Thank you everyone for joining us today. This is the first session in what we hope will become an ongoing educational webinar series for investors, where we periodically discuss industry-relevant themes and developments. Today's discussion is strictly focused on AI in media, so we request all participants to keep their questions limited to this topic. The session will run for 1 hour. Baskar will speak for the first 40 to 45 minutes, followed by Q&A in the last 10 minutes. As this is a listen-only webinar, please type your questions in the chat box with your name and company. I will voice them during the Q&A. At the end, you will be invited to share feedback through a Google Form by scanning a QR code. Please take a moment to share your inputs and help us improve future sessions. Before we begin, please note the disclaimer. Questions relating to the company's financial performance, business outlook, operational metrics, strategy, or other company-specific matters will not be addressed. Please note that certain comments may include forward-looking statements, industry observations, or estimates based on management's current views and assumptions. Actual outcomes may differ materially. With that, I'll turn it over to Baskar to kick us off. Thank you very much, Angulia. Thank you folks for joining us. Good morning, good evening, good afternoon, wherever you are. Today's topic is an interesting and exciting topic, AI in media. I'm sure AI is a topic for all of us across many different industries that we're really working on today, and media is no different, and we're going to talk about it today. If you look at it, the first thing I want to talk about is to really understand how a typical video value chain works, right? As you know, television and all the OTT video streaming that we see, for example, I want to start off with an example of how that actually works. If you look at it technically, if you take any sort of a sports match or a news or entertainment content, it starts from the camera and ends with a screen where we watch content. We watch content either on television sets or on our phones or on our tablets. Wherever you watch it, that's where the content comes through. It always starts with the camera today, as we speak. There are a set of sections of how the process works today, for example. Let's look at it, for example, a game. If you're watching a cricket match, for example. The action obviously starts in the stadium. At the stadium, the first thing that you see is that you have multiple cameras. All these cameras get connected to a van called an OB van, which is nothing but a van standing outside of the stadium, typically with a satellite antenna on top, through which it gets beamed to a studio. We've all seen it. I'm sure if any of you have gone to a stadium, you would have seen this as the mechanism that you see. Obviously, there is commentary boxes from where commentary is getting done, and also there is some sort of a production room where they're trying to look at what camera content may make sense, and they do the switching of the cameras. Once that is done, it comes to a preparation stage where it comes to typically a studio. In studio, this cricket match gets converted with sports scorecards, all the logos of the different teams, multiple audio tracks that you want, and of course, the advertising and ad breaks. All of that gets added in the preparation stage in a typical studio environment. Once that is done, it gets distributed either through cable or through streaming TV platforms that we are aware of, through which we actually watch this content. This is typically a stage where obviously monetization could be advertising, could be us as consumers subscribing to content. You kind of do multiple different monetization capabilities today, and obviously, viewership could be across the globe, where people are starting to watch content. That's all of this chain. Starts from camera, producing content, preparing the content, distributing the content, and eventually getting to viewers, for example. If you look at it, I just want to give you an analogy of what this works in the world. It's also like a retail analogy, right? I'm sure a lot of you have seen the whole retail workflows and chain, for example. Like retail, there is a production phase where I'm sure that the consumer goods, you produce your goods, you take it to a warehouse and do all the packaging. You send it through trucks and any sort of logistics to get to retailers, and eventually consumers buy it either through directly or through the warehouse or the retailers through a quick commerce or an e-commerce or the mechanism that you see it. Very similar is what's happening in the content business as well. If I'm a content creator, the first thing I do is to really produce content. Exactly like a factory. There is a content factory where hundreds of hours or thousands of hours of content gets created, right? That's the first part of the value chain. Once it's created, it goes through a preparation stage where, like a warehouse, you're actually preparing this whole media stuff, and eventually, using some sort of logistics, you're sending it to the platforms. Eventually, these platforms eventually deliver to the viewers. Exactly like a value chain of a retail. Think about it in that particular analogy that I want to really talk about from a media distribution flow standpoint as well. If you look at it, AI is transforming every industry. I'm sure you folks are hearing it every