you start looking at applying AI into elevating the quality of education in India, which will be one of the first things that will get rolled out at scale in India, and also the healthcare aspects of it and citizen services, these things can require lots of computing capacity.

So we are working with few state governments to actually see if we can bring a total transformation, you know, actually consolidate their applications, bring about a data lake, and then apply data to it, sorry, intelligence to it, and take it to masses. So the way I think it will evolve is that there will be many more regions where data centers would spring up, and then when they’re distributed, they need to be interconnected. We have very good interconnect system, and not just the telcos, but you have things like, you know, we have railway networks, we have power networks which can actually assist the good connectivity between them. When these things come into play, actually, we can have a pretty distributed, a good amount of compute in India that can actually serve this aspect.

But you must be aware that, you know, this game is actually, I mean, if you see my context, I mean, I have four diamonds to work on. One, of course, is geopolitics. I mean, there is quite a lot that is happening. We need to ensure that we have access to the technologies that we want to bring to India. So that India actually works on the best available infrastructure. It is slightly better now, but, you know, there were restrictions before. The other aspect is the amount of money. I mean, so I think it requires multiple billion dollars of investments to do. And that should be facilitated. That should really come into the country. When we have the money piece sorted out and then we build the infrastructure, a very good, you know, a very good, you know, a very good, you know, a very good, you know, a very good, you know, a very good, you know, a very good, you know, a very good, you know, a very good, you know, But the good thing that we can leverage is open source.

I mean, when we leverage open source, we can actually combine the infrastructure and open source and bring down the cost of compute so much that it is actually palatable for the Indian citizens to use because it’s not about serving this 2 percent, 3 percent of the population which pays the income tax. It’s about serving the 90 percent others. The moment we succeed in doing this, I think the talent also, the good talent that we have, we have access to good talent in India, but we don’t have good talent. The quantity is missing. I mean, so we have good people, but, you know, you need many more good people. They’re going all over the world. We want to bring them back.

These people can come back when this money and infrastructure fall in place. Right. That would ensure that India is playing a role which is actually pretty balanced, leveraging the global technologies and leveraging local talent and actually setting the standard for the future. It’s a blueprint for all the other countries which don’t have. which may miss out this revolution and become digital colonies of the top two countries that are investing heavily in AI. So I personally believe we have a good blueprint, and the blueprint can be applied to multiple countries, and we are well on the path. And I would prefer a distributed development of data centers across the country so that we are closer to the users and we

Mridu Bhandari

Absolutely. Absolutely. Well, Professor, coming to you next, studies on digital competitiveness have consistently shown that institutional capacity often determines whether technology adoption really translates into economic value or not. Now, in the Indian context, what do we really need to do to really strengthen institutions and to really build the institutional muscle here? That ensures that AI drives very, very inclusive growth rather than deepening the tech divide, because there are already many divides that we are battling with.

Bhaskar Chakravarti

and I can then end up with a solution to the problem. So the same thing for skill building, literacy. If I can see my ability to speak, my ability to read in multiple languages improve, you know, suddenly my trust goes up. So what is the minimum amount of institutional safeguards I need to provide that? Then I come to something like health care. When people have a much bigger chasm to cross, that’s where people have a lot of concerns about, you know, should I be putting my information into the system? How is it going to be used? Can I trust the phone where I’ve relied on a doctor or relied on, you know, a wise person in my community?

I’ve relied on my mother, you know, for maternal health care advice. So how do I cross that chasm? You know, being able to provide the foundational trust elements is going to be important. So the answer to your question, the long answer is it depends. It depends on the user. As is the case to a lot of questions about India, it depends, right?

Mridu Bhandari

Well, Manish, you know, globally, we are seeing nations tie AI strategy to strategic autonomy. Now, whether it comes to the semiconductor ecosystem or the supply chain, you know, the strategic autonomy is becoming extremely important for countries. For India, what are the two or three foundational capabilities that you think we need to build domestically in this decade to ensure that we are true creators of AI value and not just consumers?

Manish Gupta

Awesome. Great question, Midu. And, you know, we as a nation have proven ourselves to be phenomenal adopters of technology. You know, and the best example in my mind comes on UPI or digital payment. Ten years back, 11 years back, we were just not there. and today we are by far the largest consumers or the number of digital payments and the value of digital payments within India is multiple times of the second economy that does this, right? So that’s a great example of how we have been able to localize, democratize and proliferate the use of technology. So within that, I would really put on three hats here. The first and just, you know, inverting the pyramids, not starting with technology but starting with people.

We have really got to think away from the users to the developers. You know, it’s got to move from 1 billion users to 1 billion or 1 million or 10 million developers and that’s the skill set, that’s the IP that we are going to bring in because we’ve got that largest talent pool residing within the country. The second, and again, I heard you talk about semiconductor and supply chain. I think we have got to adopt the best that’s available. They’re globally. But equally, we’ve got to move, we’ve got to not just think about made in India. but talk about trusted in India and which is where work with organizations such as AISI, Artificial Intelligence Safety Institute to ensure that we are putting the right guardrails, putting the right governance policy and the entire institutional framework to ensure that the AI that we are building here is trusted.

And lastly, goes back to the same thing. And maybe I’ll use the same example. You know, we had the UPI of money. We need to have UPI of AI. Where we are, we are building that at scale using the data sources that we have. We have the largest ones and some of the initiatives that the government has taken, India AI mission. But equally think about AI Kosh. There are more than 7000 data sets that are now available to organizations of all sizes. Use that to ensure that we are developing for the country at the population scale through academia, through private sector, through startups, through MSMEs, all coming together. And that really represents. Be quiet. a consistent API layer that’s bringing theoretically, maybe even all of the data center and the compute capacity that we are creating as a part of AI mission to be one single layer that can be consumed by anybody and everybody across the nation to start to innovate on that, to start to develop on that.

So going back, I know a long answer, but if I were to summarize three things there, UPI of money to UPI of AI. You know, made in India, transitioning to trusted in India. And, you know, from a developer users to from a billion users to maybe a million or 10 million developers.

Mridu Bhandari

Made in India, but made for the world.

Manish Gupta

Absolutely.

Mridu Bhandari

All right. So I’m going to ask each one of you for a few concise takeaways today. Now, the blueprint that we are talking about that, you know, Dell Technologies has just unveiled talks about agile, trusted AI governance with sectoral baselines, testbeds, strong institutional coordination. Yet globalization, only what we’ve seen is that speed often beats cost. caution. And we are seeing, you know, some of the scary stuff coming out with AI as well. A lot of the experiments, a lot of, you know, agentic AI experiments that people are doing across the world, some of them call for caution. Now, in the Indian context, how do we stay globally competitive while also operationalizing very, very stringent safeguards?

