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Ladies and gentlemen, I thank our elite panelists who were a part of this ministerial conversation. Her Excellency, Ms. Togo, His Excellency, Nizar Patria, His Excellency, Rafat Hindi, Honorable Ministers from Togo, from Indonesia, and from Egypt, and I thank Ms. Debjani Kosh for moderating this ministerial conversation. And now I would like to invite Mr. Lars Recher, Executive Vice President and Chief Technology Officer, NXP Semiconductors. As we all know, artificial intelligence runs on chips, and Lars Recher is at the frontier of designing the semiconductors that will power the next generation of edge AI. In cars, in medical devices, in industrial systems. NXP’s work on secure, efficient, real -world AI hardware is essential to everything on the stage.

Ladies and gentlemen, please welcome the Chief Technology Officer of NXP Semiconductors, Mr. Lars Reger.

Namaste. Hello everyone and thanks for having me here. When we are talking about AI, at the moment there is a lot of talk about how do we pump AI in big data centers, how are we energizing these big data centers, but very honestly, there is a lot of questions. What is this AI for? What is this AI at all doing? And if I’m looking at my own lifespan, I’m coming from an analog world, was born in the 1970s. Then there was some heavy digitization in there over the last 20 years, when someone stuffed a laptop into… into a mobile phone and they called it smartphone. So we had a data display device. We could run topics that were on demand.

So on demand, I need a pizza, I need an Uber, I need to switch on the climate control in my house. And now my Marcom people would say, Lars, we are entering a phase of the world that anticipates and automates. And this little world that anticipates and automates is driving megatrends around us. And these megatrends are unchanged over the last 15 years. We have demographics changes. We have infrastructure upgrades. We have supply chain constraints. We have renewable and we have energy constraints. So out of all of these drivers, what is this modern world that anticipates and automates able to do for us? Well, jumping forward maybe 20 years, how is the cocoon that I’m living in going to look like?

I will have a shelter. I will have my house, and that house is totally barrier -free. That house will check about my health, my wealth, will protect me. I can enter and I can live. I can live. without touching anything. No one else can do the same and my property is protected very seamlessly. No barriers for me, but maximum safety and security. How will be my manufacturing landscape look like? Well, most of the manual tasks are gone. I need better education and I may be the most advanced equipment operator in the world. Look at airplane pilots 70 years ago. They were guys my size. These type of muscles and arms were flying in thunderstorms, real heroes, mechanical pilots.

Today we have more pilots, but they are all genders, shapes and sizes because they are operating flying intelligent robots. So when I come from Germany here to India, a pilot, mechanically, I’m not a pilot. has to work for 30 seconds at the end of the runway, pull up the plane, and all the rest is happening already today autonomously. And that’s going to get better in the industrial world. And of course, also in the transportation world. How are cars going to look like in 20 years? Well, they are rolling cocoons, rolling robots, and these cars are rolling living rooms. You have seen this during the COVID pandemics in China, for example. A lot of people use their cars as their office extensions.

Too many people in the house at home, the kids were too noisy. You go to these type of places, so you have a rolling cocoon again that is anticipating and automating what you want to do, what you want to achieve. And what does this all have in common? I mean, most of the people are asking me now, okay, Lars, nice. You are predicting that there is 50 billion of these smart connected robots out there in 10 years from now. But they have so different form factors. What does that mean? Well, simple answer. They have all the same ingredients. Each of these little robots has to sense its environment. So what’s happening around me? Has to connect to the cloud to get the data.

Last ones to drive from here to Mumbai, how is the traffic situation? Getting the information from the web. And then you start thinking of a smart advice. This is where AI comes into the play. At that moment, you have to think of what is my best advice to the arms and legs to my robot. And whether these arms and legs are an automotive powertrain and a steering wheel, is a manufacturing arm, or is the wireless connection to my climate control from my smart thermostat. I don’t care. Sense, think, connect, act are the ingredients for every of these 50 billion robots. Now, the only thing is, that all is nothing if you cannot trust. Because if your fridge starts ordering 500 liters of milk for the next weekend, you go shopping alone if your car does erratic driving you start driving manually again and if your thermostat sets your house on 50 degrees centigrade and your flowers are dried out and your cat is dead you go organizing it all manually again so trust is the essence and how does a nerd like me define trust that’s very simple this is functional safety like in automotive make sure that your braking system never ever fails and make sure that your connected device your car or whatsoever is never ever being hacked and then you can trust your device you can be sure that it doesn’t turn against you so these underlying levels make sure that you are energy efficient because otherwise you cannot be battery powered make sure that you are trustworthy so safe and secure you and then make sure that you can sense, think, connect, and act.

And you can build every robot in the world. And that is, of course, interesting for a semiconductor maker because for us, volume matters in these semiconductor chips. Now, you will ask me, but Lars, we have so long already these discussions on autonomous vehicles. In 2018, the entire press community thought, in 2020, my kids are going to the kindergarten without a steering wheel and without me, autonomously. That did not happen. Why? Because we have designed these robots wrong. And how do you design the robots right? Well, try to copy from nature. That normally works. And here on stage is a 90 -kilo bag of water with a couple of bones, or in other words, a biological robot. And that robot has a certain architecture.

That robot has different layers. That robot has a real -time system, highly functional safety, and that is my spine. And that is my spine. and if I stumble, the reflexes in my spine tell me already straighten your leg. In real time, highly undisturbed, very, very fast. No AI, not big AI, very deterministic system. Then I have in green my cerebellum that is working also in a highly functional, safe environment for heartbeat, stomach control, stability control. I can stand here and stand in a stable way because only the blue part is trying to find out what is the next sentence that I’m firing towards you. And green and orange are working to manage the infrastructure in a functional, safe way that is standing here in front of you.

So why don’t we copy these approaches into vehicles, into cars, into houses, into planes again? Well, there are simple architectural constraints and building mechanisms. There are building blocks that we need and we need to scale. But how big does the AI really have to be? So that AI in these systems. can be comparatively tiny. If you’re talking about transportation robots and how these transportation robots should look like, well, look at intelligent transportation robots, insects, for example. These insects have 100 ,000 neurons and an ant is already a very, very nice, very sexy transportation device. It’s not as intelligent as a human being with 90 billion neurons, but for most of the tasks, it is also not needed in this way.

And Ashwini Vishnath said it very nicely in Davos. 80 % of the AI tasks around us will be on very tiny, efficient, and very, very tailor -made models at the famous edge, so in the end devices. And this is what we are designing for. So in other words, NXP is trying to build all these Lego blocks where you can start scaling small, medium, and large devices. You have these devices here. This is, for example, sorry, very small devices. This is a complete drone control unit. And this is a complete drone control unit. that also flies with AI, artificial intelligence, and reaches targets, not only remote control, but is operating the entire drone and is finding via the camera its way.