day. Media industry is already changing, and it's no big difference. It's exactly like every other industry. AI is transforming this industry as well. If you look at it, what we're going to do today is to kind of understand this transformation that's happening across two spectrum of things that we're going to talk about today. One is, if I'm a content creator, a business of either building TV channels or OTT operations or any sort of studio or a content creator today, for example, I'm looking at either operating leverage through AI, and we talk about agent tech and workflows today, for example, or I'm looking at market expansions, where can AI really accelerate my expansion? Be it in terms of reach and audiences. These are the two real levers, if you look at it, that we really talk about when we talk about what's the opportunities in front of a content creator to leverage AI. The first step, if you look at it, is from an operating leverage standpoint. Agentic workflows. We're going to talk about what does it mean and how operational leverage actually comes to the whole system. If you go back to the world of really factory production of consumer goods, what is the biggest change in the retail industry that's starting to happen is the whole new evolution of physical AI. We're talking about robots. We're talking about things which are starting to come in and starting to make the factories completely autonomous, so that cost of production will come down in the production phase. This is the very similar scenario that's happening in the production space in the content business as well. Content business is getting disrupted with GenAI. I'm sure a lot of us are building videos today with all the Google Omni and Google Nano, and obviously with a bunch of tools like Runway, a whole slew of opportunities there that are starting to happen. CDance. It's very early today. Now we are starting to see production, really a high-quality production system starting to evolve. In the next few years, you're going to see a dramatically fundamental shift in terms of how this particular technology is going to start. What this essentially would do for the whole thing is that the cost of production is going to deflate. Let's look at it from an economic standpoint. If AI works and promises the way it kind of works today, what would happen is that the cost of content production will deflate. That essentially would mean that the whole economics of the business have to be rethought for every part of the value chain in terms of the production cost actually deflates in terms of stuff. When I say deflate here, we are starting to see a new set of AI-first studios evolving. These are studios who are trying to use AI as an important tool, but bring in the creative content capabilities and storytelling capabilities of humans, and they have fused this to create very compelling stories. I'm sure we've seen some in India. We've seen this across the globe as a major shift that's starting to happen. It's early in the days of what it is. That's a big shift that's starting to happen. I feel very live sports might be the only non-AI content to survive, if you look at it in a larger scheme of things, right? Live sports is like gladiators sports for all of us. I think from the Roman times, we always loved to see humans competing against each other, and I think that is something that we'll continue to see even in the future, given the emotions and the human needs, for example. All of the content technically will start to come to a point of where it'll be very hard for us as humans to delineate whether it was AI-generated or human-generated to some extent. That's a big change that's starting to happen. Once you create the content, the other pieces which are important is to what we call the media preparation stage. This is a stage which has lot of human cost today, for example. People have to take the content, encode it in different formats, and create a lot of different, what we call metadata. Now, what are all of this? If you look at it, I'm just giving an example of what it means by metadata. Think about you're watching a content today. I'm sure when we open our OTT platforms, we see what we call all of the data that you see on the screen is what we call metadata. If you look at it, there is an image, which we call an artwork. Typically, it's a description, which is a storyline. There are program guides, ratings. You see the actors, you see the episodic information, you see the content advisories, you see a lot of audio tracks, audio subtitles, for example, and who are the creators, copyrights, PG ratings. All of this forms what we call as metadata about each of the content that you create. Now, think about in an environment where I have thousands of hours of content getting created, and I want to do it across 40 countries, means I have to have multiple languages, not only of the metadata, but also of the language of the content, subtitles, and everything else. Think about I need to do it across multiple languages, multiple countries. Regulations are different in different countries. This is the heart of what we call is a metadata explosion that happens in the business. Today, it's technically a human cost today. Lots of humans. In this factory of content, lots of humans work either trying to manually creating this metadata content, looking at compliances. Think about all those things. Think about if I'm a sports company, I would be doing thousands of hours of sports every month. I'm getting