Designing Indias Digital Future AI at the Core 6G at the Edge

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

Session at a glance

Summary

This discussion focused on India’s strategic approach to integrating artificial intelligence with 6G technology to transform the country from a technology consumer to a global leader in next-generation connectivity and intelligence. Ashok Kumar from the Department of Telecom outlined how 6G represents a paradigm shift where AI is natively embedded into network architecture from the outset, unlike previous generations where AI was added as an afterthought. He highlighted government initiatives including the 6G Accelerated Research Program, support for 100+ research projects, establishment of 5G labs across institutes, and collaboration with the Bharat 6G Alliance to build India’s indigenous technology capabilities.

The panel discussion revealed that AI-driven applications will fundamentally change network traffic patterns, with uplink traffic expected to increase significantly due to contextual data requirements and multi-modal AI agents. Industry experts projected that AI traffic will grow from 5% to 30% of total network traffic by 2033, requiring networks to handle more bursty, persistent uplink communications. The panelists emphasized that 6G networks will need to support distributed intelligence across devices, edge computing, and cloud infrastructure, with different inference tasks allocated based on latency requirements and use case complexity.

Key challenges discussed included the need for sovereign AI ecosystems to ensure affordable intelligence reaches every citizen, the importance of training AI models on India-specific data to avoid cultural bias, and the necessity of open, interoperable frameworks similar to India’s successful UPI model. The discussion concluded that India’s scale advantage and diverse data sets position it well to democratize AI access while building end-to-end sovereign technology stacks that can serve both domestic needs and global markets.

Keypoints

Major Discussion Points:

– AI-Native 6G Network Architecture: The discussion emphasized how 6G represents a fundamental shift from previous generations, with AI being natively integrated into the network design from the ground up rather than added as an afterthought. This includes ubiquitous intelligence across all network elements – user equipment, radio, core, and applications.

– Traffic Pattern Transformation and Infrastructure Challenges: Panelists discussed how AI applications will dramatically change network traffic patterns, particularly increasing uplink traffic requirements from the current 10:1 or 12:1 downlink-to-uplink ratio to approximately 4:1. This shift is driven by AI’s need for contextual data, multi-modal agents, and real-time inferencing capabilities.

– Distributed Intelligence and Edge Computing: The conversation explored how AI processing will be distributed across different tiers – from devices and sensors to edge networks, core infrastructure, and cloud systems. The goal is to optimize latency, coverage, and token economy while managing power consumption and infrastructure costs.

– India’s Digital Sovereignty and Self-Reliance: A significant focus was placed on India’s need to develop sovereign AI and 6G capabilities rather than relying on rented intelligence from other countries. This includes building end-to-end technology stacks, participating in global standards development, and creating frameworks that serve India’s specific cultural and economic needs.

– Government Initiatives and Industry Collaboration: Discussion of various government programs including the 6G Accelerated Research Program, 100 5G labs across institutes, support for startups in standards participation, and the role of Bharat 6G Alliance in coordinating industry-academia-government efforts.

Overall Purpose:

The discussion aimed to explore how India can position itself as a leader in the convergence of AI and 6G technologies, moving from being a consumer of global technology to a creator and shaper of next-generation intelligent networks. The session focused on practical implementation strategies, infrastructure requirements, and collaborative frameworks needed to achieve this transformation.

Overall Tone:

The discussion maintained an optimistic and forward-looking tone throughout, characterized by technical expertise and strategic thinking. Speakers demonstrated confidence in India’s potential to lead in 6G and AI integration while acknowledging significant challenges around infrastructure, standardization, and coordination. The tone was collaborative and solution-oriented, with panelists building on each other’s points and emphasizing the need for industry-government-academia partnerships. The atmosphere remained professional and constructive even when addressing complex technical and policy challenges.

Speakers

Speakers from the provided list:

– Moderator: Session moderator facilitating the discussion

– Ashok Kumar: Deepthi Director General, Department of Daily Communication, Government of India – delivered keynote address on government initiatives for 6G development and AI integration

– Radhakant Das: Heads the Technology Engineering and Innovation Function for Network Solutions and Services (NSS) at TCS – served as panel discussion moderator

– Rajeev Saluja: Vice President, 5G Radio at Reliance Jio – expertise in telecommunications and 5G/6G technology development

– Surojeet Roy: Senior Telecommunications Leader, Head of Technology, Technology and Solutions, COE, at Nokia India – expertise in telecommunications technology and network solutions

– Sandeep Sharma: Technology leader and AI innovator, Vice President and Global Head of Emerging Technologies, Network Services at Tech Mahindra – expertise in AI and emerging technologies

– Audience: Multiple audience members who asked questions during the Q&A session

Additional speakers:

– Sidhu: Representative from AT&T who asked questions about network API monetization and telecom industry challenges

Full session report

This comprehensive discussion explored India’s strategic transformation from a technology consumer to a global leader in the convergence of artificial intelligence and 6G networks, representing what participants characterized as a “historic inflection point” for the nation’s digital future. The session, part of a four-day AI Impact Summit, brought together senior government officials, industry leaders, and technical experts to examine how India can position itself at the forefront of next-generation intelligent connectivity infrastructure.

Fundamental Paradigm Shift: From Connectivity to Intelligence

Ashok Kumar from the Department of Telecom established the foundational premise that 6G represents a fundamental departure from previous network generations. Unlike 2G, 3G, 4G, and even 5G—where artificial intelligence was either absent or added as an afterthought—6G is being designed with AI natively integrated from the outset. This shift moves beyond merely connecting humans and objects to what Kumar described as “connecting intelligence itself.”

Kumar referenced the International Telecommunication Union’s 6G framework, noting that it explicitly includes “integrated artificial intelligence and communications” as one of six core usage scenarios, with “ubiquitous intelligence” as a fundamental design principle. While Kumar mentioned “four overarching principles” in the framework, the technical details of this architecture represent an ongoing area of standards development.

This architectural transformation means that every element of the end-to-end 6G system—from user equipment and radio access networks to core infrastructure and applications—will have AI embedded natively. The implications extend far beyond technical specifications, representing what Kumar characterized as a “historic opportunity” for India’s ecosystem of micro, small, and medium enterprises, startups, and academic institutions to participate not just in standards development but in building indigenous end-to-end technology stacks.

Government Initiatives and Strategic Framework

The Indian government has implemented a comprehensive strategy to support 6G and AI development through multiple coordinated initiatives. The 6G Accelerated Research Program, launched two years prior to the discussion, has selected over 100 projects spanning terahertz technology, artificial intelligence, machine learning, semantic communications, and sensing capabilities. Kumar specifically highlighted support for terahertz testbeds and AOC (All-Optical Communication) testbeds as part of this strategic investment in building indigenous capabilities.

Recognizing that standards participation is crucial for technology sovereignty, the Department of Telecom has addressed cost barriers that previously prevented Indian startups from engaging in global standards development. Through support for the Telecommunications Standards Development Society of India (TSDSI), startups can now participate in 3GPP standards development for ₹10,000 rather than the previous ₹5-6 lakh, dramatically lowering the barrier to entry for Indian innovation in global standards.

The establishment of 100 5G laboratories across educational institutions, inaugurated by India’s Prime Minister, represents another strategic pillar. These laboratories are creating distributed research capabilities that are already transitioning from 5G research to 6G development. Kumar emphasized that these laboratories provide practical foundations for 6G research, as institutions with strong 5G knowledge and development capabilities are well-positioned to advance to 6G technologies.

The government’s collaboration with the Bharat 6G Alliance exemplifies the coordinated approach between public and private sectors. The alliance has established working groups on technology, spectrum, and devices, with members actively contributing to policy recommendations that shape government strategies for 6G leadership.

Traffic Pattern Revolution and Infrastructure Implications

Surojeet Roy from Nokia presented compelling projections showing how AI applications will fundamentally transform network traffic patterns in ways that challenge traditional telecommunications infrastructure design. According to Nokia’s analysis, AI traffic will grow from the current 5% of total network traffic to approximately 30% by 2033, representing a six-fold increase in AI-driven communications.