What I have here is an India -made AI accelerator from Kinara and Hyderabad that NXP has acquired. This is carrying 10 billion parameters in a large language model. So it is not as big as JetGPT. But the combination of those two systems carries a large language model and operates an intelligent system at the edge for a power consumption of 7 watts. So in other words, you can build these type of plug -in combinations and you have a system, for example, at a computer tomograph that is taking my entire X -ray pictures and is writing the doctoral report that is operating at my fridge and tells me how many bottles of milk are missing. you do not have to have it always on and always operational so these seven watts are only consumed the moment where the fridge tries to find out what is missing and then you can go to sleep again that is the answer for this global quest of how many nuclear power plants do we need when we send one question to chat gpt and that is what the edge is going to solve for all of us but beyond all of that we are always talking about ai and the brain structure of these robots most of the cars that have created fatalities in the last 10 years these autonomous vehicles didn’t create these fatalities on the roads because they had a bug in the brain structure they created these issues because they were more short -sighted than i so wouldn’t it be great to have these robots with superhero senses wouldn’t it be great if these robots out there would have telepathic capabilities?

I do not need to touch anything, but the stuff around me is arranging, like Dumbledore. One move of the magic wand, everything is arranged. Wouldn’t it be great to know what is ahead of your line of sight, like Yoda, telepathy? Wouldn’t it be great to have X -ray vision like Superman? You look in rain, in snow, and in fog what is around you. Wouldn’t it be great for the very old ones amongst us to be like in Hitchhiker’s Guide through Galaxy? You have one bubble fish that you plug into your ear and you understand the entire universe, every language that is spoken. A German can understand Hindi without a big barrier in between. And wouldn’t it be great if our robots would have better ears than Daredevil or an owl in real life and would be able to hear what is being spoken out there in the outer ranks?

If we would have that. then the driving robot that replaces Lars is way better than Lars the driver himself. But I am the entry ticket for driving 250 kilometers an hour on the left lane of a German highway with my car. Now, you think we cannot have that for our robots next to this little bit of AI that we need? Well, let me tell you, we have it already. We have ultra wideband technology that is opening gates and car keys from my watch to everything around me. I have car to car communication over more than one mile of distance in three milliseconds. I can immediately tell the device there is an ambulance rushing into the crossroads, switch the traffic lights to green for that ambulance and to red for me.

Telepathy. We have radar systems over 300 meters that see two persons sitting like you next to each other. And we can detect. I’m in rain and snow and in focus. We have meta standards. So the English for smart connected devices. There is a common language in place and all devices are talking to each other. The home gateway is talking to the window blinders, is talking to the solar cell management. This is the entry tickets for this democratization of AI functionality and for the entrance of these tiny devices here with a little bit of AI, a lot of functional, safe and secure architectures to building the right devices. And what we have done with a little bit of AI and a couple of microphones in cars, we can take the in -car microphones, the sound in a way that we hear a bicycle bell behind the cars.

And we can easily detect whether there is vulnerable road users, for example, behind the cars. We can do this in any other settings as well. But automotive is there a very nice one. So in other words, where are we at? At the moment, when I’m talking to my fellow nerds and the and the semiconductor researchers. it is not about ai alone it is how you can build systems that you absolutely can trust how can you go low power and then the key question how big does the brain has to be and the answer is somewhere between a hundred thousand and a hundred billion neurons beyond all of that there is very very interesting questions that we have to solve and where india is deeply with the europeans in research and in the activities how do we make the wiring harnesses how do we battery operate all of that how we are sensing in the right way how do we think so all of these separate topics and to not make it too nerdy and too complex all of these silicons here are driving then these form factors a lot of people are only talking about humanoids and sorry to say humanoids are the tiny fraction of robots because why should a robot look like a human being I mean, that only makes sense in a very human environment, climbing stairs or whatsoever.

Otherwise, you have robots that are looking like ultrasonic devices, that are looking like infant monitor devices on neonatology stations in hospitals. There is no need to look like me. But all of this, we are equipping already with silicon in the hundred thousands today, and the ingredients are always the same. And just to get this pitch here down on the runway, to say it in the drone language, what do you need to do? What do we need to work on? What does the industry do at the moment? Well, in a very simple way, we are working on safe and secure architectures that are ultra low power, ultra energy efficient. Again, otherwise, this dream of 50 billion smartphones.

Connected devices will not work. because these 50 billion smart connected devices need three times the energy that Mother Earth can provide. So that is the absolute must for these markets to come into play. Then what we need to work on is we have to push the boundaries, the envelope of physics, and we are doing. We are sensing better than human beings in the meantime. And then what we need to do is just a simple game that semiconductors have done since 50 years now. We need to scale in the right way. So we need to build these little Lego bricks and say, okay, here is a complete drone control unit that you can fly autonomously. You want to fly with large language models and very, very smart AI slalom between the trees.

Plug this little dongle in, and you have everything on board that you can do. And the same you can do for building control systems with manuals. You can do this for any form factor that you like. And that is what we are doing at the moment. while the AI models are getting much, much more efficient, smaller, and we carry them here. So my pitch is, when PM Modi says he wants to bring AI to everyone, this is the answer. The answer is not data centers. They will exist. But the democratization of AI and equipping everyone in Togo, as we heard earlier, or in India, or in Germany, with the right levels of AI that create the world that anticipate and automates, the answer lies at the famous edge in the end device.

Thank you.

also for contributing your expertise to this summit. Ladies and gentlemen, I now take this opportunity to invite Professor Surya Ganguly, Professor of AI, Neuroscience and Physics, Stanford University. Professor Ganguly’s research sits at one of the most intellectually fertile intersections in science today. Using the mathematics of physics and the insights of neuroscience to understand how intelligence, biological and artificial intelligence, actually works. His work is helping build the theoretical foundations that practice so urgently needs. Please welcome Professor Surya Ganguly from Stanford University.

Thank you. Great, we got the slides. So we went from a world leader to a VC to now a professor now. So we have a little bit of a change of pace. It’s going to get a little bit more technical. And because I’m a professor, there’s going to be an exam at the end. All right, so pay attention. All right, so I’m going to talk about advancing the science and engineering of intelligence. So, the last decade of AI research has led to stunning advances in the engineering of intelligence, yielding AI systems that stand poised to transform our society. Yet, alarmingly, we understand almost nothing about how they work, and we desperately need to. At the same time, our brain is the product of 500 million years of vertebrate brain evolution, and it is still orders of magnitude better than AI along several axes, and we also need to understand why.