a lot of content coming in. News, for example. Every day, I'm creating a minimum of six hours of unique news content that I'm creating. You are seeing a whole slew of content coming into my system. I start to get inundated with the amount of work to be done. There is clearly a big headcount crisis in most of these companies today, in media companies, because it's amount of work to be done. If you look at it, and when we talk to customers and others, one of the learnings that we're having is for every dollar that they've been spending on technology, there's $2-$4 of human toil. This is a big limiting factor for the video businesses today, because people have to see eyes and ears, people have to see it, control it, manage it. Everything has a human element to this whole system, right? That is the biggest change that we are starting to see, primarily through what I call agents to the rescue. Agentic infrastructure, when we talk about it, and again, agent is a very overabused word in the industry. Everybody calls everything an agent. I'm going to kind of slightly talk about what agent means in this context. Largely, what we're seeing is that now the whole chore of how people have been doing all of these content preparation stages, for example, across multiple continents, globalization of the whole thing, will start to become what we call agentic. Let's kind of give an example of what agent is so that we're all on the same page, right? If you look at it, again, I'm just taking a coffee machine example, so that it's easier for all of us to understand. Take a simple coffee machine, for example. A coffee machine which is standard is a non-agent. When I say non-agent, it has a pre-programmed set of things that you can do. You press a button, it does a particular sequence of things. You need an espresso, you need a cappuccino, you need a latte, you know to click a button, and it actually gets done. Anyway, this is just automation, a basic thing that the machine is supposed to do. This is the normal world of what we live in today, for example. What is an agent? If you think about the coffee machine became an agent for a moment, it's actually AI-enabled. How will it look like, right? Think about the coffee machine actually talking to you. It's telling you, "I've been monitoring you, and based on your facial expressions and the time of day, I predict that you might need an espresso." What did the system do? It understood your need proactively. It anticipated what you'd like to have. It actually is able to kind of act on that and be able to deliver something that was not really thought about. When the agent was created or when the machine was created, the machine was not programmed specifically to react to what you're seeing, but it's trained itself to kind of react to this whole thing. This is what we call as an agentic infrastructure. This is again, not only for a coffee machine, for any task, anything in any job in any of the industries that you see, this is what's happening. The ability to action on first be able to predict and eventually anticipate and act on it is what we call an agent. Obviously, it does not stop, right? As you know, as AI technology is becoming better and better and better, we are starting to see what I call true AI agency. What this means is like build AGI world that everybody's talking about, right? In a true agency, it learns by itself, designs by itself, changes in ways that as creators, we don't anticipate this whole thing. This is the future of how AI technology and agentic infrastructure is going towards. Think about the coffee machine really saying, "You know what? I've invented a new drink for you, and I think you will like it because I know what it is, and I'm going to make a cup of that particular coffee for you," for example. This is interesting because it actually invented something new, because it customized itself, because it knew that you wanted it, for example. That is what we call true AI agency, right? This is the future of how the progression of agentic infrastructure is going for all of us. Started with no agent. Today, we are in the world of agentic era, where the coffee machine equivalents are starting to really understand our needs and predict our needs and be able to do that. The third step is where it's able to pretty much invent and evolve itself. This is where I think the future lies in terms of how the AI technology is going. We've seen this in our own business and media business itself, where we've seen that particular transformation that's starting to happen. If you look at it from a packaging and preparation standpoint, it's exactly like a warehouse. Warehouses across the globe are starting to look at robotic agents which are actually doing the job of being able to sort things, package things, getting things done, for example. As you know, you've seen biped robots to multiple different robots starting to really come into this picture. Very similar is what we see in the AI, when the media business itself, is where we're seeing an evolution of digital agents. These are agents I can talk to. Think about it as very glorified coffee machines in the agentic era. Very similar is tasks and software, which are actually AI-enabled software, which understands the media, understands your video, understands audio, and understands the storylines, and is able to extract and create those metadata. What I showed you as an artwork there, or what I showed you as a program, as a rating of a particular content, or what