You will also find nine of our partners about how they are actually using some of the technologies, how they are building on Siemens accelerator in order to make all of this work. So, moving forward, the role of Siemens that we want to play and that we're playing in all of this when it comes to industrial AI is where we can connect, as I said, the real world with the digital world. We can bring together the data together with the applications, and then all, of course, the competencies that we need to have. And you find many statistics here on there. What I always find most important is the following, that getting data is one thing, but actually the key thing is getting the right data and understanding the domain that you're operating in. And one of the things you're seeing here on the slide is that actually every third machine that's operated in the world is being actually controlled by a Siemens controller. Which means that gives us the data in order to infer and to make, of course, those models available in order to address some of those use case challenges when it comes down to quality, productivity, time to market, whatever the challenge you're looking at solving. And maybe one fact that many of you are not aware. Siemens is the number one company when it comes down to industrial software. We have more than 11 billion dollars of sales in the digital business, which makes us the 16th largest, 16, so 16th largest software company in the world. So, probably you think of Siemens as these are the people that electrify. And you're absolutely right. This is what we've done the last century, and this is going to continue to do so, and we bring more digitalization and electrification. Then we moved over to automation, and then we automate the world, and then we digitalize the world, and now with that digitalization finally comes the industrial AI all along. Let me say it just a few words more about industrial AI, because actually it really helps to understand why this is different. For industrial AI, as I said, you need data. And most of those large language models today, they're being trained on the internet. That does not work in industry. You cannot go on the internet and download production data, design data, engineering data. This does not exist. So, the only way, the only way how we can build industrial AI is by doing it together. I tell you, each of you in your operations, in your company, you probably sit on your own data, which is the good news. The bad news is that data that you're sitting on is never going to be enough and actually building these models that need to be so reliable and so accurate that they actually make a difference. Even Siemens, we sit on about 300 petabyte of data. We have 2 million CAD files, all of this. This is a lot of data, but still not good enough for building something in the industrial world to make those applications work, because they need to be heterogeneous, they need to come from different places. And this is why we have to work in ecosystems, in data alliances, and bring actually that data together to train a model on specific use cases, and then deploy it to all of us. So, therefore, we can work with all of you. We are serving today 30 different industries from cement, glass, semiconductor, pharma, life sciences to data centers, where we can provide that data and those solutions to make that work. So, nobody can do this alone when it comes to data, but also nobody can do it alone when it comes to technology. And this is why we're also partnering a lot with Nvidia. In early this year, at CES, we spoke about how Nvidia and us are bringing the latest technologies together, and this is going to be GPU-accelerated and going to make a significant difference in the industrial world. And you're going to see two use cases out there where we talk about something that we call the digital reality viewer, and also about the digital twin composer where help you to build this digital world in a much much faster way that then connects you to the real world in real time so that you can make better predictions. So, how does it look like? This is an example and you're going to see that video also out there. It's a simulation in the end where we are able now to scale it to an amount of complexity where you can build these ships, ammonia propelled ships of 7 million parts where you can actually simulate factories where we think about how can we build these GPUs in a much much faster and more efficient way on a daily basis where we can think about shop floors, how we change them in order to make cut out of times much faster and of course for India in particular important, how can we bring railway simulations also faster time to market. So, all of this can be only done if you do this together. And that's why we are really really happy to also have today two of our key partners there. So, please everyone welcome with me on stage Vishal who is the managing director of Nvidia and Sanjeev who is the CEO of Adverb Technologies. Welcome Vishal. Welcome Sanjeev. Great to have you here and thank you for joining me here on stage. This is fantastic. And Vishal, maybe I start with you. Obviously, we are building a lot of great latest technologies together. What's all this role for you in India? Maybe you can share a few words from your perspective.
I think Sunil set up the stage. He spoke 14 15 16. Be whatever number manufacturing needs to be significant proportion of our GDP. There's no second thinking on that part. And the government is very clear. I think everybody in this room is very clear. Manufacturing has to be that much more important. And that's exactly where Siemens and Nvidia come together. If we can truly help you scale in software defined AI native factories, you're going to be scaling. Now, why do I make this statement? There are three things that are happening in India simultaneously. First, the number of greenfield projects that are coming up. And at the scale that they need to operate. If they need to operate on that scale, you can actually build true perfect virtual copy today. And you can build that virtual copy and figure out how to lower your cost. Stats show anything between 10 to 20%. Stats show your output can go up by 20%. Stats show that you can capture 90% of your mistakes in the virtual copy. Imagine Make in India world class. Second what government is doing, PLIs. The momentum of PLI which is over 20 billion dollars has already generated 1.2 million jobs. And everybody in this room who is involved with grassroots know how important those 1.2 million jobs are because that brings prosperity to people. And we haven't ceased there, the third part. The AI flywheel. What the government has done through the public private partnership is to truly build AI brains here. For sure it is coming up, it's getting built up, but imagine when that 60 plus million MSMEs will get that AI brain, what does it mean? Combine these three together is industrial AI. I believe India is now on a threshold where we can become the architects for industrial metaverse.