More significantly, Roy projected that the traditional downlink-to-uplink traffic ratio of 10:1 or 12:1 will shift dramatically to approximately 4:1 due to AI’s contextual data requirements. This transformation stems from AI applications’ need to transmit rich contextual information—such as 360-degree environmental views for augmented reality applications or comprehensive sensor data for autonomous systems—to cloud-based inference engines.

These traffic pattern changes necessitate fundamental network architecture modifications. Current networks are not designed for such uplink-heavy traffic patterns, requiring significant enhancements in uplink spectral efficiency and capacity. Roy projected that 6G networks will need to support minimum bandwidths of 400 MHz compared to the typical 100 MHz in current 5G deployments, combined with five times greater spectral efficiency, potentially delivering 20 times more capacity than existing 5G networks.

Distributed Intelligence Architecture and Edge Computing

The convergence of AI and 6G will create a distributed computing fabric that extends far beyond traditional network boundaries. The panelists outlined a tiered approach to AI processing that optimizes for latency, power consumption, and cost efficiency. Simple inferencing tasks and agentic workloads will be processed at edge locations, including enhanced user devices and edge computing nodes, while complex multi-step, multi-agent workflows will remain centralized in cloud data centers.

Roy proposed an innovative approach to democratizing AI access through existing infrastructure, suggesting the utilization of GPU resources at cell towers during periods of low network utilization. This would allow users to access AI computing resources for model training and inference tasks, potentially reducing the cost of AI access while maximizing infrastructure utilization efficiency.

This distributed approach addresses multiple challenges simultaneously, reducing latency for time-critical applications while distributing power consumption across the network rather than concentrating it in massive data centers. Roy noted that infrastructure constraints, particularly power consumption and site requirements, are driving this distributed approach as much as use case requirements.

Enterprise Transformation and Economic Implications

Rajeev Saluja from Reliance Jio outlined how AI-native 6G networks will enable fundamental business transformation beyond faster connectivity. He identified three primary value pools for enterprises: enhanced demand analysis through new data streams, comprehensive workflow automation, and robust end-to-end security frameworks.

The workflow automation capabilities will move human workers up the value chain while AI agents orchestrate complex end-to-end processes. This transformation will extend enterprise digitalization far beyond current enterprise resource planning implementations to comprehensive process intelligence and automation.

For India specifically, Saluja emphasized that the wireless nature of the economy makes this transformation particularly significant. With limited fiber penetration, 6G and AI convergence provides the primary pathway for enterprises to reach last-mile customers and operations. He noted the scale opportunity, correcting himself to emphasize “140 crore [1.4 billion] people” that this infrastructure must serve.

The Token Economy and Sovereignty Debate

One of the most significant discussions centered on what Saluja termed the emerging “token economy” and its implications for technological sovereignty. From Reliance Jio’s perspective, Saluja argued that India cannot afford to “rent intelligence” from external sources and must build end-to-end sovereign AI ecosystems from devices to cloud infrastructure.

This sovereignty imperative, according to Saluja, stems from both economic and strategic considerations. The token economy that will emerge with widespread AI deployment means that dependence on external AI systems could create unsustainable economic burdens while limiting India’s ability to serve its diverse population affordably.

However, Sandeep Sharma from Tech Mahindra, who participates in the 6G use case group of the Bharat 6G Alliance, advocated for a more nuanced hybrid approach. Drawing parallels to India’s successful Unified Payments Interface (UPI) model, Sharma argued that open, interoperable ecosystems can drive innovation and adoption while maintaining strategic control. He emphasized the importance of API-driven, loosely coupled architectures that avoid vendor lock-in while enabling global collaboration.

Cultural Adaptation and Social Context

The discussion revealed sophisticated understanding of how AI systems must be adapted to India’s unique contexts. Roy highlighted that AI models trained on data from other regions may not serve India’s specific use cases effectively, citing autonomous vehicles that would need to understand Indian driving patterns and traffic behaviors.

This cultural adaptation extends to economic structures, with Roy noting India’s approximately 490 million informal workers—including carpenters, drivers, electricians, and agricultural workers. AI systems must be designed to enhance productivity for this massive workforce segment through applications like AI-powered tools that help workers analyze requirements and generate step-by-step procedures tailored to Indian contexts.

Sharma emphasized that AI’s influence extends to fundamental questions of social values and cultural preservation, arguing that AI systems must reflect Indian social values and cultural contexts through sovereign development using India-specific data and training approaches.

Technical Innovations and Capabilities

The technical discussion revealed breakthrough capabilities that AI-native 6G networks will enable. Roy discussed Nokia’s research into deep learning algorithms for communication optimization, including DeepRx and DeepTx technologies, which Nokia projects could deliver 25-30% capacity improvements even in challenging signal-to-noise environments. These AI-enhanced communication systems can potentially decipher signals that traditional 5G systems cannot process while supporting higher-order modulation schemes.

The integration of AI into radio access networks will enable adaptive, intelligent communication that optimizes in real-time based on environmental conditions, interference patterns, and traffic demands. This represents a fundamental shift from static network configurations to dynamic, learning systems that continuously improve performance.

Semantic communications, highlighted as a key research area, will enable networks to understand and optimize based on the meaning and context of communications rather than treating all data equally.

Implementation Challenges and Open Questions

Despite the optimistic vision, several significant challenges remain unresolved. The specific distribution of AI processing across different network tiers—devices, radio access networks, core networks, and cloud—remains undefined and will likely vary significantly based on use cases and application requirements.

Interoperability concerns emerged as a critical challenge during the Q&A session, with audience members highlighting how current AI-enabled devices like Meta’s smart glasses work only within proprietary ecosystems. The development of open API architectures that enable cross-platform functionality while maintaining competitive differentiation represents a complex technical and business challenge.

Questions about network API monetization and the practical implementation of distributed edge computing also highlighted the gap between vision and current technical capabilities. Power consumption and infrastructure requirements for widespread edge deployment need further analysis and planning.

Future Roadmap and Next Steps

Kumar outlined concrete progress toward implementation, noting the Department of Telecom’s work with the Accelerated National Research Foundation to develop end-to-end systems based on 3GPP Release 18, evolving toward what he indicated would be Release 21 as the first 6G release. He expressed expectations that this work would advance significantly within the next two quarters, suggesting an aggressive timeline for moving from research to practical deployment.

The emphasis on industry adoption of the 100 5G laboratories across educational institutions provides a mechanism for scaling research efforts and ensuring that academic research aligns with industry needs. The continued role of the Bharat 6G Alliance in providing policy recommendations and coordinating industry efforts represents a crucial mechanism for maintaining strategic alignment across stakeholders.

Economic Vision and Global Positioning

Roy referenced projections from a Niti Aayog report suggesting India’s goal of achieving a $30 trillion economy, positioning the AI and 6G convergence as fundamental to this economic transformation. The democratization of intelligence through affordable, accessible AI capabilities could enhance productivity across India’s massive informal economy while enabling new forms of economic activity.

India’s scale advantages—with over 1.4 billion people and diverse data sets across multiple languages and cultural contexts—position the country to achieve significant economies of scale in AI development and deployment. This could potentially reduce costs and improve accessibility compared to smaller markets while building export capabilities in next-generation technologies.

Conclusion

This discussion revealed both the transformative potential of AI-native 6G networks and the complex challenges involved in positioning India as a global leader in this technological evolution. The convergence represents more than a technical upgrade—it constitutes a fundamental shift toward distributed intelligence infrastructure that could reshape economic, social, and cultural interactions.

The strong consensus among government, industry, and technical experts on the need for indigenous capabilities, balanced with open collaboration, provides a foundation for coordinated development efforts. However, the discussion also highlighted significant unresolved questions about technical implementation, interoperability standards, and the balance between sovereignty and global collaboration.