And where should we really draw the line between the speed of innovation or innovation velocity and regulatory discipline? So, Rajgopal, if we can start with you,

A. S. Rajgopal:

please. If you take the birth of Gen AI, I mean, it actually, I don’t think none of those rules were actually followed. And, you know, it was built on every data that was available in whatever form. Personally, you know, in most places, I’m actually trying to tell people, just it’s not about ignoring the risk factors of it, but I think the regulation should not curtail the innovation in the thinking that you know we should be restrictive about whatever we are working with and so one of the most important things is i think we should have less regulation in this place because i think overall the benefit you should look at ai like a utility i mean you will have more good with ai than with bad yes there are things that can be handled as we go along if you see what we do in cloud today we haven’t been able to sort out our security and you know data protection postures even today it’s an evolving journey and i think we will continuously catch up with the bad factors around the ai adoption and and that’s a journey it cannot start or stop or it can be implemented at a point in time so we should keep looking at those aspects and keep putting whether regulations or technology interventions to ensure that we handle the problem and we should keep looking at those aspects and keep putting whether regulations those pieces but we should go fast forward with implementation and adoption of And I see a lot of Indian enterprises really being reluctant in terms of adopting it, especially the larger ones.

But if you see in India, I think government will set the pace and the startups and the MSMEs will actually catch on from there. And the large enterprises will actually struggle to catch up with the amount of innovation that’s happening in these

Mridu Bhandari

Where does that reluctance come from? Like, what are the top three reasons large organizations are reluctant? One is, of course, the fact that it’s not easy to adopt and transform a large organization. And perhaps startups and SMEs have the benefit of the agility and the small scale that they’re at. What else?

A. S. Rajgopal:

So I think the first issue is not about security and all that. I mean, about these aspects. But most importantly, I think a lot of people are struggling to imagine where to apply AI. And I think the moment we can understand that, you will start seeing that. that the benefits far outweigh the negative aspects of it. So I think that’s the first thing that people should look at, is to not just look at leveraging the Gen AI in its chatbot form, but actually really look at where you can deploy. I talked about that deduplication piece. We are working on more than 150 projects, and not all of them are bot -based. So that imagination is what is important, and once that imagination comes, the benefits will outweigh the negative aspects of whatever we

Mridu Bhandari

All right. Professor, final takeaway from you on speed versus caution.

Bhaskar Chakravarti

Yes, so if you think about speed, I always like to use the analogy of a car and a road. you can think about the speed that you can build into the car, the velocity of the Ferrari. And a lot of the conversations that have happened, not necessarily in this room, but in other rooms, is about the Ferraris, whether I’m talking about agentic AI or AI optimized for certain applications and the technical aspects, you know, really, really important. Now, if you take the Ferrari and you bring it into the Indian context, maybe it’s a Maruti or something else that I need to be talking about. But then the question is, what’s the road on which this Ferrari is going?

If it’s a dirt road full of potholes, even a Ferrari is not going to go very fast. So much of our conversation here is about that dirt road. And what are the things, what are the potholes that we need to fix? There’s one elephant on the table that we did not address, and I’m just going to leave it at that, which is when we talk about trust, there’s a whole bunch of things you can do from an institutional standpoint to build trust, transparency, explainability, and so on. There’s a huge issue that we need to think about, which is what is going to be the impact on jobs? What’s going to be the impact on jobs?

This is the youngest major country in the world. It’s also one of the least employed country in the world. And now with AI coming in, is that going to help boost jobs or is it going to take jobs away? If we don’t fix that problem, get ahead of that, all the trust we are talking about, all the institutions you build could come down. So part of the policy infrastructure here is to figure out what is the post AI jobs picture.

Mridu Bhandari

Absolutely. Manish, final word to you.

Manish Gupta

So, you know, I honestly don’t think that these are opposing forces. Agility versus security. And, you know, particularly in this in this side of technology, you cannot have them act as opposing. It’s really about building the frameworks that are going to take both of them together. This is fast evolving as a technology, but equally as as as institutions will have to be faster than that in evolving. I think the government has done a phenomenal job in building some of the frameworks around that, you know, and the and the institutions, the ASI as one example. on the privacy side, DPDP or DEPA, all of those acts being there, are good frameworks to start with. And I’ll just index back on the question that you had asked Raj Gopal earlier on what is the hesitation from enterprises in adopting.

I don’t think it’s necessarily about security. You know, it’s really about saying how many of those have real use cases? While the real use cases exist, how many of them are able to monetize or are able to see them scale from experimentation or pilots into production? And I think that’s a job that we as industry folks who understand the technology, who are innovating in this space, really need to bring to the table so that we can bring this to the fore across the nation and enterprises and organizations of all sizes and academia and public. I think that’s where this will get practical, but equally these are not opposing

Mridu Bhandari

Right. Well, thank you, gentlemen, for that absolutely incredible conversation. You know, the takeaway is clear that investing, innovating and, of course, innovating by expanding skills pipelines and accelerating AI deployment is going to be key to India’s sovereign AI infrastructure. And appreciate you joining us here and taking the time today. We are also very delighted to now be joined by Honorable Shri Jayant Chaudhary Ji, Minister of State for Education and Minister of Skill Development. Huge round of applause. We are going to have him up here shortly. Thank you, gentlemen. Thank you very much for joining us. So if we can have you up here for a quick photo op and we will then continue the conversation.

Thank you. Thank you. Thank you, everyone. Thank you. Thank you, gentlemen. Manish, if I can please request you to felicitate our speakers. Mr. Rajgopal. Let’s have a huge round of applause for our panelists here today. Professor Bhaskar Chakravarti. thank you so much for joining us here today if you all can just get off the stage for two minutes we are getting it ready for our next conversation thank you so much well ladies and gentlemen time to move on now if India’s AI ambition is to translate into real economic growth it’s obviously not going to be any one entity’s job it is not going to be driven only by the government or by the industry alone it will be driven by partnership India has the talent the digital backbone and the momentum the real question though is how do we scale AI responsibly securely and inclusively so our next fireside chat conversation will explore what is the role of AI in the development of the future what a powerful public -private model regarding AI could really look like.