Wow, okay. So, that's 20% faster for more productive, 90% capturing failure rates earlier. Virtual twins. Now, Sanjeev, tell us a little bit about Adverb, about what you have founded and what you created and how those numbers are reflecting back on you using Siemens and Nvidia technology.
Hey, so Adverb is a robotic company. We automate warehouses and factories. We started 10 years back and we are India's largest robotic company today and one of the world's fastest growing robotic company. Yeah, and we build all forms of robots. So, we build mobile robots, cobots, quadrupeds and now 10 days or 1 month back we launched our humanoid. So, we build all forms of robots. Mostly for industrial application, we don't get into home application. We do get into defense, but industrial and defense is what we do. As far as leveraging Siemens and Nvidia technologies, so if it is industrial AI which we are in without these two partners, it is just not possible today in the world. So, we leverage as far as Siemens is concerned, when we started in 2018, from then onwards everything we design, whether it is mechanical, electrical, electronics everything happens on Siemens platform. Then we have product lifecycle management Teamcenter where we do all the versioning controls, etc. Then we have Plant Sim where we do the simulation. We have Technomatix and Technomatix platform is because we don't sell products to our customers. We sell solutions. So, if we have to sell solutions then we need to show how these products helps our customers solving their problems. So, and that we do it on Technomatix platform. So, we build the model, we do all the coding as far as PLC coding is concerned and then also the WCS and WMS, all the enterprise level software fleet manager for robotics, etc. And then with customers, we sit together and do what if analysis and what happens if this happens and whatever whatever we were talking about in the simulation world. So, that when we go to the real world, we see less of challenges. So, and then we also use Mendix platform to support our customer and which is where we are leveraging lot of AI and we can keep on talking about it, but so that the customers can use the machines the best in the best form and also take care of the machines. As far as Nvidia is concerned, again because it is robotics, it has to be. So, our the form factor, so the cobots or the quadruped now quadruped has to walk on different terrains and it has to walk blindly walk on the staircases, etc. So, all those the training of the models, we do it on Nvidia simulation platform. And then those policies once it is trained, we take it to the robot and whatever seem to real gap is there, we try to reduce it. So, again and we keep on trying on these platforms. So, until unless we have Nvidia platform or Siemens platform for our engineers to play with we cannot build great and reliable products for our end customers. So, that is how Nvidia and Siemens are helping.
Great, thank you Sanjeev. Fantastic obviously because fastest growing robotic company. Now, now Vishal, you mentioned that we want to unlock that power. So, we really want to contribute to the next generation of manufacturing. How do we unlock that here in India? What are the challenges and how can from your point of perspective, how can we overcome them?
Yeah. So, one of the most important thing that Siemens and Nvidia is doing is to really build up industrial AI operating system. India can benefit from here. You can reimagine your factories. You can have optimization taking place. You can control your supply chains. And just to give you a quick example, Sanjeev of course spoke about that example. Even in some of the large organizations, for example, we spoke about Reliance New Energy. Reliance New Energy is basically talking about a gigafactory without even breaking the ground. They're virtually building that up. And exactly what I spoke in my previous answer is some of the benefits that they are doing. I have my colleague here I know she looks after Hero Corp. They're doing acceleration of the entire design cycle. And it's working on Siemens accelerator, it's working on Nvidia infrastructure, and they able to reduce the time from design to completion. Now, think about three other things. We're going to be building 12 brand new industrial cities. 12 new cities which are going to come up. How you going to do it? You're going to obviously need to do it on AI native operating system. We're supplementing as you saw in some of the slides, how much energy is going to be added, especially the renewable energy. How do you build real-time virtual scenarios there? If there's electricity being produced on solar and wind, if there's a cloud cover, what happens? If you've got to automate the grid, how does that happen? Those are the possibilities that are possible. We've already seen in a city very close from here in Pune, AIRI has actually created a lab where you can do crash analysis. You can actually do crash analysis for EV packs right here, and you can cut down on the time. They have two spokes, one in Bangalore, one in Guwahati, which is taking place. What does it really mean at the end of the day? It basically means that you're looking at AI moving beyond the words to the world of atoms being grounded there. Most importantly, being connected and scalable. That's your advantage, that's advantage India.