The success of India’s AI and 6G convergence strategy will depend on sustained commitment to indigenous capability development, continued investment in research and infrastructure, and effective coordination among all stakeholders. While the participants expressed optimism about India’s potential to move from technology consumer to technology creator, the path forward requires addressing substantial technical, economic, and policy challenges while maintaining the collaborative momentum evident in this discussion.

Session transcript

Moderator

opportunity, ensuring that India moves from being a consumer of global technology cycles to becoming a sharper of the world’s next intelligence and connectivity frontier. To kick off the discussion, I would like to invite Mr. Ashok Kumar, Deepthi Director General, Department of Daily Communication, Government of India, to deliver a keynote address. Thank you.

Ashok Kumar

So my colleague panelist, the expert panelist here, the distinguished dignitaries in the hall, and other participants gathered here, Thank you,

Moderator

Mr. Ashok. Thank you, Mr. Ashok. Thank you, Mr. Ashok. So it’s

Ashok Kumar

my privilege to deliver the keynote address before such a gathering. So although the hall is like empty, but I suppose many of our participants are online. The theme of this session, AI at the Core, 6G at the Edge, captures the transformative journey which we have started now. So let me go back slightly back in the history. When we rolled out 2G, 3G and 4G, the vision was to connect V, human beings, and as technology progressed, we started connecting machines and objects through innovations like NB -IoT, as all of us know. Although they were not part of the original vision and we can say that those were evolutions, extensions and maybe we can also call that as afterthought.

When the work on 5G started at ITU way back in 2012, if you recall, after three years of deliberations with all the state 190 plus countries and also the sector members like industry, academia. So ITU released a 5G framework, they call it IMT 2020. And for the first time. The usage scenario, the three usage scenario envisioned by ITU included support for massive connectivity of objects and machines and also the applications which required very, very low latency. So, what we should say that for the first time technology was designed, it was not an afterthought even for machines, not only for we humans but also for the machines. As we know that the 5G journey started with 3G BP release 15 and that was also delivered in three parts, right?

Just to start early, so they had three part of releases of release 15 and then every one and half years or two years we have the next evolution of the 5G technology. And when we reached to release 18 and that is also called 5G advanced. So, basically AI, artificial intelligence began to be integrated at part of 3G. To solve the network functions or to solve some of the network functions requirement. So again, this was some sort of an afterthought, right? Because we started, our vision was not the native integration of AI into the 5G system, but as technology evolved, we started doing that, perhaps the precursor of the 6G. The shift now which we are seeing in 6G, the story is different.

If you look at the ITU framework for 6G, which was released two years back, so that has got six usage scenarios, they have envisioned six usage scenarios, and one of the usage scenarios is integrated artificial intelligence and communications. So now, the artificial intelligence is part of the initial thought itself, and more important, along with those six usage scenarios, what ITU conceived is the four overarching principles, and the fifth is the three main principles, and the sixth is the three main principles, and the sixth is the three main principles, The four, the key design principle we can say, and one of the design principle if you read is ubiquitous intelligence. So when we say ubiquitous intelligence, what we mean is that every element of our end -to -end 6G system, be it the user equipment or be it the radio or be it core or be it applications, everyone will use AI embedded natively into the system.

So the earlier generations, if you talk about connected humans and objects or machines, 6G will actually correct the intelligence as it is envisioned in the ITU document. And of course, 3GPP has started working on all those aspects. So this is a kind of… It’s a historic opportunity for me in India, particularly for our… ecosystem, that is our MSME, startup, academia and everyone. So it’s an opportunity not only to like participate in the standard so that our technology, our innovations becomes part of the standard, but also to build our own end -to -end 6G technology stack. So what are the different government efforts since I come from government, Department of Telecom, so I would also like to touch upon what are different efforts the government is trying to do to create a robust ecosystem of 6G research and innovations.

Of course, government alone cannot do everything, but whatever effort we are trying. So one of the important aspects is about whatever technology we are trying to develop, right, whatever IP we are trying to create, if that enters into the standard, 3GPP standard itself, it’s good for us that we are shaping the standards. The India is also. So, I mean, we started doing such activities from 5G onwards. Before that, we were not at all participating in the 6G, I mean, telecom technologies standard making. So, to support our startup, et cetera, onto this, so if a startup company want to, say, participate in 3GPP standards, that company has to be member of first our TSDSI and also individual members of 3GPP and that’s a cost, right?

So, at DOT, we are supporting TSDSI so that our startups can be member of TSDSI and 3GPP at a very, very low cost of 10 ,000, not 5 lakh, 6 lakh, and they can participate. So, that’s, it’s a continuous thing which we try, trying and doing. Interesting. In addition to that, as we know that unless we do our own kind of a research and technology development, even before the standard starts, building up and then take it to the standard. So to support that activity, we had come out with a scheme called 6G Accelerated Research Program. So that was floated, I think, two years back. And we have selected 100 plus 6G related projects in different area.

That includes terahertz technology, artificial intelligence, machine learning, semantic communications. And every aspect of sensing, every aspect of the vision of the 6G. And those projects are progressing. And we are trying to help them also participate into the standard. In addition to that, we have also supported some 6G related testbed like terahertz testbed and one AOC testbed, which is doing very good work as of now. In addition to that, there are many other. Programs which are sort of in progress. For example, recently we worked with. ANRF wherein we are trying to come out with a scheme wherein we are trying to build end -to -end system based on release 18 and evolve it to release 19, 20 and 21.

As you know that release 21 would be the first release of 60. So we are trying to do that and perhaps that will come very soon, maybe in the next two quarters that will be out. In addition to that, I would also like to take name of Bharat 6G Alliance here because we are also closely working with Bharat 6G Alliance as government. So Bharat 6G Alliance has created multiple working group on technology, on spectrum, on devices and some of the members of the alliance have been working on the technology and some of the members of the chair of those working groups are here as part of this session also. So, basically, Bharat 6G alliance is kind of suggesting government that what next to be done to be leader in 6G and based on that, we are trying to, I mean, shape the policies of the government.

In addition to Department of Telecom, our other ministries like Maiti is also supporting various 6G related projects. I would take name of the scheme of DST, which is RDI. So, once you have a technology, perhaps you want to scale RDI will come handy. And we have taken up with DST that telecom sector should be included as part of the sector which will be supported. In the RDI and Secretary DST had agreed to this particular aspect and whenever the schemes are getting floated, our companies, our startup in the field of telecom can actually apply. As part of DST, they also have, they have been running cyber physical programs. So, they are also, they are supporting some of the.

5G and 6G related projects. One most important thing which DOT did previous years, which was actually announced in the budget and inaugurated by our Honorable Prime Minister was 100 5G lab in 100 different institutes across the country. Those labs are actually operational. So those are some of the points where actually 6G research has also started because once you have good knowledge of 5G and if you are able to develop use cases or 5G network elements itself, perhaps you are ready to do something on 6G. And so my request to industry here, those who are online, that please adopt one or two 5G lab and try to work with them that what more can be done in the technology area.

With this, I want to conclude my address by inviting the esteemed panelists to deliberate and provide some answers to your questions. Thank you. Thank you. not only to the government, but also to the industry, MSME and startup and academia on the way forward on this

Moderator

Thank you, sir. So now we are moving to our very next segment, the panel discussion. Our first speaker is Rajiv Seluja, Vice President, 5G Radio at Reliance Jio. Also joining us is Surojeet Roy, a Senior Telecommunications Leader, Head of Technology, Technology and Solutions, COE, at Nokia India. Sandeep Sharma, a technology leader and AI innovator, Vice President and Global Head of Emerging Technologies, Network Services at Tech Mahindra. The dialogue will be moderated by Radhakant Das, who heads the Technology Engineering and Innovation Function for Network Solutions and Services, NSS, at TCS. Before we start this panel discussion, I would like all the speakers to have group photographs, please. May I also request Ashok Kumar, sir, to be here?

Thank you. Thank you, sir.