So, I work in a unified science of intelligence across both brains and machines that seeks to both understand biological and artificial intelligence and create more efficient, explainable, and powerful AI. Today, I’ll work on understanding and improving intelligence along three lines. Data efficiency, energy efficiency, and melding brains and machines. First, data efficiency. so um ai is vastly more data hungry than humans we get about 100 million words of language experience ai gets 10 trillion it would take us 240 000 years to read everything that ai read so why is ai so data hungry well well in ai error falls off as a power law it falls off very slowly as a power law with the amount of data this is an example of a famous neural scaling law which captured the imagination of industry and motivated significant societal investments in data collection compute and energy but despite the importance of these neural scaling laws discovered over half a decade ago we lack any scientific theory for why they exist for any modern large language model and why they are so slow just last week we posted the first theory to do so from first principles, we could analytically predict the slope of these neural scaling laws and reconnected their shallow slope to the weak surface statistical structure of natural language itself.

The black line is our theory and the colored lines are experiments in modern LLMs. You can see there’s a good match. But can we make the scaling laws better? We actually can. We actually showed, both in theory and practice, that we can bend the slow power law down to a much faster exponential drop. The key idea is that large random data sets are extremely redundant. If you already have a billion random sentences, it’s unlikely that the next sentence is going to tell you very much that’s new. But what if you could find a non -redundant training set in which each new data point is carefully chosen to tell you something new compared to all the other data points?

We developed theory and algorithms to do just this, and that’s what got us the better exponential. In a completely different line of work, we asked if the process of evolution itself could speed up learning. And we showed it actually can. We evolved robot morphologies, shapes of bodies, from generation to generation. And we showed that successive generations could learn faster. They did so by designing the body to be easier to learn to control. This is an example of something called the morphological Baldwin effect. It’s an effect that has long been conjectured in evolutionary theory, but hard to test in the real world. We demonstrated it for the first time in our simulations. Okay, let’s go on to energy efficiency.

AI is vastly more energy hungry than humans. Our brain only spends 20 watts of power, but modern AI can consume 10 million watts. So why is AI so energy hungry? Well, the fault lies in the choice of digital computation itself, where we use very fast and reliable bit flips at every intermediate step of the computation. Now the laws of thermodynamics demand that every fast and reliable bit flip must consume a lot of energy. Biology chose a very different route. It gets the right answer just in time using the slowest, most unreliable intermediate steps possible. Biology does not rev its engine any more than it needs to. It also co -designs computation and physics much better. For example, it directly uses Maxwell’s equations of electromagnetism to do addition, instead of using complex energy -hungry transistor circuits.

So biology matches its computation directly to the native physics of the universe. So to bridge the vast energy gap between brains and machines, we need to rethink our entire technology stack. . from electrons to algorithms, and optimally match computational dynamics to physical dynamics. For example, given a particular computation, what are the fundamental limits on its speed and accuracy under energy constraints? We recently solved this question for the computation of sensing, which every cell has to do. We found fundamental limits on the lowest achievable error achieved by any chemical computer whatsoever. That’s the red curve. And we also found the family of optimal computers that hug this curve. And we showed, remarkably, that these optimal chemical computers behave a lot like something called G -protein coupled receptors, which hide in every single cell, and they do sensing.

So this yields a connection between what neurons do and what optimal physical sensors would do. Popping up a level, in neuroscience, we can now measure non -neural sensors. We can now measure non -neural sensors. We can now measure non -neural sensors. We can now measure not only neural activity, but also energy consumption in the form of ATP usage. the fundamental chemical fuel that powers all life’s processes. We can do this across the entire fly brain. So by analyzing the couple dynamics of neural computation and energy consumption, we discovered that the brain actually works like a smart energy grid, remarkably. The brain can predict where and when energy will be needed in the future, and it produces just the right amount of energy at just the right time, at just the right location.

So in summary, we still have a lot to learn from evolution in our quest to build more energy -efficient AI, but we don’t have to be limited by evolution. We can go beyond evolution to instantiate neural algorithms in quantum hardware that evolution could not discover. For example, we can replace individual neurons with individual atoms. A neuron in different states of firing correspond to atoms in different excited electronic states. So we can do this with the help of neural networks. We can also replace individual synapses between neurons with photons, quanta of light. Just as synapses allow two neurons to communicate, photons allow electronic states of atoms to communicate through photon emission and absorption. So what can we build with this?

As one example, we could build a Hopfield associative memory network. This is the same network that recently won John Hopfield the Nobel Prize in physics. But this is a quantum version this time that can be built with atoms and photons. And we can show that the quantum dynamics endows the memory with superior capacity, robustness, and recall. We can also go beyond this to build quantum optimizers made entirely out of photons. These photonic computers solve optimization problems in interesting new ways, and we can analyze their energy landscape. So the marriage of neurons… …and neural algorithms with quantum hardware leads to an entirely new field that I like to call quantum neuromorphic computing. okay now returning to the brain the marriage of neuroscience and ai enables a powerful new path forward by melding minds and machines as follows imagine a scenario where we read lots and lots of neural activity from the brain then we use ai to build a model or a digital twin of brain circuits then we can do rapid in silico experiments on the digital twin and use explainable ai to understand how it works but we don’t have to stop there we can control the brain too we can use control theory to learn specific neural patterns that we can write into the digital twin to control it then we can transfer these same neural patterns into the actual brain to write into the brain and control the brain in essence we can learn the language of the brain and then speak directly back to it in its own neural language you So, as one example of this program, we recently developed the world’s most accurate digital twin of the biological retina, and we used explainable AI to understand it.

And in Silico, we could reproduce two decades’ worth of experiments in a matter of days. So this shows a general path forward to dramatically accelerating neuroscience discovery using AI. We also carried out this program in mice, where we were able to use AI to read the mind of a mouse. We could look directly at neural activity in the brain of a mouse, and we could decode what it was seeing at the lower level of resolution that mice can see. This shows that we can learn the native language of the visual brain. But we can go further than that to write to the mind of a mouse. By writing in carefully designed neural activity patterns, we could make the mouse hallucinate a particular percept.

In fact, we could control the mouse brain’s soul. We could even tell it to do this. We could even tell it to do this. We could even tell it to do this. We could even tell it to do this. We could even tell it to do this. We could even tell it to do this. We could even tell it to do this. We could even tell it to do this. We could even tell it to do this. We could even tell it to do this. We could even tell it to do this. So in essence, we could control what the mouse saw by writing directly into the brain using the native language of its brain itself.