And for this, I’m delighted to welcome two very eminent leaders who are instrumental in shaping the journey, both from policy and industry perspectives. We have, of course, Honorable Shri Jayant Chaudhary Ji, Minister of State for Education and Minister of Skill Development and Entrepreneurship, Independent Charge, Government of India. And we have Dr. Vivek Mohindra, Special Advisor to the Vice Chairman and COO, Dell Technologies Global. If I can please have both of you up here for a quick conversation. Thank you so much. Thank you very much. Thank you, gentlemen. Well, it’s quite clear that public -private partnership is going to be critical to AI scaling and adoption in India. You know, Chaudhary, if I can start with you, how can PPP models really, how can PPP accelerate?

large scale AI infrastructure? What have been some of the on ground experiences you’ve seen so far? And of course, the government has been moving at breakneck speed when it comes to deploying more technology, sort of, you know, giving a more fillip to innovation in India. How are you really ensuring trust, resilience and long term national competitiveness as AI becomes very mainstream with this event in India?

Shri Jayant Chaudhary Ji

In the Indian context, as the audience is aware, we had a lot of catching up to do. And it’s fair to say that a lot of what we are seeing around us in AI has been facilitated by creating an ecosystem in a short span of time. Perhaps we may enjoy a second mover advantage with regards to this technology. And that has come about only because of a strong top down emphasis and push. the only reason why this event is happening here in India is because the leadership at the top understood very quickly the value and the potential of this new technology that we should not view it as a disruption but view it as an opportunity to leapfrog legacy problems deficit problems and provide access and equity to our citizens and dignity to our workers and that’s why prime minister you know the last event in France shared that leadership space every opportunity he gets he talks about skilling about young people about the potential for AI and the enunciation in manner of that this technology needs to be human -centric I think that has given us a real emphasis and a push for the academia for our industry for our vibrant startups systems you know to really think about what they are doing in this space I think that is the background to the event that we are all witnessing.

Thousands and thousands of people, casual visitors, apart from those who are already entrenched in technology. And the message that goes out is that one billion strong young people in a developing country are already thinking about what AI means to them and what they can do in this space. Not just be consumers, but also be producers and innovators and thinkers and creators. Now, PPP in this domain for me, and when you think about Manav being human centric, citizen centric, the P that really matters is the people. And in that context, it’s important that you have the broad architecture which is open. This is something that India has stood for from the beginning. When there was a lot of debate about what should be the policy that enables AI.

But there was also a lot of fear around AI about trust factors, about privacy, data, sovereignty, multiple issues about the human interface, the augmented human worker, what this means for education, for the future of jobs. A lot of those issues were being discussed and debated. And India said that, yes, it’s good to have a strategy. And out of that strategy and experiences will evolve a robust policy. It is essential to have guardrails. But it is a starting point. Currently, we don’t want to infringe upon the possibility of innovation. And India took that approach. And we had open access to whatever compute, you know, India AI mission was set up with a target of 18 ,000 GPUs.

And in a short span, they’ve surpassed it. It’s about 38 ,000. And a roadmap is by the end of this year, it’s going to cross one lakh, threefold. now think about it all of this compute facility that has been created is a model of ppp it has to be housed in educational institutions so that real research can happen in our premier educational institutions this is a great time when academia is more important than it perhaps ever was in the indian context in the indian context academia was partly separated from industry and the real economy in our minds but now every indian citizen is realizing the value of research and innovation every family is saying that no this is important we must value it and every educational institution is saying that we are not divorced from the market and needs of our community and society and nationhood building so that engagement with nationhood building and concept of technology is deeply immersed thanks to the efforts of india ai mission and here i’ll just you know leave one data point to you what is the cost of this compute facility it’s being provided for startups, for researchers at 65 rupees for an hour.

You pay 300 rupees for a couple of hours PVR cinema ticket. So it’s probably the world’s cheapest compute facility which is open. We are celebrating Sarvam. Let’s not focus on for profit not for profit. Because everything has to be for people. If you look at Sarvam, that’s also in my mind a PPP. It’s been incubated by IIT Madras. It has been supported by AI mission. So that’s another example because you’re right. Government cannot invest in from data to energy to compute to innovation. It really has to come from our citizens, our researchers, our technologists. It’s a collective mission.

Mridu Bhandari

Absolutely. Well, Dr. Mohindra getting your point of view from perspective now, if we look at PPP as far as job enablement is concerned, because that’s the big concern that citizens have that what’s going to happen to our jobs. And of course, skilling is part of that journey. How can Dell partner with, you know, Ministry of Skill Development and Entrepreneurship? What are you all doing from a future skill labs perspective? How are you accelerating AI apprenticeships, so that basically the jobs also move beyond the metros, because tier two, tier three towns is where, you know, we are looking at a lot of talent sitting there, but we are looking at a lack of access of sorts when it comes to skilling?

Dr. Vivek Mohindra

Yeah, I think that’s a great question. And I think, Honorable Minister, good to see you again. It reminds me of a discussion we had back in last time we met in October 2024. I know we missed each other and often. And we covered very similar ground. I think at the heart of it, it does come down to, and I commend India on the progress it’s made in making access to all these GPUs available. It is an industry -academia partnership, working closely with the minister here. And our view is when you think about skilling, there are three different levels. You have to think about schooling. You have to think about college level. And you have to think about employment.

People entering the workforce are employed today. And you think about delivery of this through online, in -person, incubation. So those are the two big dimensions. And from our perspective, we are very excited to partner on extending it to Tier 2, Tier 3, working closely with the minister and other institutions in India. And the core of it, having accessibility to this GPU at such an amazing price point, really unlocks the potential. And I think as Minister and I, we have to be very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very, very,

Mridu Bhandari

Right. Well, finally, to both of you, now, as we embed AI into all our critical sectors, whether it’s BFSI or telecom or agriculture, healthcare, education, now governance has to move from intent to very, very strong operational safeguards. What does Zero Trust AI architecture then practically look like at a national level? Minister, if we can start with you.

Shri Jayant Chaudhary Ji

Well, Zero Trust, it’s an interesting terminology. For me, the way I look at it, it means that you have to be able to verify each and every protocol in your design. And in India, we generate a lot of data. Everything, and Indian citizens are quite open about access. globally privacy has been a major concern and sometimes it becomes also an impediment for governance because those data points aren’t being collected, analyzed, researched. In the Indian context, citizens are okay about sharing their data. And I’m saying this with the knowledge that, you know, crores of upper IDs have been formed, created using consent. But we have not received any blowback. In a way, from students’ families saying that, why are you?

Because they understand that if we are able to, with technology, customize and tailor our experience in the classroom for every student, where no student can get left behind, what that means for the employability, for the knowledge acquisition for that student, for the quality of their educational experience is immense. So I think, but once you’re collecting the data, there is a lot of… effort that needs to be put in so that that trust is maintained from zero trust to 100 % trust in the public mind. So I think our data sets need to be segmented. There are protocols within Government of India. In education, we’re thinking of creating a complete AI stack, which means the anonymized data sets will be made available for researchers for creating the value for the layers of innovation, for enabling startups to engage with that data that Government of India and the citizens have shared.