Radhakant Das

Okay. Can we start? Great. So, good morning, our distinguished guests. My colleagues from the Government, Industry and Academy. All of you who are online, good morning to you all as well. So, this entire topic, which you can read out, A at the core and 6G at the edge, and Designing India’s Next Resilient, Innovative and Efficient Digital Frontier. We are at a historic inflection point where the intelligence is the basic infra. based on which the next evolution of this planet will actually continue. And we have seen until 5G, but in the 6G, a lot of hope. And we see that 6G not only emerges a faster network as an option, but as a distributed computer fabric.

It’s going to have a platform that enables the intelligence everywhere across radio, core, and age, including the satellite, which is non -terrestrial networks, and the sensor ecosystems. Devices in 6G and AI will take a major role. We’ll talk about how the 6G payloads or the designs will actually be AI -native, how it will drive the overall objective of bringing AI and 6G together. As a success. The professor has already pointed out the standards of already… will take in AI native to the 6G standards which is coming up in Magda’s next two quarters. It’s quite optimistic but yes, we are looking forward for the faster to come. And thanks for the government to give all the support to the industry, academia and the Vara 6G Alliance is also doing a great job and our Honourable Minister and Prime Minister are actually actively supporting and giving directions time to time to get this forward.

So we will focus on the edge interfaces at a scale. We’ll talk about semantic communications where like you would have seen India has really put a very strategic point of view that AI is, we will ensure that AI is kind of energy efficient. It will not be responsible for melting the data centres. It will be power efficient. And we will ensure that every compute capacity is being optimally utilized, not like we have enough compute and we will use it as much as possible. And data is a strategic fuel for this AI. And the networks, telecom networks, it’s not only 6G, but all kinds of connectivity networks, they will drive this data, this strategic fuel to the users, to the sensors, to the cloud, to the computing systems and deliver it.

So here we go. We start, I think, all our panelists are there. I think their names are already there in the backside of the screen. So I’ll just start with some of the questions. So maybe we’ll start with Surajit. Yeah. So Surajit, I have. I have the first question for you. The India in the context of India’s, in the context of 6G vision. where networks are expected to reason, self -organize and optimize across ecosystem, run, core, edge and of course when you say edge, it includes devices and the sensors. How do you see AI is transforming the RAN for the 6G in the day one? You may throw some light on that please.

Surojeet Roy

Yeah, sure. So, I think we can talk about it in few steps. For example, first one is on the devices. So, we have many form factor of devices coming up. We already have smart glasses launched. We had this AR, VR glasses earlier where we could not see outside, but then now we have glasses which look more like the normal glasses we wear. But those are having this AI functionalities, right? you can do lot many you know work in the background and nobody would know that you are actually looking at something else while you are talking to a person so I think from the device perspective the intelligence is being built up in the devices handsets there was a talk that maybe all these smart glasses and wearables will take away the handset but then I guess these handsets are going to stay for a while I don’t know till when but at least for the next 4 -5 years those are going to stay and we will also have lots of wearables right and maybe some you know body patches as well which can sense your heart rate so as a person as a user I see that we will be having multiple devices going forward not only one device we will have handset, we will have smart glasses, we will have wearables right and this all will be having AI enabled devices capabilities, but because of the form factor, these devices might not be able to do all the inferencing tasks on their own, which means that there will be some inferencing help needed from the data centers, whether it is centralized or edge data centers, which means there will be lots of traffic requirements towards the network, especially in the uplink.

Radhakant Das

Okay. So, Rojit, if you would like to expand it a little further, the inferencing is now tiered or distributed, as what I am mentioning. So, what percent is, maybe you can take an average application, will be there residing in the devices or sensor side? What percent is in the RAM? What percent is in the core of the network premises? And what will go to the cloud?

Surojeet Roy

Yeah, I think we do not have those exact numbers, but I think if I look at the data traffic as such, the WAN traffic. So, it is going to grow maybe six to nine times from now. till 2033 there is a position we have from Nokia Bell Labs and out of the total traffic in 2033 almost 30 % will be AI driven right. So 30 % traffic will be AI driven. It can be direct AI which can be slightly lesser but the indirect AI where you know once you use any application it drives you towards some other application and that increases your data. Maybe right now we have 5 % of the AI traffic it will go to 30 % in next 3 years.

Not 3 years I think the projection is by 2033 around 2033. So it might get to 30%. It is getting embedded to all our life faster than we have thought about.

Radhakant Das

So any of you would like to address this thing like how much of influencing would you like to see from the agency? Any of you would like to address this thing like how much of influencing would you like Like for example, of course, cloud has to do large part of it.

Surojeet Roy

Yeah, on that, what I can comment, maybe Sandeep and Rajiv can also add. So first thing is, you know, it really depends on the use case. Physical AI use cases like autonomous vehicles, robots, I think autonomous vehicles are definitely picking up in US and China. But if I look at India, I think it’s going to take some time because we don’t follow rules. You know, we have a bad habit of driving. You know, I think the AI models have to tune to understand how the drivers drive in India. Right. So I think autonomous vehicles will take some time. But those are the use cases, autonomous vehicles, industrial robots, maybe robotic surgeries, where you need much lower latency.

Those are the ones where the inferencing might be needed at the edge. But I think for normal consumers and normal use cases, we can still manage with the inferencing at the central location. Right. But the main problem is. having a centralized data center establishing that is a problem because I think the power consumption and the power requirements site infrastructure, those are a major challenge and that’s why we see a trend that the data centers are gradually moving towards the edge. Maybe not driven by only the use cases but maybe driven by the infrastructure.

Radhakant Das

Yeah, a lot of, a heavy dose of this data center related concerns were there for last four days in the summit. So Rajiv if I can come to you question for you is are you witnessing a shift from telcos as a connectivity providers to intelligence utilities and how does your organizations plan to deliver intelligence at the lowest cost?

Rajeev Saluja

Right, you know, so in the past decade like Ashok sir also mentioned was about democratizing the connectivity, right? Today more than 99 % of India’s population is connected by high speed broadband, right? The next decade is going to be about how can we democratize intelligence. So how can the last citizen of India have the strongest intelligence ecosystem built? That is the whole objective towards which we are working. And like our chairman said yesterday, you cannot rent intelligence. We cannot, as India, we cannot afford to rent intelligence. We need to build it. We need to scale it. And the complete infrastructure that we are building, we are building up from connectivity to the cloud, to the edge, and then the intelligence ecosystem on top.

So just to add to your previous question, we believe that most of the simple agentic and inferencing workloads will get handled at the edge. And only the multi -step, multi -agent, complex workflows. those are the ones which will get handled at the central location but our whole focus is how can we create an ecosystem an end -to -end ecosystem which can ensure all pervasive and an affordable intelligence to every citizen of this country that’s the whole focus

Radhakant Das

Thank you So Rajiv, what you are actually referring to is if we distribute the inferences and the processing across so the power requirement will get distributed and also we will not have a lot of concentration of power consumption and the data centers itself it’s a good thought so maybe Sandeep, we’ll just come to you how do you see AI and 6G anchor use cases can deliver the ROI within next, let’s say one and a half year from the India’s priority sectors such as BFSI, manufacturing, healthcare, mobility and how do you see that and how do you put the metrics as a success?

Sandeep Sharma

I think fairly good question honestly speaking and if you look at AI and 6G are two parallel things. They are going to merge but as on today we see lot of AI traffic is getting generated. Maybe it’s 6G or 5G or maybe on wireline. And the pattern is also evolving drastically the type of AI traffic that is running. So till now, till 2G, 3G, 4G we thought of only voice and data is the actual traffic for which network should be defined. But going further, depending on different type of use cases, different latencies use case need, network has to be defined for three parallel dimensions which is latency. Latency is there today in the network but we don’t take much of attention because most of the use cases are not latency sensitive.

The other thing is coverage. Coverage is equally important. reason being the uplink sensitivity of the traffic is getting more and more relevant in the AI type of traffic. And finally, one thing that we all should be aware of, the token economy is something which drives all the use cases. How much token you are going to consume, at what pace, at what latency, drives many of the use cases, efficiency or not. So if we bifurcate it from the industry to industry perspective, if you look at the industries which are more sensitive to delay, or maybe the robotic surgery, the hospital industry, and maybe the floor machines where robots are taking all the production control, their latency plays an important role.