I showed you as a story summary, for example, today, machines can actually create those summaries automatically. You don't need a human in the middle to do that. By just providing an overall sense of philosophy of how you want the metadata to look like, systems can do that today automatically. That's the power of these systems. What it essentially does is that these agents are starting to replace a lot of churn work, which is very hard because we couldn't get our customers to scale because all of the media companies were really struggling with this problem of messy media. You would have seen our ad time report that went out a week back. I would urge some of you to download that and see that as well. We talked about metadata problems is one of the largest problems the industry faces, which is slowing them down. We believe, like what we see here, for example, the agents will start to become a very, very important part of the whole equation to solve these problems, for example. Once you solve it, how should it really look like? Again, this is an imagination of a daily experience. Think about you are a scheduler in a TV channel, for example. The way I think eventually we need in an agentic world, we need to enable it is 2:00 to 5:00 A.M. This is obviously early mornings. The system is actually working. You're sleeping for sure as a scheduler, but the system is actually building all of the schedules for you automatically. It understands your taste. It understands the genre of the content that you're working with. It understands new content coming in the system. It understands the business needs that you have. It's understood the analytics of what happened yesterday in terms of viewership. It looked at social signals and figured out that yesterday was Amitabh Bachchan's birthday. It understands all of that. Once it understands, it's able to schedule it, do what a particular scheduler can do as a coworker, it actually works with you to do that job. Think about 10:00 in the morning when you wake up. The first thing it says, "Hey, I finished my whole schedule. Here is a summary of the schedule. I want you to take some decisions. Hey, human operator, please help me to take those decisions." It actually reads it for you, for example. Once you do that and you see the whole grid, you are comfortable with the schedule, you look at everything and say, "Okay, everything is done." In 10 minutes, you finish your job, you confirm it, and the whole system is scheduled, and you're done with the job. Today, this takes eight hours of a particular person to do this day in and day out, for example. Think about the value and the productivity that can happen is now this person can suddenly do tens of channels. All our customers want to do more and more channels, because the amount of content available and the number of global regions they want to reach is much higher. These sort of agentic infrastructure is starting to really help for people to be able to create this whole preparation step, which is the biggest chore job today, for example, will become agentic, that's the biggest change from an AI standpoint that we're going to see as we move forward. Once you do it, the question is also when you have the content done, you want to be able to take that content and deliver it across multiple platforms. As I told you, content is going global. You want to go to tens of countries in different languages and different regulations, and you go to different people. This is a logistics problem. This is exactly like you are a toothpaste manufacturer, you want to take your toothpaste and be able to take it to every possible store in the world, be it a Kirana store or an e-commerce or a quick commerce or wherever you want to take to a big retailer, for example. When you start doing that, the biggest challenge is a lot of back and forth interactions that happens today, for example. A studio, it has to transact. Today, all of those transactions are almost like human transactions today. There are people on both sides talking to each other. It starts with a very simple thing, right? For example, if I'm an OTT platform, I ask a simple question, "Hey, what is the show for Diwali that you're actually doing? I want to plan for Diwali," for example. Now, just think about it. This platform OTT brand has to do this for hundreds of such content owners, a very similar conversation. Either this happens on email today or in phone transactions where this person is actually talking to hundreds of such content creators. The reverse is true as well. The content creators are talking to hundreds of platforms worldwide across 40 plus countries, where they need to get these capabilities actually defined as well, for example. What you see is a world today of a lot of chatter and conversations and communications and negotiations happening between the studio or the content creator and the brand which actually need to do this, for example. Now, this is a very manually intensive job. It's not only about communication, it's about preparation, negotiations, and everything else that starts to happen. Think about a world that you're actually able to create two agents which are talking to each other. An agent which understands the platform's needs is talking to hundreds of such content creators saying, "Hey, what are we planning for Diwali? Each one of you, can you tell me what you're doing?" Think about the agent able to aggregate it from hundreds of content creators, find out the best Diwali shows that it wants, and