Similarly, in skill, we have Skill India Digital Hub, which is also looking at creating those data sets, which can then really help us unleash the next wave of innovation and requirement that we have in skilling. So I think once you have that system design in place, it can be achieved. The prime minister spoke about a label for content. That it should be verifiable and legal. this technology and consumer awareness is a big aspect of it and how we engage with these tools and how we understand what the outcome of our engagement, how true is it? How is it verifiable? Where are these AI models trained? Is there any bias in that data set? All that knowledge needs to be out there for the consumers.

I feel also that there needs to be an audit trail for our new AI models. Maybe in the future you could have CAG come out with an audit report of all the AI models. So it’s a brave new future, but it’s a balance. For partnership at scale, you need architecture with trust.

Mridu Bhandari

Absolutely. Final 30 seconds to you, Dr. Mohindra.

Dr. Vivek Mohindra

I think the minister said it very eloquently. I would extend the notion to zero trust should extend to start with data, go into AI models. the usability, the cybersecurity elements, and the identity and access management. Those would be the ways I would extend it. And practically, it means beyond the governance framework, having things like a national risk registry, observability, being able to report whenever there is an infraction and auditability. But, Minister, you said it very eloquently, and I think our AI blueprint has more details that would be worth looking at.

M

Mridu Bhandari

Speech speed

135 words per minute

Speech length

1976 words

Speech time

875 seconds

Coordinated AI execution pathway

Explanation

Bhandari stresses that translating the invest, innovate and evolve pillars into concrete actions is essential for scaling India’s AI ecosystem. A clear execution pathway that links investment, innovation and governance will drive sovereign AI adoption.


Evidence

“We are here this morning to really translate the invest, innovate and evolve pillars into very actionable steps that we all can take together to grow India’s AI ecosystem.” [1]. “Invest, invest in sovereign, scalable compute and data foundations, innovate with collaboration and with a future ready workforce and evolve, evolve into a responsible, agile, security first governance structure.” [3].


Major discussion point

AI Blueprint – Investment, Innovation, Governance


Topics

The enabling environment for digital development | Artificial intelligence


D

Dr. Vivek Mohindra

Speech speed

160 words per minute

Speech length

1078 words

Speech time

402 seconds

Compute and energy investment for sovereign AI

Explanation

Mohindra highlights that investment must cover both compute hardware and the energy infrastructure that powers it, as without reliable energy there can be no scalable AI compute. This dual investment is critical to ensure nationwide access, including for MSMEs.


Evidence

“Investment also includes energy infrastructure, because without energy, there is really no compute infrastructure you can put in place which can run on that.” [16]. “And the investment really goes at the heart of the compute infrastructure that a country needs to put in place to ensure that everybody has access to that infrastructure, including MSMEs who sometimes do not have the capacity to be able to put their infrastructure in place.” [20].


Major discussion point

AI Blueprint – Investment, Innovation, Governance


Topics

The enabling environment for digital development | Artificial intelligence | Environmental impacts


Public‑private partnership as core driver

Explanation

Mohindra asserts that realizing sovereign AI potential hinges on strong collaboration between public resources and private innovation, positioning PPP as the central mechanism for scaling AI infrastructure.


Evidence

“And at the heart of it, I hope what you will take away from our blueprint is realizing sovereign AI potential for any country, including India, is really about the public -private partnership.” [22].


Major discussion point

Public‑Private Partnership and Strategic Autonomy


Topics

Financial mechanisms | Artificial intelligence


Agile regulatory framework

Explanation

He argues that regulations must keep pace with rapid AI advances, balancing innovation with responsibility, so that policy does not become a bottleneck.


Evidence

“And the regulations have to be agile because the technology is moving at such a fast pace that you cannot anchor the regulatory framework to yesterday’s technologies.” [76]. “one of the fundamental balances that countries need to strike vis‑a‑vis, regulations, is striking the balance between innovation and responsibility while anchoring it to responsibility.” [79].


Major discussion point

Trust, Governance, and Regulatory Framework


Topics

Artificial intelligence | The enabling environment for digital development


Multi‑level AI skilling

Explanation

Mohindra emphasizes that AI adoption requires coordinated skill development from schools through colleges to the workforce, ensuring a talent pipeline that can leverage AI across sectors.


Evidence

“But innovating around how the skilling occurs all the way from schools to colleges to workforce entering employment and employers themselves, what role they play across a whole spectrum of mediums to deliver that skilling is a key part of the innovate pillar.” [111].


Major discussion point

Skills, Talent Pipeline, and Developer Ecosystem


Topics

Capacity development | Artificial intelligence


A

A. S. Rajgopal

Speech speed

173 words per minute

Speech length

1866 words

Speech time

644 seconds

Distributed, open‑source data centers lower compute cost

Explanation

Rajgopal proposes leveraging open‑source software and a geographically distributed data‑center model to dramatically reduce compute expenses, making AI services affordable for a broader population.


Evidence

“I mean, when we leverage open source, we can actually combine the infrastructure and open source and bring down the cost of compute so much that it is actually palatable for the Indian citizens to use because it’s not about serving this 2 percent, 3 percent of the population which pays the income tax.” [23]. “And I would prefer a distributed development of data centers across the country so that we are closer to the users and we” [24].


Major discussion point

AI Blueprint – Investment, Innovation, Governance


Topics

Artificial intelligence | Environmental impacts | The enabling environment for digital development


GST waiver and tax incentives

Explanation

He suggests that waiving GST and offering income‑tax benefits on imported servers would cut upfront infrastructure costs by about 18 %, easing financial barriers for AI deployments.


Evidence

“So maybe a GST waiver and some income tax benefits could be good.” [40]. “…the way to one thing that we could look at is whether we can wave off the GST up front and just take the GST when the services are being delivered what that would do is it will deliver it will reduce my upfront infrastructure cost to by about you know you know 18 percent…” [42].


Major discussion point

Enabling Access to AI Compute for SMEs and Startups


Topics

Financial mechanisms | The enabling environment for digital development


GPU capacity gap

Explanation

Rajgopal points out that India’s current GPU inventory (40‑50 k) falls far short of the estimated requirement (≈ 200 k), underscoring the need for massive investment to meet AI compute demand.


Evidence

“Maybe we need about 200 ,000 GPUs now, and we have about 40 ,000 to 50 ,000 now.” [54]. “In terms of actually enabling more GPUs, I think we have, you see, we need a lot of money.” [55].