So we should be using 6G -centric or the 5G -centric networks to realize as good as low latency, so that the tokens which are exchanged should be acknowledged well in time, and we have a faster time to resolve. And even we have observed that even if you reduce 10 to 20 % of latency, the efficiency improves drastically so it’s no more a network KPI it’s a productivity KPI for those use cases if we talk about the coverage perspective AI is going to be more uplink heavy more bursty and we need persistent traffic around it requests will keep on coming and that persistence in uplink will only be achieved if you have a good reliable coverage and the scenarios like when you have to do a lot of tracking of the assets lot of monitoring of the assets you need to have certain use cases realized on that those are the immediate use cases that industry will look at and finally all these AI specific things will only scale when you have some national framework around it you have certain national sandboxes around it so whatever is coming into the ecosystem it’s well tested across a diverse set of vendors diverse set of customers diverse set of ecosystem players because use cases for AI may not be related to the one use cases which we have seen so far so these three dimensions we should look at and once we look at the economics of the token then coming back to the question that you asked Rishabh where the influence should happen I think it’s not about only the where but at what cost so that defines how the influencing traffic will shape up

Radhakant Das

has also urged some of the industries to take over or adopt a couple of these labs. I think what you are suggesting as a part of sandbox on the applications, they are already happening. I think more the Department of Telecom and GOI should be working on that part. Okay, Surajit, we’ll come back to you again. Again, let’s say for the next four years, until 2030, how do you see the evolution, Surajit? And starting from the devices, use cases, traffic growth, and how do you see the impact of AI derived from the networks? One is tokens, the number of tokens, we’ll start using the KPI, which Sandeep has already mentioned about. So, what’s your opinion on that?

Yes. Surojeet Roy

Yeah, I think we touched upon it, I think, but just to be more specific about it, So the uplink traffic is going to see a significant increase. So currently we see a downlink to uplink ratio of maybe 10s to 1 or 12s to 1. I don’t know the exact number, but that’s the range we’re talking about. But with this AI applications, we are predicting that this pattern will change to maybe 4s to 1, 4 in the downlink and 1 in the uplink. So what it means is that you need much higher data rates in the uplink. Today the networks are sort of not built for that, which means there will be lots of enhancements required in the network.

This can come a bit from the 5G advanced, and then more enhancement will come when we go to 6G. There will be lots of improvement on the spectral efficiency in the uplink, and then using AI in the RAN. We can improve the coverage. I’ll give you some example how that can be done. So, for example, you know, the communication between the transmitter and receiver, it involves the signal received, the interference, the noise floor, right, and the scheduling. And there is lots of data, huge amount of data which is involved there. So, I think with AI using the deep learning algorithms, we can create, you know, some logics which can help optimize this entire communication. So, and then with AI, this communication can be adaptive as well.

So, we are talking about something called DeepRx, DeepTx, where Nokia is very much, you know, engaged. And we have done some initial proof of concepts. And using that, what we have seen is that even in an environment where you have the signal to noise ratio, which is much worse than what, for example, 5G can decipher. using AI you can actually decipher those signals and that can give a capacity increase maybe 25 -30 % and what you can also do is you can have higher order modulation supported. So this is going to increase the capacity of the network and then as I mentioned the multitude of devices which will require lots of low latency use cases, much higher capacity, we are talking about minimum 400 MHz of bandwidth when we are talking about 6G.

So today 5G networks are primarily running with say 100 MHz typical bandwidth. We are talking about 400 MHz of bandwidth which might be required and we are talking about 5 times spectral efficiency. So which means 5 into 4, you are talking about 20 times more capacity coming out from 6G networks. But I think this is an evolution, right? So we are doing the standalone networks right now and you know this voice over NR, slicing, this will… you know, get, I would say, advanced and will have the entire network having slicing capabilities, voice will transform to voice for NR and then gradually you go towards 6G where you will be building the networks which are more AI native.

Radhakant Das

Very interesting point you brought in, Surajit. So what you mentioned is, it’s very interesting, I didn’t think about it earlier. You were saying even a single digit designer, I can extract more information. Actually, we are going to improve Sagan’s principle. That’s a good aspect, right? Exactly. And also, one thing if you can just throw on, the tokens are smaller packets. You just have instructions and some questions. Why it should increase the opening bust? Ideally, it should not. There are a lot of popular talks like it is going to the 6Gs or the AI is going to reverse the traffic pattern. But why? Just tokens.

Surojeet Roy

So I think it depends on the, because you have to send the contextual information, right? For example, you are standing somewhere and you want to send a 360 degree view of where you are and you want to send it to the inferencing application so that it can help you, you know, understand whatever question you have. So I think that contextual information, sending it upwards, right, it will take lots of data requirement and primarily we are not doing it today. That’s the main reason because and this type of tasks will increase and that’s going to increase the uplink requirement.

Radhakant Das

Rajiv, do you want to add something to this?

Rajeev Saluja

No, the only thing which I wanted to add on the uplink side was that, you know, there are going to be multi -modal agents. So right now the traffic that we see is which consumer initiates, right, but when the agents and then on top multi -modal agents who are orchestrating end -to -end workflows, then they start initiating the traffic, that’s when the uplink also starts. So you will have multiple agents.

Radhakant Das

A2A traffic.

Rajeev Saluja

Yep.

Radhakant Das

Good. So Sandeep, the next question for you, there are a lot of AI6Z pilots are happening. I think whatever the organizations we have seen in last four days of AI Impact Summit, a lot of them are there. And what specific coordination mechanisms or co -creation models do you think we all should work together as industry, academia, government to ensure that these pilots, they align to the standards, they just don’t build on the silos while 6Z standard is maybe two quarters or maybe couple of more quarters away. So how we put the standardization as a perspective which can be adopted later stage. Also safety guidelines. A lot of safety issues will come. The more we are excited about how great things can happen, also the more of the things are exposed.

And we have seen the goals. We have seen the work, what it’s doing and how things are really getting into out of control. and so any AI maybe outcome based AI native deployment if you just can throw some light on that

Sandeep Sharma

Frankly speaking your question is so long I am not sure how long should be the answer I got it I got it just kidding so I think if you look at the perspective lot many good things are being done in the country there are lot many good organizations as we heard in the keynote that there is a Biosense there is a DSDA as well so lot of coordination is already in place the problem is with the pilot and the scale gap is not a technology gap is basically a gap of how we put things together in the frameworks which are scalable and referenceable as well so as I mentioned in my previous response that we need to have some national frameworks around AI native architectures once that is in place I think the quicker thing can be done is that let’s align the fundamental what type of use case are being driven and how the data needs to flow around it.

Other part is that India, as a consumer, we have a huge amount of data across the industries. And data is like bread and butter for AI. There’s no AI if there’s no data. But the data today is either siloed within the industries. If you look at the sector, they don’t combine the data together. So a national framework of putting the data together creating national exchanges where data can come in and people or organizations are allowed to train the data, train the models with the data. And we can have certain models which are more industry specific. And that plethora of variation of data, putting it together gives a very useful reference of creating frameworks which can be referenced or replicated, not only in India, globally as well.

And certain organizations are already in place to take care of that. I think more and more efforts, more and more programs are needed. thirdly if you look at more and more the safety guardrails I think we need to have certain framework in place how the AI is audited monitored within the telecom network as well we can’t allow some model to take a change of any parameter in live traffic if we can’t audit it maybe policy frameworks for intervention if certain models are changing in network parameter how and why they are changing certain explanation needs to be brought in and it can’t be done in isolation reason being if you do it in isolation again there is no clarity will come in to have a national policy around it will improve the reliability or explainability of the models hence people will come together rather than creating a differentiation of another layer of security that may encompass certain things that should be known to the larger audience and certain things that we all should do as an industry that let’s contribute more and more in these forums which government has started like Bharat 6G Alliance.