We also applied this to epilepsy. Sorry, we also carried out this program in epilepsy where we built a digital twin of the epileptic brain. Our twin could reproduce actual epileptic seizure dynamics across the entire brain. We then used explainable AI to understand how these seizures were starting. Then we used control theory to be able to control the seizure amplitude in the digital twin. Then we injected these same control signals into the actual brain and controlled seizure amplitude in the actual brain. This shows how to meld brains and machines to control epilepsy. Building on all this, we’re actually creating a new startup called Metamorphic. And we’re going to be using this to control the seizure amplitude in the digital twin.

It will work closely with the Enigma project at Stanford University and together Enigma and Metamorphic. will scale up the construction of digital twins to encompass the entire primate brain, starting with the visual brain. Such scaled -up digital twins offer a powerful path forward to building robust biohybrid AI systems that are taught directly by brain data and to treat brain disease in new AI -driven ways. More generally, the possibilities of melding brains and machines are limitless, both to advance AI and to understand, cure, and augment the brain. To close, what I think we really need is a unified science of intelligence that spans both brains and machines to help us understand both biological and artificial intelligence and create more efficient, explainable, and powerful AI.

Importantly, this pursuit must be done out in the open and shared with the world, and it must be done in a way that is both biological and artificial. It must be done with a long time horizon. This makes academia an ideal place to pursue a science of intelligence, and I believe it’s imperative to expand public investment in the academic study of intelligence, because the academic studies of yesterday laid the strong foundation for today’s AI technology, and it will be the academic studies of today that lay the foundation for tomorrow’s technology, enabling us to go beyond large language models and diffusion models and so forth. Despite the huge and exciting advances happening now increasingly, unfortunately, behind closed doors at companies, I’m extremely excited about what the science of intelligence can achieve out in the open for the public benefit of all.

Thank you.

Thank you so much, Professor Ganguly.

Thank you so much, sir. Your reflections on artificial intelligence and its use in overcoming the global challenges has really elevated this summit. Thank you so much. We are deeply grateful for your valuable contribution. Ladies and gentlemen, I would like to now invite Mr. Bejil Somaiya, Managing Director, Lightspeed Venture Partners, one of the most respected technology investors in Asia. Mr. Somaiya has backed some of India’s most consequential startups from the earliest stages. His view of where AI investment is going and which bets are likely to pay off is one of the most grounded in the room. Please welcome the Managing Director of Lightspeed Venture Partners, Mr. Bejil Somaiya. Thank you very much.

For the founders in the audience and live streaming, I want to take you back to 2008. If you were involved with the innovation economy in India at that time, you were living in a strange kind of tension. On one hand, there was a sense of inevitability around the arrival of the Internet and the creation of the digital economy in India. On the other hand, if you focus solely on what was happening in that moment, there was very little evidence that we could all point to. Internet penetration was in the low single digits. Smartphones were a luxury. Broadband was a distraction. It wasn’t promise. And the main question anyone was asking was whether India would ever be more than a services economy for the rest of the world.

And yet, within 15 years, India became the world’s third largest digital economy. Not by copying what happened elsewhere, but by inventing something entirely of its own. A digital stack, a payments infrastructure, and a consumer internet economy that served a billion people at price points the world had never seen before. The companies that emerged were not just Indian versions of American ideas. They were fundamentally different companies built for a fundamentally different context. At Lightspeed, we think about that moment often, because we are sitting inside a very similar moment right now, except that the technology is AI, not the internet. The window of opportunity is shorter. and the stakes are much higher. So let me ask you all a question.

In 2008, if you had known with certainty that the internet was coming to India, not if, but when, what would you have done differently? What companies would you have started? What investments would you have made? Because here is what I believe with real conviction. The arrival of AI in India is not a question of if, it is a question of how and how fast. One of the most important mental shifts in the innovation economy, and it took me many years to fully understand this, is that what matters is not where you are, but how fast you are moving. Scale is a snapshot, but the slope or the trajectory is the story. When people talk about India and AI today, the conversation often gravitates towards infrastructure gaps, compute access, data quality, language diversity.

These are real challenges, but they are the current state, not the destiny. And in technology, the current state is almost always a misleading guide to the future. India’s slope right now is incredibly exciting. The pace of AI adoption among developers, the sophistication of the startup ecosystem, the depth of engineering talent, the hunger to solve hard problems, these are indicators of the trajectory that lies ahead. And trajectory, not position, is what creates value in the innovation economy. Thank you. Now, two areas where AI has the greatest potential to be transformative for India, and you just heard this from the former prime minister. are healthcare and education. World -class healthcare requires specialists who are available, reachable, and affordable, requires follow -up, monitoring, personalization.

In most of the world, and certainly in most of India, these things are rationed by geography and by income. The quality of healthcare you receive is a function of where you were born and what you can afford. AI will break that equation, not by replacing doctors, but by making the intelligence of the best diagnosticians in the world accessible to a primary care provider in a third -tier city, by making a first -level triage conversation available to anyone with a smartphone, by turning the enormous volume of health data generated across India into insights that improve outcomes, outcomes at population scale. The opportunity is not just to digitize an existing system. It is to reimagine what healthcare delivery looks like when intelligence is abundant and geography is irrelevant.

Education is the same story. For decades, the highest quality education in India has been rationed by examination performance and proximity. For every student who made it through, there were thousands who didn’t. Not because they lacked potential, but because they lacked access. A truly intelligent tutoring system has never existed at scale anywhere in the world. Building it for India, in India’s languages, for India’s learners, is one of the most important things our current generation of entrepreneurs could do. And it is now within reach in a way that has never been possible before. The impact of getting these two things right The future of the country is a huge challenge for the future of the country. is not just economic, it is civilizational.

A country where every child has access to a genuinely excellent education and every person has access to the best personalized health care, that is a different country and a different India than the one that exists today. Now there is an absolutely profound change underway, the impact of which deserves more attention than it currently receives. And that change is that the cost of intelligence has collapsed. It hasn’t declined, it has collapsed. The cost of running sophisticated AI inference has dropped by orders of magnitude and it continues to drop. What cost hundreds of dollars per query two years ago costs fractions of a cent today and the models are getting dramatically more capable, not less. Why does this matter for India specifically?

because price has always been the friction point in this country. Every technology wave that came before, smartphones, broadband, software, had to solve the affordability problem to unlock the India market. Sometimes it took a decade. Sometimes it required entirely new business models. Sometimes, like in healthcare and education, it led to haves and have -nots. AI is different. The underlying cost structure is compressing so rapidly that the question of whether AI will be affordable for Indian consumers and Indian businesses has already been answered. The question is not whether the economics can work. The question is simply who will be fast enough to build the right applications before this window closes. For the first time in the history of technology, a person in a village in Rajasthan with a smartphone, will have access to the same underlying intelligence as a knowledge worker in Manhattan.