Major discussion point

Enabling Access to AI Compute for SMEs and Startups


Topics

Artificial intelligence | Financial mechanisms


Attract diaspora talent with infrastructure

Explanation

He notes that providing world‑class infrastructure and funding will encourage Indian talent abroad to return and contribute to the domestic AI ecosystem.


Evidence

“These people can come back when this money and infrastructure fall in place.” [117].


Major discussion point

Skills, Talent Pipeline, and Developer Ecosystem


Topics

Capacity development | Financial mechanisms


B

Bhaskar Chakravarti

Speech speed

183 words per minute

Speech length

1314 words

Speech time

430 seconds

Trust infrastructure as non‑technical bottleneck

Explanation

Chakravarti identifies a dedicated trust infrastructure—covering transparency, explainability and institutional safeguards—as the primary non‑technical obstacle to AI momentum.


Evidence

“Now, there’s a third infrastructure, which is the non‑technical part, and that is what I would call the trust infrastructure.” [65]. “when we talk about trust, there’s a whole bunch of things you can do from an institutional standpoint to build trust, transparency, explainability, and so on.” [67].


Major discussion point

Trust, Governance, and Regulatory Framework


Topics

Artificial intelligence | Building confidence and security in the use of ICTs


Institutions must evolve faster than technology

Explanation

He warns that institutional frameworks need to keep ahead of rapid AI advances to provide safeguards without slowing innovation.


Evidence

“This is fast evolving as a technology, but equally as institutions will have to be faster than that in evolving.” [85].


Major discussion point

Speed of Innovation vs. Caution/Regulation


Topics

Artificial intelligence | The enabling environment for digital development


M

Manish Gupta

Speech speed

174 words per minute

Speech length

1181 words

Speech time

405 seconds

Sustainable, energy‑efficient data centers

Explanation

Gupta highlights the need for highly sustainable, energy‑efficient data‑center designs that maximize resource use while democratizing compute access across organisations of all sizes.


Evidence

“building highly sustainable, more energy efficient data centers that will allow us to use our energy resources in the best possible manner while democratizing the access of compute capacity…” [27]. “We’ve also got to remember the sustainability aspect of it… energy efficiency, sustainability on data centers…” [36].


Major discussion point

AI Blueprint – Investment, Innovation, Governance


Topics

Environmental impacts | Artificial intelligence


UPI of AI for strategic autonomy

Explanation

He proposes creating a unified, interoperable AI platform—likened to India’s UPI payment system—to secure strategic autonomy and foster a domestic developer ecosystem.


Evidence

“We need to have UPI of AI.” [8]. “globally, we are seeing nations tie AI strategy to strategic autonomy.” [21].


Major discussion point

Public‑Private Partnership and Strategic Autonomy


Topics

Artificial intelligence | Financial mechanisms


Shift focus to developers

Explanation

Gupta argues that moving from a user‑centric to a developer‑centric mindset—targeting millions of AI developers—will generate indigenous IP and sustain long‑term AI growth.


Evidence

“We have really got to think away from the users to the developers.” [103]. “You know, it’s got to move from 1 billion users to 1 billion or 1 million or 10 million developers and that’s the skill set, that’s the IP that we are going to bring in…” [104].


Major discussion point

Skills, Talent Pipeline, and Developer Ecosystem


Topics

Capacity development | Artificial intelligence


Agility and security are not opposing

Explanation

He contends that frameworks can simultaneously deliver rapid AI adoption and robust security, emphasizing integration rather than trade‑offs.


Evidence

“I think that’s where this will get practical, but equally these are not opposing” [134]. “It’s really about building the frameworks that are going to take both of them together.” [135].


Major discussion point

Speed of Innovation vs. Caution/Regulation


Topics

Artificial intelligence | Building confidence and security in the use of ICTs


S

Shri Jayant Chaudhary Ji

Speech speed

148 words per minute

Speech length

1259 words

Speech time

508 seconds

Ultra‑cheap compute for startups via PPP

Explanation

Chaudhary highlights a public‑private model delivering AI compute at roughly ₹65 per hour for startups and researchers, dramatically lowering cost barriers for innovators.


Evidence

“cost of this compute facility it’s being provided for startups, for researchers at 65 rupees for an hour.” [33]. “You know, Chaudhary, if I can start with you, how can PPP models really, how can PPP accelerate?” [96].


Major discussion point

Enabling Access to AI Compute for SMEs and Startups


Topics

Financial mechanisms | The enabling environment for digital development


Zero‑Trust AI audit trail

Explanation

He stresses that a Zero‑Trust AI architecture must include verifiable audit trails and protocol‑level verification to ensure trustworthiness of models.


Evidence

“I feel also that there needs to be an audit trail for our new AI models.” [12]. “For me, the way I look at it, it means that you have to be able to verify each and every protocol in your design.” [91].


Major discussion point

Trust, Governance, and Regulatory Framework


Topics

Artificial intelligence | Building confidence and security in the use of ICTs


PPP models accelerate human‑centric AI

Explanation

Chaudhary argues that PPPs, with a focus on people, can rapidly scale AI infrastructure while ensuring outcomes remain citizen‑centric and inclusive.


Evidence

“Now, PPP in this domain for me, and when you think about Manav being human centric, citizen centric, the P that really matters is the people.” [97]. “You know, Chaudhary, if I can start with you, how can PPP models really, how can PPP accelerate?” [96].


Major discussion point

Public‑Private Partnership and Strategic Autonomy


Topics

Financial mechanisms | The enabling environment for digital development


Agreements

Agreement points

Public-private partnership is essential for AI development and scaling

Speakers

– Dr. Vivek Mohindra
– Shri Jayant Chaudhary Ji

Arguments

Public-private partnership is key to realizing sovereign AI potential


PPP should focus on people as the most important ‘P’ in partnership


Summary

Both speakers emphasize that successful AI development requires collaboration between public and private sectors, with Dr. Mohindra focusing on marrying public resources with private innovation, and Minister Chaudhary emphasizing people-centric partnerships


Topics

Financial mechanisms | Artificial intelligence | The enabling environment for digital development


Trust and governance frameworks are critical for AI adoption

Speakers

– Dr. Vivek Mohindra
– Bhaskar Chakravarti
– Manish Gupta
– Shri Jayant Chaudhary Ji

Arguments

Balance between innovation and responsibility in regulatory framework


Trust infrastructure as critical non-technical element for AI adoption


Need for explainability and transparency to build user confidence


Zero trust architecture requires verification of each protocol with audit trails


Summary

All speakers agree that building trust through proper governance, transparency, and regulatory frameworks is fundamental to successful AI adoption, though they approach it from different technical and policy perspectives