I am part of the 6G use case group, work very closely with Shokji and I think many things are already in place. We drafted certain white papers which could be referenced around AI, what type of implications it will bring into the network and we collaborate well with the 5G 100 labs as well. The things that we have done already, we should encourage them to take these things to the next level. Certain things could be referenced, certain things could be evolved. Not everything may be done right, but there is an opportunity to do everything right in 6G in terms of coordination, in terms of national referenceable frameworks. Good. Have I missed anything? It was a long question.

Radhakant Das

No, no, no. Thanks for that answer. So what actually we are seeing is in the security perspective, we are seeing we have… already a work is happening on interest in terms of policy perspective. These are in place and typically what if I have to bring in the DPI stuff or public and digital public infrastructure. So you have a lot of learning from all these sectors and how to deal with it. Even in the telcos, even in all the industry sectors, how to keep that as a creative, how that particular data which is not been seen in the telco ecosystems but still we are all responsible to deal with that. I think with the AI coming in, maybe some way we need to understand the DPI of DPI.

Sandeep Sharma

And just to add here, you brought a very good point. I think whenever I give a reference, the importance of open ecosystem, interoperable ecosystem, I always give an example of UPI. UPI wouldn’t have been a success if we had not promoted the open ecosystem around it. I think same mindset is needed in the AI era.

Radhakant Das

Good, good. Thanks. So Rajiv, I have the next question for you. how does the AI native telco change the way enterprises consume technology and what are the new value pools that will emerge out of it?

Rajeev Saluja

I think it’s a very good question see Sandeep brought out a very important aspect of latency and the second was on the uplink so first of all 6G is the solution to both the problems right enterprises in particular will benefit a lot from the advent of and the confluence of 6G and AI so there are three major drivers of value pools which enterprises can derive from 6G and AI the first would be demand analysis so you know they will be able to analyze what kind of demand is coming today their entire data is limited to the research that they do right But with the new data streams flowing in, they will be able to understand what new services can they provide to their customers and how can they embed intelligence into those new services that they are able to deliver.

The second important value pool which enterprise would be able to deliver is the workflow automation. So today, a lot of work which is manual will get automated. They will be able to orchestrate end -to -end workflows and humans will go up the value chain. That is the second important value which enterprises can derive from the confluence of 6G and AI. The third most important part which enterprises can derive out of 6G and AI is how can they make their end -to -end processes, how can they make their end -to -end security framework more robust. And see, till now, whenever we used to speak about digitization of enterprises, it used to stop at ERP implementation. Or basic business process automation.

Now, AI and 6G are going to take it to a completely different level. India is a wireless economy we don’t have fiber penetration in this country so the only way enterprises can go and reach to the last mile in this country is through the confluence of 6G and AI

Radhakant Das

so another extension to this question so how do you see sovereignty of the entire ecosystem we should deploy when you say sovereignty it is a very complex question one side that we think that ok if we make it open only it will grow at the same time sovereignty is also asked for every country maybe you can actually make entire European continent will only have one sovereign stuff what do you see on that and how much we should make it as a sovereign how much we should make it open what is your viewpoint on this

Rajeev Saluja

see this token economy in which we are going to go in the next 5 to 7 years so sovereignty is going to be a token sovereignty right in my point of view it will be very important for us to build our own intelligence and then deploy and scale it. We cannot be dependent on the world to deliver the intelligence to us because that will simply be too expensive for us to handle. So in order to make sure that intelligence reaches the last person in the most remote area and the last remote enterprise in India, the most important thing we need to have is have a token sovereign. We need to have a sovereign AI ecosystem, an end -to -end ecosystem starting from device to the cloud to the edge to the intelligence layers on top.

This end -to -end ecosystem has to be sovereign and we don’t have an option in this.

Radhakant Das

So you are saying end -to -end ecosystem platforms or stacks are sovereign?

Rajeev Saluja

Yes.

Radhakant Das

Token may or may not be sovereign or you can classify as a sovereign or as a general public one?

Rajeev Saluja

Correct, but we are basically calling it as a sovereign token. What I basically mean is that right from the time the request gets initiated by an agent or by a human. to the time an inference happens and the value gets delivered to the human or to the enterprise, this entire value chain has to be made in India, has to be sovereign.

Radhakant Das

So you have some view on it?

Sandeep Sharma

I think I was just supporting him with the gesture, but honestly speaking, the level of intelligence that country needs may not be a priority for the intelligence for other regions who are creating their own intelligence. So having a sovereign AI has an economy sense as well and has an importance for our own social values that we build in the system. AI is not only about telecom, AI is a bigger base. What we are getting as a query output as a new generation, they should be very well aware of what is right and that can be ascertained only if we have certain sovereignty developed in the ecosystem for our own nation.

Radhakant Das

So while we are talking about sovereignty, we should be very specific about it. so there is something which we need to keep as a there are certain things which you need to keep it as open stuff for learning from each other community learning across the country across the planet and all so that’s something would you like to comment and maybe I would request Surajit to also comment on that as well

Sandeep Sharma

I think just to start and Surajit will elaborate more the era of going further will not be a abstract one or abstract zero we need to look over the hybrid ecosystem which works best as a mix of which type of AIs as a mix of which type of compute and mix of which type of influencing and industry or the economy is going to be more use case and I would say efficiency driven so we should be leveraging which is best for to satisfy that particular use case

Radhakant Das

Surajit thank you

Surojeet Roy

Yeah, I think just to add, I was reading one Niti Aayog report, you know, where we are aiming for a 30 trillion economy by 2047 as part of the Vixit Bharat initiative. So out there it was very much mentioned that there are approximately 490 million informal users, you know, workers. So for example, all these carpenters, drivers, so they are the informal users and they are not yet equipped with all the applications which might enhance their productivity. So I think from that perspective, AI use cases can be significantly helpful out here. We can have, you know, smart robots working in the, you know, fields for helping on the agriculture. then there may be use cases where maybe an electrician or carpenter, you send a video of your work and they can, that AI can generate a list of the tools they need, what all steps they have to come prepared with, right?

But for all this, I think the most important part is the model needs to be trained based on data which is coming from India. Because if you train the models based on data which is coming outside India, then maybe it is not tuned for, you know, the India specific use cases. There will be a bias there. So I think that’s why it is important.

Radhakant Das

So the cultural perspective you are hitting upon, the cultural perspective has to be understood by us. So there is a bell. So are we going to have some questionnaire sessions, Q &A sessions? Any questions? Maybe we have just two minutes left.

Audience

Can you hear? Yeah, we can. In fact, I had a lot of questions. Okay, so let me first, my question would be around interoperability. So in mobile world, we see that whatever user equipment you buy from the market, it works on all the operators, right? When we are moving towards having AI -related applications, we see there is some problem. So I was looking at meta glasses they were exhibiting. So basically, the meta glass being, say, coming out in the market will only work with the meta. So should we not think of creating some AI? API sort of architecture wherein a product created by one. user side should work in different applications. It should work with Google.

It should work with geo -applications sort of. That’s the first question which I have. The second question is about the model training which Surjeet was trying to address. So I mean the advantage of India is like any applications we can scale to a billion users. That is one. And the second advantage is that we have a huge data set on many aspects. So how to leverage these two for AI because although we may not be good at LLMs, various LLMs which we have today. Of course new companies have started. Servum and all have started working on. But we are good at having a data and the market. So how to leverage that so that. I mean models are trained here models are utilized here so these are the two questions which I have in mind thank you.

Rajeev Saluja

Sir I will try to attempt to answer your question the first part I think Sandeep also mentioned see this entire ecosystem end -to -end ecosystem has to be open has to be API driven and loosely coupled so that you know there is no proprietary interface from one point to another so the whole work which is going on right now at least in our organization is to make sure that how this end -to -end ecosystem can become open can become efficient and can scale you brought out a second very important point about India’s scale right and this scale is going to reduce the cost of intelligence and that affordability is a also a very important factor for us to deliver value to our you know 140 million sorry 140 crore people that reduction in cost is a very very important factor The third important point which I want to make here is that when you talk of LLMs, they are important.