Not an inferior version, not a stripped -down version. The same intelligence delivered through applications built for their context, in their language, at a price point they can afford. That is not a small thing. That is a compression of centuries of knowledge inequality into a very short window of time. And there is a deeper implication of this cost compression that we have not yet fully appreciated, one that is specific to India in a way that is different than most other countries. And that is that we have historically operated with a scarcity mindset. For good reason. Scarcity was our lived reality. Scarcity was our life. Scarcity was our life. Scarcity was our life. Scarcity was our life.

Scarcity of capital. Scarcity of infrastructure. Scarcity of opportunity. but perhaps the deepest and most consequential scarcity of all was the scarcity of talent. Talent is the raw material of innovation. It is the thing that you cannot substitute or work around. You can raise more capital, you can build more infrastructure, but you cannot conjure on demand a great engineer, a great product thinker, or a great operator. And in India, the talent pool, while deep in absolute terms, has always felt insufficient relative to the scale of our ambition. The best people were oversubscribed. The gap between what founders wanted to build and what they had the human capital to execute has been a constant constraint. How many companies have been in a situation where they have been in a situation where they have been slowed or never started because a founder could not find the right 10 people?

This is the constraint that AI is dissolving. When intelligence becomes abundant, when a founding team of five can do the work that previously required 50, when every developer has a sophisticated co -worker available at all times, when customer support and legal analysis and financial modeling and content creation can be augmented dramatically, the talent bottleneck changes in character. It doesn’t disappear. Judgment, creativity, domain expertise, leadership, these remain scarce and human. But the leverage available to every unit of talent expands enormously. A first -time founder in Bengaluru or Hyderabad or Pune, building with AI tools available today, has access to a level of organizational leverage that a well -funded startup in Silicon Valley can’t. Silicon Valley didn’t have just three years ago.

The playing field is meaningly more level than it has ever been. Now this mind shift is not trivial. Scarcity thinking is deeply ingrained in how we hire, how we plan, how we measure organizational capacity. We have to move from how many people do we have to what is the effective intelligence that is now available to our team. But this is the frame we need for the world that we’re entering. The founders and leaders who truly understand this and act on it early will build fundamentally different organizations. Leaner, faster, more ambitious in scope than those of us who carry the old constraints into a new environment. Now I want to be direct about one thing because I think that many of us right now are focusing on the wrong part of the stack.

Well, the founders and leaders who truly understand this and act on it early will build fundamentally different organizations. The foundation models, the large language models, the reasoning engines. are largely being built outside India, with the exception of Sarvam, this will not be the primary area of opportunity in India. The primary opportunity area here is in the application layer. That is how this technology diffuses into the economy, making it real for businesses, for consumers, for institutions. And this requires building applications that understand specific workflows, languages, cultural contexts, and very specific regulatory environments. It requires the kind of close -to -the -ground insight that comes from being inside a market, not just observing from the outside. And this is exactly what Indian entrepreneurs are uniquely good at.

The history of the Indian consumer Internet is a history of taking global ideas and rebuilding them from first principles for a market, that global players fundamentally misunderstand. The history of the Indian consumer Internet is a history of taking global ideas and rebuilding them from first principles for a market, that global players fundamentally misunderstand. The history of the Indian consumer Internet is a history of taking global ideas and rebuilding them from first principles for a market, And that skill and that instinct is exactly what the AI application layer demands. Now, I want to close with something that I mean sincerely, not as a formality, that everyone that is listening to this is not just a spectator about what is happening in India.

You are the protagonists for it. And that is not a comfortable position because protagonists carry weight. They make decisions under uncertainty. They move before things are obvious. They make mistakes and endure criticism. But those are the people that shape our future. In 2008, a small number of entrepreneurs and investors in India looked at a world with very limited internet penetration in India and decided that the trajectory and the potential was more important than the current scale. And that is why I am here today. They built companies that didn’t yet have customers. on infrastructure that didn’t yet exist for a market that hadn’t yet arrived. Most people thought they were too early, and some of them were.

But the ones who got it right created something unlike anything we had seen before. So we have been here before, and Indian founders have shown emphatically that they know what to do. The only question is whether we will move with the conviction and intensity that this moment deserves. I believe we will. Thank you.

Ladies and gentlemen, we are honored to have the Right Honorable Rishi Sunak with us. Former Prime Minister of the United Kingdom, Mr. Rishi Sunak, he was the force behind hosting the landmark AI Safety Summit at Bletchley Park, the point where the international conversation on AI safety truly began. He understands, perhaps better than almost anyone, how technology intersects with geopolitics, with democratic institutions, and with the everyday lives of citizens. And of course, we are honored to have you here with us, sir. May I please invite Former Prime Minister Rishi Sunak on the stage to share his views on the summit. Please welcome with applause, the Right Honorable Rishi Sunak.

Thank you. Namaste, thank you it’s such a privilege and indeed a pleasure to be with you today now as we’ve been hearing all week in Delhi artificial intelligence can do many things but it will never replicate that sense of wonder that you feel seeing the Red Fort the pleasure that you get from biting into a sweet laddu or if I can say this here in Delhi the joy you get from watching RCB’s Smriti Mandana hit the perfect drive now when I launched the first AI leaders summit in 2023 I created that summit to be a forum where we could all from Presidents and Prime Ministers to CEOs and CTOs, to developers and development specialists, come together, share the latest advances, and work out how to ensure that we tip the balance of this technology in favor of humanity.

So I’m grateful that South Korea, France, and now India have taken up the baton. Back at Bletchley, we committed ourselves to an AI future that worked for humanity. And that is why the first summit began with safety. There were risks, new risks, that we knew that we must avoid. And I’m proud that the Frontier Labs today are working with our AI Security Institute to test models before they are deployed, ensuring their safety. But I also knew that AI progress and AI safety went hand in hand. It is by showing the world that this technology is safe that we can make a difference. And I’m proud that the Frontier Labs today are working with us to help us make a difference.