Topics

Building confidence and security in the use of ICTs | Artificial intelligence | Human rights and the ethical dimensions of the information society


Skills development and workforce transformation are crucial for AI success

Speakers

– Dr. Vivek Mohindra
– Manish Gupta

Arguments

Skilling required across schools, colleges, and workforce through multiple delivery mediums


Need to move from 1 billion users to 1 million or 10 million developers


Summary

Both speakers emphasize the importance of comprehensive skills development, with Dr. Mohindra advocating for multi-level skilling programs and Manish Gupta focusing on transitioning from technology consumers to creators


Topics

Capacity development | Artificial intelligence | Social and economic development


Democratized access to AI infrastructure is essential for inclusive growth

Speakers

– A. S. Rajgopal
– Manish Gupta
– Shri Jayant Chaudhary Ji

Arguments

AI should serve 90% of population, not just 2-3% who pay income tax


UPI of AI similar to UPI of money for democratized access


World’s cheapest compute facility at 65 rupees per hour for startups and researchers


Summary

All three speakers advocate for making AI infrastructure accessible and affordable to the broader population, not just elite segments, through various mechanisms including subsidized access and unified platforms


Topics

Closing all digital divides | Artificial intelligence | Financial mechanisms


Similar viewpoints

Both advocate for a light-touch regulatory approach that prioritizes innovation over restrictive regulations, allowing the AI ecosystem to develop organically while addressing issues as they arise

Speakers

– A. S. Rajgopal
– Shri Jayant Chaudhary Ji

Arguments

Less regulation preferred to avoid curtailing innovation


India took approach of open access and strategy before robust policy


Topics

The enabling environment for digital development | Artificial intelligence


Both recognize the critical importance of energy infrastructure and distributed computing capacity for scaling AI infrastructure across India

Speakers

– Dr. Vivek Mohindra
– A. S. Rajgopal

Arguments

Energy infrastructure investment critical for compute infrastructure development


Distributed data center model across six states with 100 megawatts capacity


Topics

Information and communication technologies for development | Environmental impacts | Artificial intelligence


Both emphasize that technical infrastructure alone is insufficient; institutional frameworks and user understanding are essential for successful technology adoption and economic value creation

Speakers

– Bhaskar Chakravarti
– Manish Gupta

Arguments

Institutional capacity determines whether technology translates to economic value


Need for explainability and transparency to build user confidence


Topics

The enabling environment for digital development | Building confidence and security in the use of ICTs


Unexpected consensus

Light-touch regulation approach for AI innovation

Speakers

– A. S. Rajgopal
– Shri Jayant Chaudhary Ji

Arguments

Less regulation preferred to avoid curtailing innovation


India said that, yes, it’s good to have a strategy. And out of that strategy and experiences will evolve a robust policy


Explanation

It’s unexpected to see both industry and government representatives agreeing on minimal upfront regulation, as typically government officials advocate for stronger regulatory frameworks. This consensus suggests India’s unique approach of learning through implementation rather than restrictive pre-regulation


Topics

The enabling environment for digital development | Artificial intelligence


Focus on serving the broader population rather than elite segments

Speakers

– A. S. Rajgopal
– Shri Jayant Chaudhary Ji

Arguments

AI should serve 90% of population, not just 2-3% who pay income tax


World’s cheapest compute facility at 65 rupees per hour for startups and researchers


Explanation

The strong consensus between industry and government on prioritizing mass accessibility over premium services is unexpected, as industry typically focuses on profitable segments. This alignment suggests a shared vision of AI as a public utility rather than a luxury service


Topics

Closing all digital divides | Social and economic development | Artificial intelligence


Overall assessment

Summary

The speakers demonstrate remarkable consensus across key areas: the necessity of public-private partnerships, the importance of trust and governance frameworks, the critical role of skills development, and the need for democratized AI access. There’s also strong agreement on adopting a light-touch regulatory approach that prioritizes innovation while building institutional safeguards


Consensus level

High level of consensus with significant implications for India’s AI strategy. The alignment between government and industry perspectives suggests a coordinated approach to AI development that balances innovation with inclusion. This consensus provides a strong foundation for implementing the Dell Technologies blueprint and achieving India’s AI ambitions, particularly in creating sovereign AI capabilities while ensuring broad-based access and benefits


Differences

Different viewpoints

Regulatory approach to AI development

Speakers

– A. S. Rajgopal
– Dr. Vivek Mohindra

Arguments

Less regulation preferred to avoid curtailing innovation


Balance between innovation and responsibility in regulatory framework


Summary

Rajgopal advocates for minimal regulation to avoid stifling innovation, arguing that benefits outweigh risks and issues can be addressed as they evolve. Mohindra emphasizes the need for balanced regulatory frameworks that are agile but still anchor innovation to responsibility.


Topics

Artificial intelligence | The enabling environment for digital development | Building confidence and security in the use of ICTs


Speed versus caution in AI implementation

Speakers

– A. S. Rajgopal
– Bhaskar Chakravarti
– Manish Gupta

Arguments

Less regulation preferred to avoid curtailing innovation


AI impact on jobs requires proactive policy infrastructure for youngest major country


Need for explainability and transparency to build user confidence


Summary

Rajgopal favors rapid implementation with minimal upfront safeguards, while Chakravarti warns about the need to address job displacement proactively, and Gupta emphasizes that speed and security should not be opposing forces but must be balanced through proper frameworks.


Topics

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


Unexpected differences

Large enterprise AI adoption readiness

Speakers

– A. S. Rajgopal
– Manish Gupta

Arguments

Government setting pace with startups and MSMEs following, while large enterprises struggle


Need to move from 1 billion users to 1 million or 10 million developers


Explanation

Rajgopal unexpectedly argues that large enterprises are struggling with AI adoption and may fall behind startups and government initiatives, while Gupta focuses on building developer capacity without specifically addressing enterprise readiness challenges. This disagreement is unexpected because large enterprises typically have more resources for technology adoption.


Topics

The digital economy | Artificial intelligence | Capacity development


Overall assessment

Summary

The main areas of disagreement center around regulatory approach (light-touch vs. balanced frameworks), implementation speed (rapid deployment vs. cautious with safeguards), and institutional priorities (infrastructure vs. trust-building vs. people-centric approaches)


Disagreement level

Moderate disagreement level with significant implications for AI governance strategy. While speakers share common goals of AI advancement and democratization, their different approaches to regulation, implementation speed, and priority focus could lead to conflicting policy recommendations and implementation strategies.