Fantastic. Great examples there, and maybe to conclude then Sanjeev from your perspective. So, we want to bring technology from India to the world. So, you've built all the quadcopter pads and the mobile robots and so on. Tell us a little bit about where do you see those industries and which markets are really important as you go from India and then conquer, of course, all the markets that are out there.
Yeah. So, India is very important for us, and we started the company because we wanted to help Indian companies become more productive so that they can scale. For them, India only does not become the market. For them also, the whole world becomes the market. So, that was the objective, and that is how we started. Today, 40% of our revenue comes from India, and it would continue to be in this range, 40 to 50% for the next 10 years when we scale from 10 times of what we are today. As far as so, we export all robots from India to 25 countries today. We have 100% subsidiary in US, Europe, Australia, and Singapore. There is a dream that in next 10 years, we reach out to more than 100 countries from India. So, and building all different kinds of robots for the world for different applications. You were talking and Suneet sir spoke about the opportunity in green energy. So, today I can we are so, Reliance New Energy is a 20 GW factory, and there are and that is solar itself, and it is going to be 40 GW, etc. There are several solar factories, at least 11 of them who are our customers today in the range of 6 GW and more. So, and then batteries, so and electronics. Today, 25% of Apple's mobile phone is getting manufactured in India, and they are customers. So, not Apple, the Tata Electronics and Foxconn. So, all these sectors are growing up rapidly in India. But, we also believe that because of the geopolitical thing, every country would like to have lot of these industries in their own country as well. And because we have this experience of automating in India at scale at a price which is very, very competitive, and those values we will be able to bring to the customers across the world.
Thank you so much, Sanjeev. Thank you so much, Vishal for joining us on stage. You can't go. We need to ask you a question, too. Okay. How can it be only one-way traffic? So, we got Peter, who's very fortunate. He gets to see everything across the globe. The mega scale when it comes into play, which India needs to play one day, Sangeet is already going through that experience. What is one or two things that we can learn from you that India should be sensitive about instead of learning on the spot. If that wisdom is available, we can overcome it. So, give us one or two lessons that you've seen across the world.
I think what we've seen is as as so many others is that we've seen so many proof points of POCs, yeah. There we have shown a first pilot, but then it does not scale. And the key question is, how can we get to scale? You had great examples, Sanjeev. You had great examples there, but very often they're very specific, and what we realized is you have to do this first customer backwards, have the great idea, but then do it industry by industry. So, in other words, you have to go do it by electronics, you have to do it by battery manufacturing, you have to do it by automotive, and really understand what are the key use cases in there, and then really understand how they scale, and then actually build these applications. This is what I really recommend to all of you as you think about your AI in particular is that a use case actually that can scale, otherwise it will be just yet another POC out there.
That's so good. Go beyond POCs to implementation. If our country became a shining star with less than 1% of developers from our total population, imagine each one of you now is a developer. You can speak to the computer in your respective language. You can mix your language. Computer understands it, program it, go from POC to production, and make India really smart. Fantastic. Great summary. Very good. Thank you so much. Thank you. Wonderful. Okay, you're okay, we should take a quick picture, I understand. Okay, good. Very good. Okay, thank you once again. And I hand it over to Indu, our master of ceremony.
Thank you so much. I hope you're also enjoying it as much as everybody who was listening to it is enjoying it. And I'm hoping there are a lot of questions out over there in the audience, and we'll pass you the mic in case you have something. But, to proceed with the Q&A, I'll invite Mr. Sunil Mathur also to join us onto the stage. If you have a question in the audience, please raise your hands. We have heard so much. So many new technologies, so many examples. So, what are the boiling questions in the room? Let's hear them out. But, before they say something, what excites you about India? You heard so much. Sunil just threw so many numbers. Obviously, we win the matches, but beyond that, what is that is interesting and what's on your mind for us?
So, I talked with Sunil. He had so many numbers for you today. I'm not sure how many numbers you're going to remember going out of here. And I'm not sure if I remember all the numbers that you had on there when you spoke about the Indian economy. But, there was one thing that I kept in my mind, and this were the arrows. And these arrows, they pointed all steeply upwards, and it's very true. No matter if you talk about the data center deployment, no matter renewable energy, no matter about airports, semiconductor plants, and so on. All such a steep increase, you rarely find this anywhere in the world. So, it's a super exciting place.