But delivering intelligence is not about LLMs or training LLMs. It is about delivering this entire ecosystem to the last mile. When it comes to LLMs, the way we are building intelligence in India is in every language. These models have to be trained. So every person, whether it is from the south, from Kerala, or from Assam, or from any state in the northeast, they should be able to get this intelligence in their local language made. That is the whole work we are doing right now as part of Jio.

Sandeep Sharma

Thank you, Rajiv. I think just to answer the second part of the question, there is a lot of data, how we can ensure. I think a framework of having is centralized data exchanges and centralized processes. Training exchanges. where enterprise can port their data with a certain anonymization so that no confidential data passed out but industries can come and train the models specific with the data which is available from the enterprises or from the end users within India. But I think central exchange mechanism is need to be placed.

Surojeet Roy

So just to add, I think democratizing the AI is also very important. It should be accessible to everybody at much lower cost and I think in that direction putting GPUs at cell towers can be one way of doing it because what you can do is when the network is not very busy and the resources are free, those resources can be given to the users to train their models or do some inferencing functions because those resources are there at every site. So that can be one way of helping on this direction.

Radhakant Das

Thank you. So you have another question? All right.

Audience

Morning. My name is Sidhu. I’m from AT &T. One quick question, now that Rajiv is also here. See, across the world, telecom companies are realizing that not having a network API exchange and then monetizing that is becoming a problem for many of the large enterprise customer use cases. For example, if a bank wants to understand their customer behavior, customers have got multiple networks, so they don’t get the visibility, right? So with OneEdge, I think Jio and Airtel have also joined hands last year. On the U.S. side, some work is happening, but I wanted to understand how much of this monetization of the network API -centric economy is materializing from India’s standpoint. I know Jio covers almost, I don’t know, 40%, 50 % of the overall population in India, so you might throw some.

I don’t know if you’re going to throw some. I don’t know if you’re going to throw some. I don’t know if you’re going to throw some. I don’t know if you’re going to throw some.

Rajeev Saluja

I will try to answer this quickly because the time is up and we can take this discussion offline. But see, we are committed to an open AI ecosystem to drive value. And like I said, enterprise value cannot be delivered unless the end -to -end ecosystem is open and connected. I think we are ringing the bell, but I will take this discussion offline. Thank you, sir. Thank you.

Radhakant Das

So we

Moderator

have time to stop. Any other questions that we can remind of now? Thank you. Thank you, everyone. May I request Radhika and Das to hand over the memento to all our speakers? May I also request Ashok, sir, to please come on stage and kindly collect your memento? Thank you. Thank you so much. Thank you. Thank you. Thank you. Thank you. Thank you.

Ashok Kumar

Speech speed

129 words per minute

Speech length

Evolution of 5G to 6G and AI Integration

Topics

Artificial intelligence

Radhakant Das

Speech speed

150 words per minute

Speech length

Evolution of 5G to 6G and AI Integration

Topics

The enabling environment for digital development

Surojeet Roy

Speech speed

151 words per minute

Speech length

Enterprise Value and Use Cases for AI + 6G

Topics

The digital economy | Artificial intelligence

Rajeev Saluja

Speech speed

163 words per minute

Speech length

Building a Sovereign, Democratized AI Ecosystem in India

Topics

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

Sandeep Sharma

Speech speed

165 words per minute

Speech length

Coordination, Standardisation and Interoperability Across Stakeholders

Topics

Internet governance | The enabling environment for digital development

Audience

Speech speed

152 words per minute

Speech length

Data Sharing, Model Training Localisation and Cultural Relevance

Topics

Data governance | Artificial intelligence

Moderator

Speech speed

42 words per minute

Speech length

258 words

Speech time

364 seconds

Strategic opportunity for India in AI‑6G

Explanation

The moderator frames the session as a historic chance for India to shift from a technology consumer to a global leader in AI‑enabled connectivity.

Evidence

“opportunity, ensuring that India moves from being a consumer of global technology cycles to becoming a sharper of the world’s next intelligence and connectivity frontier.” [153]

Major discussion point

Evolution of 5G to 6G and AI Integration

Topics

The enabling environment for digital development | Artificial intelligence

Agreements

Agreement points

AI is natively integrated into 6G design from the beginning, unlike previous generations

Speakers

– Ashok Kumar
– Radhakant Das

Arguments

AI integration was an afterthought in 5G but is native to 6G design from the beginning

6G will emerge as a distributed computer fabric platform enabling intelligence everywhere across radio, core, edge, satellite, and sensor ecosystems

Summary

Both speakers agree that 6G represents a fundamental shift where AI is embedded from the design phase rather than added later, creating an intelligent distributed computing platform

Topics

Artificial intelligence | Information and communication technologies for development

Distributed inferencing across network tiers is essential for 6G and AI convergence

Speakers

– Surojeet Roy
– Rajeev Saluja
– Radhakant Das

Arguments

Multiple AI-enabled devices per user will require distributed inferencing across network tiers

Simple inferencing workloads will be handled at edge, complex workflows at central locations

AI inferencing will be distributed across multiple tiers including devices, RAN, core networks, and cloud

Summary

All three speakers agree that AI processing must be distributed across different network layers, with simple tasks at the edge and complex workflows centralized

Topics

Artificial intelligence | Information and communication technologies for development

Uplink traffic patterns will fundamentally change due to AI applications

Speakers

– Surojeet Roy
– Rajeev Saluja
– Sandeep Sharma

Arguments

Arguments

490 million informal workers can benefit from AI applications to enhance productivity

Next decade focused on democratizing intelligence after achieving connectivity democratization

Summary

Both speakers see AI democratization as the next phase after connectivity democratization, focusing on reaching India’s large informal workforce

Topics

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

The digital economy | Social and economic development | Artificial intelligence

Artificial intelligence | Internet governance | The enabling environment for digital development

Overall assessment

Summary

Strong consensus on AI-native 6G design, distributed computing architecture, need for sovereignty balanced with openness, and democratization of intelligence access. Speakers agree on fundamental shifts in traffic patterns, the importance of India-specific AI models, and the need for comprehensive government-industry collaboration.

Consensus level

High level of consensus across technical, policy, and strategic dimensions. The alignment suggests a mature understanding of the challenges and opportunities in 6G and AI convergence. This consensus provides a strong foundation for coordinated development efforts and indicates that stakeholders have moved beyond basic concepts to implementation strategies. The implications are positive for India’s 6G and AI development trajectory, suggesting unified direction among government, industry, and technical experts.

Differences

Different viewpoints

Degree of sovereignty vs. openness in AI ecosystem architecture

Speakers

– Rajeev Saluja
– Sandeep Sharma

Arguments

End-to-end sovereign AI ecosystem needed from device to cloud for token sovereignty

Hybrid approach needed balancing sovereignty with open collaboration for community learning

Summary

Saluja advocates for complete end-to-end sovereignty across the entire AI value chain from devices to cloud, emphasizing that India cannot afford to rent intelligence. Sharma supports a more balanced hybrid approach that combines sovereign capabilities with open collaboration for community learning, citing UPI’s success as an example of open ecosystem benefits.

Topics

Artificial intelligence | Data governance | The enabling environment for digital development

Primary drivers of edge computing deployment

Speakers

– Surojeet Roy
– Rajeev Saluja

Arguments

Power consumption concerns are driving data centers toward edge deployment

Simple inferencing workloads will be handled at edge, complex workflows at central locations

Summary

Roy emphasizes that infrastructure challenges, particularly power consumption and site requirements, are the main factors pushing data centers toward edge locations, suggesting it’s driven by practical constraints rather than use case requirements. Saluja focuses on use case-driven architecture where edge handles simple tasks while centralized locations handle complex workflows, implying a more strategic rather than constraint-driven approach.