Partial agreements

Partial agreements

All speakers agree on the importance of public-private partnerships for AI development, but they emphasize different aspects – Mohindra focuses on marrying public resources with private innovation, Gupta emphasizes building developer capacity, and Chaudhary reframes PPP to prioritize people as the central focus.

Speakers

– Dr. Vivek Mohindra
– Manish Gupta
– Shri Jayant Chaudhary Ji

Arguments

Public-private partnership is key to realizing sovereign AI potential


Need to move from 1 billion users to 1 million or 10 million developers


PPP should focus on people as the most important ‘P’ in partnership


Topics

Financial mechanisms | Capacity development | Social and economic development


Both speakers agree on the need for democratized AI access but propose different approaches – Rajgopal advocates for geographically distributed data centers across states, while Gupta proposes a unified API layer similar to UPI for consistent access.

Speakers

– A. S. Rajgopal
– Manish Gupta

Arguments

Distributed data center model across six states with 100 megawatts capacity


UPI of AI similar to UPI of money for democratized access


Topics

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


All speakers recognize the critical importance of trust in AI systems but focus on different dimensions – Chakravarti emphasizes institutional trust infrastructure, Gupta focuses on technical explainability, and Chaudhary emphasizes verification protocols and audit mechanisms.

Speakers

– Bhaskar Chakravarti
– Manish Gupta
– Shri Jayant Chaudhary Ji

Arguments

Trust infrastructure as critical non-technical element for AI adoption


Need for explainability and transparency to build user confidence


Zero trust architecture requires verification of each protocol with audit trails


Topics

Building confidence and security in the use of ICTs | Human rights and the ethical dimensions of the information society | Data governance


Similar viewpoints

Both advocate for a light-touch regulatory approach that prioritizes innovation over restrictive regulations, allowing the AI ecosystem to develop organically while addressing issues as they arise

Speakers

– A. S. Rajgopal
– Shri Jayant Chaudhary Ji

Arguments

Less regulation preferred to avoid curtailing innovation


India took approach of open access and strategy before robust policy


Topics

The enabling environment for digital development | Artificial intelligence


Both recognize the critical importance of energy infrastructure and distributed computing capacity for scaling AI infrastructure across India

Speakers

– Dr. Vivek Mohindra
– A. S. Rajgopal

Arguments

Energy infrastructure investment critical for compute infrastructure development


Distributed data center model across six states with 100 megawatts capacity


Topics

Information and communication technologies for development | Environmental impacts | Artificial intelligence


Both emphasize that technical infrastructure alone is insufficient; institutional frameworks and user understanding are essential for successful technology adoption and economic value creation

Speakers

– Bhaskar Chakravarti
– Manish Gupta

Arguments

Institutional capacity determines whether technology translates to economic value


Need for explainability and transparency to build user confidence


Topics

The enabling environment for digital development | Building confidence and security in the use of ICTs


Takeaways

Key takeaways

India needs a three-pillar AI strategy: invest in sovereign compute infrastructure and energy, innovate through skilling and collaboration, and evolve governance frameworks with agile regulations


Public-private partnerships are essential for realizing India’s sovereign AI potential, requiring marriage of public resources with private innovation


India must scale from 1 billion users to millions of developers to become AI creators rather than just consumers


Trust infrastructure is as critical as technical infrastructure, requiring institutional safeguards, transparency, and explainability


Distributed data center model across multiple states is needed to democratize AI access beyond metros to tier 2 and tier 3 cities


India has significant advantages including largest talent pool, strong digital backbone through DPI, and citizen willingness to share data for governance benefits


AI adoption should focus on practical applications beyond chatbots, such as citizen services, healthcare, and education transformation


Speed of innovation should be prioritized over excessive regulation, with guardrails evolving alongside technology development


Zero trust AI architecture requires verification protocols, audit trails, and segmented datasets while maintaining citizen trust


Resolutions and action items

Dell Technologies to partner with Ministry of Skill Development for AI apprenticeships and future skill labs extending to tier 2 and tier 3 cities


Government to continue expanding GPU capacity from current 38,000 to over 100,000 by end of year through India AI mission


Industry to work on developing practical AI use cases that can scale from experimentation to production for enterprises


Creation of AI stack in education with anonymized datasets for researchers and startups


Development of Skill India Digital Hub with datasets for next wave of skilling innovation


Implementation of content labeling for AI-generated content to ensure verifiability and legality


Establishment of audit trails for AI models with potential CAG auditing in future


Unresolved issues

Significant funding gap – India investing one-hundredth of what US invests in AI infrastructure


Large enterprises showing reluctance to adopt AI due to difficulty imagining applications and scaling from pilots to production


Impact of AI on employment in world’s youngest major country with existing unemployment challenges


Geopolitical restrictions on access to latest AI technologies and hardware


Need for institutional capacity building to ensure AI drives inclusive rather than divisive growth


Balancing innovation speed with responsible AI development and safety concerns


Addressing regional disparities in AI access and ensuring truly inclusive growth across diverse Indian geography


Suggested compromises

GST waiver on server imports upfront with collection only when services are delivered to reduce infrastructure funding burden


Income tax benefits for Indian AI service providers similar to those given for global market services


Less restrictive regulation initially with continuous evolution of safeguards as technology and understanding develop


Open source technology adoption to reduce costs while leveraging global innovations


Distributed rather than concentrated data center development to balance efficiency with accessibility


Sectoral approach to trust building – lighter governance for familiar applications like education, stronger safeguards for complex areas like healthcare


Thought provoking comments

The single most important determinant of what keeps a country on trajectory in terms of both the momentum of growth and also the state of their digital evolution is the demand side… there’s a third infrastructure, which is the non-technical part, and that is what I would call the trust infrastructure.

Speaker

Bhaskar Chakravarti


Reason

This comment reframes the entire AI discussion by introducing the concept of ‘trust infrastructure’ as equally important as technical infrastructure. It challenges the prevailing focus on technical capabilities and highlights that trust is what people recognize when it’s missing, making it a critical but often overlooked foundation for AI adoption.


Impact

This shifted the conversation from purely technical and policy discussions to examining the human and institutional elements of AI adoption. It prompted subsequent speakers to address trust, transparency, and explainability as core requirements, fundamentally broadening the scope of what constitutes AI readiness.


There’s one elephant on the table that we did not address… what is going to be the impact on jobs? This is the youngest major country in the world. It’s also one of the least employed country in the world. And now with AI coming in, is that going to help boost jobs or is it going to take jobs away? If we don’t fix that problem, get ahead of that, all the trust we are talking about, all the institutions you build could come down.