And I’m proud that the Frontier Labs today are working with us to help us make a difference. And I’m proud that the Frontier Labs today are working with us to help us make a difference. And I’m proud that the Frontier Labs today are working with us to help us make a difference. And I’m proud that the Frontier Labs today are working with that will be able to fully reap the benefits of it. And the public sector is where trust in AI will really be won or lost. When people see faster services, better healthcare, simpler interactions with government, that’s when the debate about AI becomes real rather than abstract. Now, the pace of change that we’re about to see is going to be quicker than anybody realises.

I truly believe that there is nothing in our lifetimes that will be more transformative for our economies, for our societies, indeed all our lives, than artificial intelligence. But we do have to appreciate how quickly this is happening. From the invention of the telephone, it took around 75 years to get to 100 million users. It took the PC. 15 years. The internet, seven years. So how long did it take ChatGPT? Two months. So we do need a regular forum where we can all meet and discuss this technology and that is what this summit provides. Under Prime Minister Modi’s leadership, this summit will deliver impact. It will show us how we can make AI work not just for the developed world but for the developing world too.

How it can improve health and education in every corner of the globe. How it can enhance human dignity. How it can raise the floor for humanity. And there is no better place to discuss this AI transformation than India. The AI debate is moving from technology to strategy, from what these tools can do to what countries can do. And we are all in this together. We are all in this together. We are all in this together. We are all in this together. We are all in this together. We are all in this together. Indians are among the world’s most prolific users of both mobile data and AI tools. You are the second largest contributor to AI projects on GitHub anywhere.

The India Stack has shown people how technology can benefit them in their everyday lives. This digital public infrastructure, Aadhar, UPI and now Ayushman Bharat health accounts provide universal digitally verified foundations on which AI applications can now reach 1 .4 billion people. The energy that I’ve seen this week, the young people that I’ve spoken to, are testament to the vibrant startup ecosystem here in India, which has produced over 125 unicorns with new fantastic businesses like Sarvam AI leading the way. A remarkable culture of frugal innovation is why India could send Chandranayan to the moon for less than the cost of making the movie interstellar. And no country will realise the benefits of AI if its citizens are fearful of it.

Because people don’t adopt a technology that they are scared of. Again, India has huge advantages here. At a time of mounting AI pessimism in the West, this nation stands out for the fact that almost 9 out of 10 Indians are optimistic about AI. And all of this is why, in the latest Stanford University ranking of global AI powers, India has overtaken the UK into the medal places. Although I should say, England remain just ahead in the ICC test rankings. Now, the sprint to be the first company and indeed the first country to achieve AGI dominates our headlines. But what India shows is that the real race is the race for everyday AI, to spread this technology throughout your economy and society.

History teaches us that leadership in technology does not only depend on who invents it, but on how effectively it is deployed and adopted in your country. Take the printing press, invented in 1440 in Mainz in Germany, but, as Jeffrey Ding shows in his book Technology and the Great Powers, it was the Dutch Republic that extracted the most value from it, and in turn became the publishing powerhouse of the world. Now, San Francisco may be today’s Mainz, but it is increasingly India that is doing what the Dutch Republic did. It is the Dutch Republic that has done what the Dutch Republic did so effectively, and maximizing the benefits of this new technology. Because when it comes to AI, adoption is all.

It will be those countries and those companies that adopt, adopt, adopt who will be the biggest winners. Now India can also lead the way on showing how AI can address the great challenges of our time and raise the floor for humanity. If we are to feed a global population of 10 billion people in 2050, food production must increase by 70%. By 2030, we will have a global shortage of 11 million health workers and 44 million teachers, meaning hundreds of millions won’t get the care or education they need. And there is already a $4 trillion funding gap for achieving the Sustainable Development Goals. These problems threaten to cause famine and hardship, to destroy the human potential of billions. and to make the world an ever more unequal place.

But AI can and is helping us solve these problems and at a fraction of the cost. Look at how AgroSmart is enabling farmers in Latin America to access on their phones, in their fields, the kind of up -to -date weather and soil information that up to now has been the preserve of the largest commercial producers and the results have been sensational. It is boosting crop yields by a fifth while halving water and energy use. Now this technology offers the chance to achieve a breakthrough in agricultural productivity on the scale of India’s green revolution and if AI helps us achieve this, we truly can feed the world. Now for most of human history, the most dangerous thing a woman could do is give birth.

One in 18 married women died from childbirth in 17th century England. That number has fallen by 99 % today, but in sub -Saharan Africa, maternal mortality is comparable to what it was in England four centuries ago. And AI can help us tackle this inequality. Take the prompt service in Kenya, which offers 3 million pregnant women health advice by text message in their own language. The AI can flag high -risk cases and ensure that they quickly get the healthcare and medical care they need. For 74 cents a patient, this technology is saving lives and tackling one of the great injustices of our time. Now no country has become healthier. And wealthier without expanding education. As Kofi Annan reminded us, knowledge is the key to success.

is power. Information is liberating. Education is the premise of progress in every society, in every family. But today, too many children lack access to quality teaching and resources. And again, AI can and is changing that. Take MindSpark, which is teaching half a million pupils already in India. These children are being provided with personalized lessons in just the way that the most privileged children in developed countries are. And for just a few dollars a month, their rate of learning has doubled. The genius of this technology is that it doesn’t require super fast broadband and a fancy laptop, but just a simple tablet with preloaded content that draws on 20 years of research and 5 billion student interactions. think of the dreams that are being sparked by this the human potential that will no longer be wasted so in conclusion today we can see the bletchley so in conclusion today we can see the bletchley vision of an AI that favours humanity becoming a reality we’re seizing the opportunities of the greatest breakthrough of our time while giving our citizens the peace of mind that we will keep them safe AI will deliver huge economic gains it will have twice the impact of the industrial revolution in just half the time but what we are seeing here at this summit is how AI will raise the floor for humanity rural clinics will soon be able to offer the same level of medical expertise as big teaching hospitals as the son of a doctor and a doctor and as the parent of two girls blessed with the best medical care the world can provide, as the grandson of someone born in rural Tanzania, I know what a difference this will make.

It will lead to an improvement in human health and happiness that we have not seen before. Farmers on their small holdings will be able to call on the combined expertise of the world’s best agronomists and soil scientists. In the greatest step forward ever for equality of opportunity, every child will now have access to a personalized tutor. It won’t matter if you’re born in the Lutyens bungalow zone or in Ali Rajpur, you will, thanks to this technology, have the same educational opportunities. It will be the greatest democratization of knowledge ever. Friends, you This is the new world that we are entering. Never before in human history will so many people receive a boost to their quality of life.

That will be this technology’s greatest achievement. And that will be your legacy. Thank you.