Topics

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

Unexpected differences

Infrastructure vs. use case drivers for edge computing

Speakers

– Surojeet Roy
– Rajeev Saluja

Arguments

Power consumption concerns are driving data centers toward edge deployment

Simple inferencing workloads will be handled at edge, complex workflows at central locations

Explanation

This disagreement is unexpected because both speakers are discussing the same technological trend (edge computing) but attribute it to fundamentally different drivers. Roy suggests infrastructure limitations are forcing the move to edge, while Saluja presents it as a strategic architectural choice based on workload characteristics. This reveals different perspectives on whether edge computing is a constraint-driven necessity or a design optimization.

Topics

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

Overall assessment

Summary

The discussion shows relatively low levels of direct disagreement, with most tensions arising around the balance between sovereignty and openness in AI development, and different perspectives on the drivers of technological architecture decisions. The speakers generally align on the vision of AI-native 6G networks and India’s need for indigenous capabilities.

Disagreement level

Low to moderate disagreement level. The disagreements are more about emphasis and approach rather than fundamental opposition to goals. This suggests a generally collaborative environment where stakeholders share similar objectives but may have different strategies for implementation. The implications are positive for policy development as there appears to be broad consensus on key objectives with room for incorporating different approaches in implementation strategies.

Partial agreements

Both speakers agree on the need for open, interoperable ecosystems and cite UPI as a successful model. However, they differ on the balance between sovereignty and openness – Saluja emphasizes maintaining sovereign control while building open systems, while Sharma advocates for a more hybrid approach that balances sovereignty with collaborative learning from global sources.

Speakers

– Rajeev Saluja
– Sandeep Sharma

Arguments

Open, API-driven, loosely coupled ecosystem required while maintaining sovereignty

Importance of open ecosystem and interoperability, drawing parallels to UPI success model

Topics

Artificial intelligence | Internet governance | The enabling environment for digital development

Both speakers agree that India needs its own data for training AI models to serve local contexts effectively. However, they propose different mechanisms – Roy emphasizes the importance of India-specific training data to avoid bias, while Sharma focuses on creating centralized data exchanges with proper anonymization frameworks to enable this training at scale.

Speakers

– Surojeet Roy
– Sandeep Sharma

Arguments

Models must be trained on India-specific data to avoid bias and serve local use cases effectively

Centralized data exchanges required for enterprise model training with proper anonymization

Topics

Data governance | Artificial intelligence | The enabling environment for digital development

The digital economy | Social and economic development | Artificial intelligence

Takeaways

Key takeaways

6G represents a paradigm shift from 5G with AI natively integrated from design phase rather than as an afterthought, featuring ubiquitous intelligence embedded in every network element

India is positioning itself to move from being a consumer of global technology to a shaper of next-generation intelligence and connectivity, with government supporting 100+ 6G research projects and 100 5G labs across institutes

AI will fundamentally change network traffic patterns by 2033, driving 30% of total traffic and reversing uplink-downlink ratios from 10:1 to 4:1 due to contextual data requirements and agent-to-agent communications

The next decade will focus on democratizing intelligence after achieving connectivity democratization, requiring end-to-end sovereign AI ecosystems that cannot be rented from external sources

6G will deliver 20 times more capacity than 5G through 400 MHz bandwidth and 5x spectral efficiency improvements, with AI enabling 25-30% additional capacity gains through deep learning algorithms

Enterprise value creation will emerge through three key areas: demand analysis with new data streams, workflow automation replacing manual processes, and enhanced security frameworks

Distributed intelligence architecture will handle simple inferencing at edge locations while complex multi-agent workflows remain centralized, driven by latency requirements and power consumption concerns

India’s scale advantage and diverse datasets provide opportunities to reduce intelligence costs and train models for local use cases, languages, and cultural contexts

Resolutions and action items

Government to continue supporting startups in 3GPP standards participation through TSDSI membership at reduced costs (₹10,000 vs ₹5-6 lakh)

Department of Telecom working with ANRF to develop end-to-end systems based on Release 18 evolving to Release 21 (first 6G release), expected within next two quarters

Industry encouraged to adopt 5G labs for collaborative 6G research and development work

Bharat 6G Alliance working groups to continue providing policy recommendations to government on technology, spectrum, and devices

Need to establish national frameworks for AI-native architectures and centralized data exchanges for enterprise model training with proper anonymization

Telecom sector to be included in DST’s RDI (Research, Development, and Innovation) scheme for scaling developed technologies

Unresolved issues

Specific distribution percentages of AI inferencing across device, RAN, core, and cloud tiers remain undefined and use-case dependent

Exact timeline for 6G commercial deployment and Release 21 standards completion unclear beyond ‘next two quarters’ estimate

Interoperability challenges for AI applications across different platforms and providers (e.g., Meta glasses working only with Meta ecosystem)

Network API monetization strategies and cross-operator collaboration mechanisms still developing

Power consumption and infrastructure requirements for distributed edge computing deployment not fully addressed

Regulatory frameworks for AI auditing, monitoring, and intervention in live telecom networks need development

Balance between sovereignty requirements and open ecosystem collaboration needs further definition

Specific metrics and success criteria for ROI measurement in AI and 6G implementations across different industry sectors

Suggested compromises

Hybrid ecosystem approach balancing sovereign AI capabilities with open collaboration for community learning and global standards participation

Gradual evolution from 5G Advanced to 6G rather than complete technology replacement, allowing for incremental deployment and learning

Open, API-driven, loosely coupled architecture that maintains sovereignty while enabling interoperability across platforms and providers

Centralized data exchanges with anonymization protocols to enable enterprise model training while protecting confidential information

This is a novel architectural insight that proposes using existing telecom infrastructure for distributed AI computing. It’s innovative because it suggests repurposing network resources during off-peak times for AI democratization, addressing both cost and accessibility challenges.

Impact

This comment introduced a creative solution that bridges infrastructure efficiency with AI accessibility. It demonstrated how 6G and AI integration could create new value propositions and business models, influencing the discussion toward practical implementation strategies.

Overall assessment

Explanation

This is critical for ensuring safe and controlled deployment of AI systems in critical network infrastructure

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.

Designing Indias Digital Future AI at the Core 6G at the Edge

Summary

Keypoints

Major Discussion Points:

Overall Purpose:

Overall Tone:

Speakers

Fundamental Paradigm Shift: From Connectivity to Intelligence

Government Initiatives and Strategic Framework

Traffic Pattern Revolution and Infrastructure Implications

Distributed Intelligence Architecture and Edge Computing

Enterprise Transformation and Economic Implications

The Token Economy and Sovereignty Debate

Cultural Adaptation and Social Context

Technical Innovations and Capabilities

Implementation Challenges and Open Questions

Future Roadmap and Next Steps

Economic Vision and Global Positioning

Conclusion

AI‑native 6G design

Explanation

Evidence

Major discussion point

Topics

Government 6G research programmes and ecosystem support

Explanation

Evidence

Major discussion point

Topics

Transition from 5G to AI‑enabled 6G

Explanation

Evidence

Major discussion point

Topics

AI‑native 6G design

Explanation

Evidence

Major discussion point

Topics

Energy‑efficient AI and semantic communications

Explanation

Evidence

Major discussion point

Topics

Government support and alliance

Explanation

Evidence

Major discussion point

Topics

Uplink traffic shift and edge inferencing

Explanation

Evidence

Major discussion point

Topics

Capacity boost via AI‑driven RAN optimisation (DeepRx/DeepTx)

Explanation

Evidence

Major discussion point

Topics

AI traffic projection ~30 % by 2033

Explanation

Evidence

Major discussion point

Topics

Priority sectors and edge vs cloud inference

Explanation

Evidence

Major discussion point

Topics

Sovereign end‑to‑end AI ecosystem and token sovereignty

Explanation

Evidence

Major discussion point

Topics

Open API‑driven ecosystem model

Explanation

Evidence

Major discussion point

Topics

Enterprise value: workflow automation and new value pools

AI traffic projection ~30 % by 2033