Speaker

Bhaskar Chakravarti


Reason

This comment directly confronts the most sensitive and potentially destabilizing aspect of AI adoption that other speakers had been avoiding. By connecting India’s demographic reality (youngest, least employed) with AI’s job impact, it highlights a fundamental contradiction that could undermine all other AI initiatives.


Impact

This comment created a sobering moment in an otherwise optimistic discussion, forcing acknowledgment that technical progress without addressing employment consequences could be counterproductive. It elevated the urgency of the skills and workforce transformation discussion beyond mere technical training to existential economic planning.


We need to have UPI of AI. Where we are building that at scale using the data sources that we have… Think about AI Kosh. There are more than 7000 data sets that are now available to organizations of all sizes… a consistent API layer that’s bringing theoretically all of the data center and the compute capacity… to be one single layer that can be consumed by anybody and everybody across the nation.

Speaker

Manish Gupta


Reason

This analogy brilliantly connects India’s most successful digital transformation (UPI) to AI strategy, providing a concrete, relatable framework for understanding how AI infrastructure could be democratized. It transforms abstract concepts of AI accessibility into a proven model that resonates with Indian experience.


Impact

This comment provided a tangible vision that other participants could build upon, shifting the discussion from theoretical frameworks to practical implementation models. It gave concrete meaning to concepts like ‘democratization’ and ‘inclusive AI’ by referencing a successful precedent that everyone understood.


I don’t think we will be pure play, this chatbot… generative AI, the way it has been envisaged… will be the primary use case. I think we are going far beyond what AI can be applied in terms of actually improving the productivity of citizen services… We did that [photo deduplication for Election Commission] in a matter of 51 hours, and then we responded to them as to whether they had complications.

Speaker

A. S. Rajgopal


Reason

This comment challenges the dominant narrative around AI being primarily about chatbots and generative AI, instead positioning India as potentially leading in practical, governance-focused AI applications. The concrete example of processing 90 crore photographs demonstrates AI’s transformative potential for uniquely Indian challenges.


Impact

This reframed the entire discussion about India’s AI competitive advantage, suggesting that rather than competing in consumer AI, India could lead in civic and enterprise AI applications. It shifted focus from following global AI trends to identifying India-specific AI opportunities and use cases.


For partnership at scale, you need architecture with trust… In the Indian context, citizens are okay about sharing their data… But we have not received any blowback… because they understand that if we are able to, with technology, customize and tailor our experience in the classroom for every student, where no student can get left behind, what that means for the employability… is immense.

Speaker

Shri Jayant Chaudhary Ji


Reason

This comment reveals a fundamental cultural and policy insight about India’s unique position regarding data sharing and privacy, contrasting sharply with Western approaches. It suggests that Indian citizens’ willingness to share data for collective benefit could be a strategic advantage in AI development, provided trust is maintained.


Impact

This observation recontextualized the entire privacy and governance discussion, suggesting that India’s approach to data and AI governance might need to be fundamentally different from Western models. It validated a more open, collaborative approach to AI development while emphasizing the critical importance of maintaining public trust.


Overall assessment

These key comments fundamentally shaped the discussion by expanding it beyond technical infrastructure to encompass trust, employment, cultural context, and practical applications. Chakravarti’s interventions consistently challenged the panel to address harder questions about institutional capacity and social impact, while Gupta’s UPI analogy provided a concrete framework for understanding AI democratization. Rajgopal’s examples demonstrated India’s potential for unique AI applications, and the Minister’s insights about Indian citizens’ data-sharing attitudes revealed cultural advantages that could inform policy. Together, these comments transformed what could have been a standard technology discussion into a nuanced exploration of India’s distinctive path to AI leadership, balancing optimism with realistic acknowledgment of challenges like employment impact and the need for sustained public trust.


Follow-up questions

How can we remove GST bottlenecks for AI infrastructure providers to reduce upfront costs by 18%?

Speaker

A. S. Rajgopal


Explanation

This policy intervention could significantly reduce infrastructure deployment costs and accelerate AI adoption by making compute resources more affordable


Should Indian AI service providers receive the same tax benefits as global providers when hosting services in India?

Speaker

A. S. Rajgopal


Explanation

This addresses competitive parity and could encourage more domestic AI service development rather than just focusing on global markets


How do we bridge the gap between India’s current 40,000-50,000 GPUs and the needed 200,000 GPUs?

Speaker

A. S. Rajgopal


Explanation

This represents a critical infrastructure gap that needs to be addressed for India to meet its AI compute requirements


How can district-level implementation of AI policies be made sensitive to local variations across different states?

Speaker

Bhaskar Chakravarti


Explanation

Trust and governance implementation varies significantly between states like Telangana and Jharkhand, requiring localized approaches


What will be the impact of AI on jobs in India, especially given it’s the youngest major country but also one of the least employed?

Speaker

Bhaskar Chakravarti


Explanation

This is described as an ‘elephant on the table’ that could undermine all trust-building efforts if not addressed proactively


How can we help large enterprises overcome their reluctance to adopt AI and move beyond just imagining chatbot applications?

Speaker

A. S. Rajgopal


Explanation

Large enterprises are struggling to identify practical AI applications beyond basic chatbots, limiting widespread adoption


How can we scale AI use cases from experimentation and pilots into production across enterprises?

Speaker

Manish Gupta


Explanation

Many organizations have AI pilots but struggle to scale them into production systems that deliver real business value


How can we develop a ‘UPI of AI’ – a unified infrastructure layer for AI services similar to India’s digital payments success?

Speaker

Manish Gupta


Explanation

This would democratize AI access across the nation, allowing organizations of all sizes to innovate using shared AI infrastructure


How can we transition from ‘Made in India’ to ‘Trusted in India’ for AI systems?

Speaker

Manish Gupta


Explanation

Building trust and safety frameworks is crucial for India’s AI systems to be globally competitive and locally accepted


How can we extend AI skilling programs to Tier 2 and Tier 3 cities effectively?

Speaker

Dr. Vivek Mohindra


Explanation

Ensuring AI skills development reaches beyond metros is critical for inclusive growth and tapping into distributed talent


How can we implement a national AI audit system, potentially involving CAG audits of AI models?

Speaker

Shri Jayant Chaudhary Ji


Explanation

This would provide systematic oversight and accountability for AI systems deployed at scale across the country


How can we create effective content labeling systems to ensure AI-generated content is verifiable and legal?

Speaker

Shri Jayant Chaudhary Ji


Explanation

Consumer awareness and content verification are essential for maintaining trust in AI systems


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