Thank you, Mr. Chit Adani, for sharing your insights with us and your vision, as well as for enriching this August gathering. Ladies and gentlemen, it’s my privilege to now welcome Mr. Vinod Khosla, founder of Khosla Ventures, co -founder of Sun Microsystems and one of Silicon Valley’s most visionary investors. Mr. Vinod Khosla has been making bold bets on AI, on climate and health care for decades. He has argued that AI will replace 80 % of the jobs and that this is cause for optimism rather than despair. How? Let’s listen to him. Please welcome the founder of Khosla Ventures, Mr. Vinod Khosla.

Good afternoon. I am going to talk to you about some applications of AI that should be done immediately. I’m not going to talk about business or technology or where it’s going. I’m going to talk to you about what can be done today. If I can get my slides on the screen. Okay, so I’m going to talk to you about what can be done today in the next year or two to reach a billion and a half people in this country with really impactful immediate benefits. And unless AI benefits the bottom half of the Indian population, we’re not going to see a huge amount of impact. So, the first thing I’m going to talk to you that’s possible today, and in fact millions of kids in India are using today, is AI -based personal tutors.

And I’m going to talk to you about 24 by 7 almost free doctors available to everybody through AI. This is not helping a doctor, this is building a doctor. And of course, every farmer should have AI -level PhD agronomists available to them in their local small plot. This is all possible, they don’t even need to know how to read and write, just speak and look and take pictures. So, let me start with AI tutors. There’s a lot of children in India. There’s a lot of children in India who don’t get much help. in their education. In fact, in rural India, teachers don’t often show up. So it’s very important that this kind of a service be available so every child has their destiny in their own hands.

Thank you. The screen wasn’t showing my slides. My wife has been running a non -profit, ck12 .org, that offers it now. These are worldwide usage. About 400 million students have already used this service of AI content, which is all free, and AI tutors. In India, 4 million students have benefited by using the AI tutor. More than 12 million have used it constantly. So it is already in widespread use. This is already CBSE compatible, the national education policy compatible. The curriculums available in English or Hindi or Odisha or Meghalaya in these state standards, there are plenty of studies to show that they can be very efficacious. Does a student learn better with AI than without? In fact, I would venture to guess a student learns better with AI than if they had a personal tutor.

Rich people can afford personal tutors. They won’t do as well as people who have access to this AI. It’s a holistic kind of approach. I won’t go into much. It’s a pretty complex system. And I won’t go into the complexity of the system, but this is not just a chatbot. This is not just a key sort of use. AI simply. This has been built based on, and we’ve been working with Sarvam here in India to propose Diksha, which is a large collection of content in India, which is mostly unusable, to be honest, and build a 3 .0 version of Diksha, which is an AI -first experience. This is built on billions of student questions that have already been asked on the CK12 website.

Billions that is used to train the model to know how to teach a student. So it also has a teacher professional development curriculum, so teachers can keep up with it and keep up with most modern education. Again, compatible with the national education standards in India and the CBSE curriculum. Before. I go talk about AI doctors, I’m going to make a couple of comments. the AI tutors I’m talking about are far superior to human tutors. Here’s what they can do. They can quickly assess a student, where they are, in minutes, 10 minutes or 15 minutes, and then teach a tutor to the gaps in what the student doesn’t know through a complex process called knowledge tracing or tracing what they don’t know.

Moving on to AI doctors, which are also entirely possible today. In fact, these will make available 24 -7 to every Indian for almost trivial or no cost. Full primary care expertise, full disease management, chronic disease in India has been going up very, very dramatically. free mental health therapy, free physical therapy, and health and nutrition coaching. A level of comprehensiveness in AI health that isn’t available to the people who have the highest, most best -paid doctors in the world. None of this is available at this level, even in the U .S. or most Western countries. More than that is possible here for almost no cost. And of course, these AIs will be smart enough to know when to triage up to a human to do whatever functions only humans can do.

But let me not delude you. There’s very little a human doctor can do that this AI can’t do today. Other than the physical parts. If they have to feel your stomach, of course an AI can’t do that yet. I also fundamentally believe these services, AI -based doctors and AI -based personal tutors, should be part of the Aadhaar system. Aadhaar allowed us to offer UPI. There’s no reason we can’t offer on the same identity -based system where the hard work has already been done within a year or two to every Indian these services. So what I’m specifically proposing, to build a Section 8 nonprofit company to build, operate, and transfer into the Aadhaar ecosystem such systems. I think they’re relatively simple to do.

They need many cycles of iteration to adapt specifically to Indian conditions, all the Indic languages, all the differences in diseases. in each part of India. So I’m very, very excited about this. And what you can do for not only education, healthcare, the third element I want to talk about is agronomy. Having every farmer have a PhD level agronomist available locally 24 -7 alongside a UPI -like service as part of the Aadhaar system. A tutor that can engage students, it can find and teach to Gapson students and make the current Diksha system much more useful, much more friendly and leverage all the great content that is in the Diksha system in India today, but is not really usable because there’s no way to organize it and have an AI tell you what part of this vast system this vast library is relevant to you.

on the day you’re trying to do your homework or prepare for a test. You can multiply India’s doctors’ resources. So many years ago, I looked at the question of how you could scale the doctor -patient ratio in India to the same level that is in the West, like in the United States. And it wasn’t possible, even if you had a trillion dollars and decades to do this. That’s how far behind we are. But this will get us in India an opportunity to get well far ahead of the level of care available in a country like the United States, at least at the doctor level. There’s still surgeries. There’s still drugs. Those are separate matter, all areas in which AI can help.

But that’s sort of my hope. So the AI talks directly to patients. It diagnoses, prescribes tests, prescriptions. We are using technology from a company that’s been developed over the last five years and with Sarvam’s help and adaption to Indian languages using the Sarvam model. And you start with physician approval of the dialogue. So you oversee the AI with the doctor initially for the first couple of years. So think of AI as an intern, fresh graduate, an MBBS graduate who works for the doctor. They let them do a lot of things, but then they oversee them and watch that. And that’s the model I propose is possible in the next year or two. And within two or three years, I think that need for supervision will go away.

And of course, there’s emergencies. Sometimes you have to send somebody to the emergency room or the hospital, so AI can do that. The same is possible with agronomy, the third service. I won’t go through the details of this. I will try and finish up here. But I want to finish by saying the future is here today. Today, these massive impact services that couldn’t be done with hundreds of billions of dollars can be done very, very cheaply. Scale medicine, scale teaching, scale education, scale agronomy. And these services impact the bottom half of the population more, and they need it more than almost anybody else. That’s exciting. If we don’t do that, it is a massive opportunity loss for us.

Thank you.

Thank you so much, Mr. Menj for your compelling insights and also for highlighting the importance of technological independence in this digital era. Ladies and gentlemen, I would now like to invite Mr. Rajesh Subramanian, CEO FedEx. At the helm of one of the world’s largest logistics networks, Mr. Rajesh Subramanian is deploying artificial intelligence to optimize supply chains, predict demand and make global trade more efficient. His vantage point, moving 15 million packages a day, offers a uniquely grounded view of how AI performs in the real world. Ladies and gentlemen, let’s welcome CEO of FedEx, Mr. Rajesh Subramanian.

Thank you very much for the kind introduction and for the opportunity to participate in this important discussion. Gatherings like this are critical to advancing technology and making global progress collaboratively, responsibly, and equitably. And that’s truly our charge at this pivotal moment. The recent exponential growth of AI has the potential to be one of the most significant events in human society since the advancement of electric power systems and the introduction of the Internet. AI is no longer a trend. It’s the next industrial system, a union of compute, the energy, and labor that will redefine how economies operate and how humanity evolves. We understand the science, and now it’s about rapid amplification and application. Building AI capabilities is not optional, it’s essential.

Intelligence is not an asset, it’s infrastructure, the foundation of the future of global progress, productivity, and economic growth. As such, we have an opportunity and a responsibility to be more than consumers. We must be architects of AI. Every company, government, and institution should ask, how can we use AI to expand our ability to solve our most pressing problems, from broadening the economy to eradicating disease to improving energy efficiency? One of the most exciting aspects of AI is the potential to turbocharge problem -solving at scale. And that’s always been… the guiding principle at FedEx. to solve problems by connecting people and possibilities. Our journey began at another technological inflection point. Half a century ago, our founder, Frederick W.

Smith, realized that we are moving towards a computerized, automated society. He saw the world changing, and he seized the opportunity. He created an industry for the computer age, a novel way to move critical goods reliably and at scale. The integrated air and ground network would become pillar of the modern economy. And flight by flight, route by route, hub by hub, we built a network that connected the world, shaping business, communities, and global commerce. Along with pioneering overnight shipping, we invented tracking. We also fueled a massive growth of high -tech, advanced pharmaceuticals, international trade, and e -commerce. We are indeed the heartbeat of the industrial economy. Now, the world is undergoing a fundamental shift. The patterns and the rules of commerce are changing.

Impacts from the pandemic and shifts in trade policy are driving towards a new period of re -globalization. Global supply chains are moving from one equilibrium state to another. And FedEx is right in the middle of this transition. In my 35 years at FedEx and monitoring global trade, I’ve never seen change in this space and scale. We’ve also never had a powerful technological force for effecting change. AI is a force multiplier for shaping modern supply chains in a more connected, complex, and opportunity -rich world. We’ve always been a data -driven company. We have nearly 700 airplanes. We’ve always been a data -driven company. 200 ,000 motorized vehicles and more than 500 ,000 team members. We handle $2 trillion in goods annually, move more than 17 million packages daily across 220 countries and territories, and generate two petabytes of data every day.

We realized the value of our data, our insights, and supply chain intelligence early. And we set about organizing and engineering our data ahead of the current AI revolution. And when you power the industrial economy and generate over two petabytes of data, the potential to harness that intelligence with AI to create smarter, more resilient supply chains is immense. And it is a capability only a few organizations on this planet can make. And for the past 50 years, FedEx built the world’s most reliable network for moving physical goods. Over the next 50 years, our differentiation will come from orchestrating the intelligence that governs modern commerce. That is our future. And to get there, we’re using AI to transform our real -time network data into actionable insights that enable prediction, orchestration, and optimization across the entire supply chain.

This is not just visibility into what happened, but intelligence about what will happen next. Identifying vulnerabilities and addressing them before they become disruptions is probably the most crucial element of supply chain resilience. Over the long term, we will power solutions allowing FedEx and our customers to anticipate disruptions. to reroute flows, rebalance capacity, and prevent localized issues from becoming systemic failures. More importantly, we are scaling these capabilities responsibly and grounding them in strong data governance, cybersecurity, and ongoing focus on AI literacy so our teams know how to use these tools effectively. Now, this opportunity is not just operational. It’s also commercial. We are extending our intelligence through digital tools that solve problems for customers and enable them to optimize their operations.

Through collaborations and platform integrations, we can embed our intelligence directly into customer workflows, helping businesses make sourcing, routing, inventory, and fulfillment decisions with near real -time supply chain insights. We create common data platforms with our customers to build the best supply chain solutions together. This goes far beyond a supplier -vendor relationship. Our customers are often co -creators, and many of our digital tools are shaped by the feedback for small and medium businesses seeking to grow internationally. Just one example is our clearance solution has become increasingly important to our customers with ongoing shifts in trade requirements. FedEx Import Tool was first developed in India to simplify international shipping for small and medium enterprises. Their feedback helped us design features such as predictive logistics, automated shipment tracking and real -time customs updates, all powered by AI.

And this is now rolling out globally. What used to be a complex process became much simpler, giving small businesses better visibility and control over their shipments. Our AI -powered capabilities go far beyond traditional logistics, positioning FedEx as the digital backbone of supply, demand and decision networks across industries. Today, technology is business, and business is technology. As global citizens and leaders, we must think this way. We must use the AI revolution to propel global progress. From our experiences at FedEx, I encourage you all to get in the game. You cannot innovate from the sideline. If you don’t like change, you will hate extinction. Cease this opportunity with AI. Our actions must be decisive because the opportunity is genuinely transformative.

Ask not why, but why not. Question all ways of thinking. Take risks and embrace change as an opportunity for exploration and growth. And keep asking questions. Nothing will matter more in the age of AI, as AI increasingly contains more of the world’s knowledge. It does not matter what the world thinks. It becomes an even more powerful tool for anyone with questions. have a bias to action. The world is becoming more agile by the day and action is critical to keep pace. There’s also so much more to discover, including tremendous opportunity in how we apply emerging technologies. We have barely scratched the surface on how we use AI and machine learning to manage global supply chains.

For a company with legacy of delivering what’s next, I cannot tell you what the end state of AI will look like, but I can tell you that we will be ready for it. The transformative potential of AI is immense, and with that potential comes the responsibility to ensure that its benefits are widely accessible. How this latest generation of AI comes to life and how it impacts every aspect of our civilization is up to us. To us. FedEx is ready for this journey. We’re embracing AI and eager to solve problems, drive economic growth and global progress, and deliver a better and a brighter future. Thank you for your attention.