starts to prepare for advertising or even creating content on your homepage to really promote Diwali content for all its viewers, for example. Think about and imagine the situation on the media studio side, for example. The studios can now be able to talk to all the tens of different platforms they're going to worldwide and actually talk about what they're going to plan for Diwali and be able to provide that and prepare it in multiple languages, multiple artwork, different regulation environments. They can prepare all of this capabilities and provide it, for example. Now, all of this is a big challenge. Just by having a human system, we were able to find it very hard to connect all these dots together. What we see is the future is what we call intercompany agent transactions. This is a very powerful new concept that's starting to happen in every industry. Media is no different. If you look at it, all the agents that we talked about earlier, were all inside the company doing a particular job for that particular company. In the media companies, as a content factory, as I told you, they define, they decide on the content, create your metadata, prepare the content, encoding all of the content related preparation is what it did. Here is a possibility where now these agents are talking to other platforms, transacting, negotiating commercials, identifying what's the opportunities, and eventually be able to transact it like a marketplace. Think about agents starting to talk to each other. Think about every company has agent interfaces through which it happens. Your legal documents get done. Your commercial negotiations get done. The technicalities of what content you're transacting gets done. This is a step in the future that we see, where you're going to see this whole interaction starting to happen across agent to agent that's going to happen, for example. What we started off with producing content, which I think with GenAI and with a lot of autonomous capabilities is switching. It's changing a lot of things in terms of how people are going to creating content. Once you create the content, how do you prepare the content to be distributed? We saw this whole agentic infrastructure coming in. Once you do that, you come to the point where you actually prepared the content, you want to transact that content across different globe. Inter-agent transactions become the core feature of how you do this. The innovation doesn't stop here. The moment you'll be able to do this, for example, fundamentally what is happening is think about delivery to consumers. Take what is happening in the world of e-commerce or commerce that we see as it is, right? If you look at consumer delivery today in a real world of how we are buying products today, our delivery of today is happening because we're discovering content through an e-commerce or a quick commerce or any of the websites or equivalents that we are actually going through, for example. I think we are in a very, very, almost in a close phase of how a big transformation is happening, and that's what we call as agentic discovery. What does it mean? Think about the world where if all of us have our personal conversational bots with us. That could be a ChatGPT, a Claude or a Gemini or tomorrow, whatever the sort of conversational bot that you have, think about what would happen. Would we go to a website? I'm sure already most of us are not searching on a Google Search today, but we're actually going to your conversational bots today to talk to things. Think about the bot. The bot has become extremely powerful. It understands, and it can actually understand your nuances, it understands your preferences, and we would start to really transact our goods through that. We're going to really buy all our stuff through possibly an agentic discovery interface, which is nothing but our own personalized conversational bot, which understands us, for example. That's the same thing that would potentially happen for the viewing delivery as well, right? We're already seeing that, for example, should you go to a destination? Should you go to an OTT brand website to actually look for content? Yes, the content is locked behind all of this paywalls today, for example. Think about a world that could potentially happen is you have an agentic discovery platform. You, as a consumer, would own the surface, own the interface, own the client that you have, for example, where you are able to pretty much decide on the content that you want to watch, and you're able to click buttons from that and be able to watch the content. The discovery is personalized to an extent that it knows what you have watched earlier. Not specifically on a particular surface. It's not about Netflix or a Prime or a YouTube or any of the other platforms you're watching, but it's actually having a good understanding of what you're watching behaviors across the whole spectrum of things you do. It also understands your mood, it understands your emotion, it understands the things that you are actually dealing with on a day-to-day basis. It understands you better than anything else in the world. That's the power of this whole agentic infrastructure that's trying to happen on a personal level. I believe that the discovery of content, discovery of commerce, will start to change, where we're going to start to really transact content through an entirely different surface, which is where I feel the next step in the evolution of these technologies are going to happen. If you look at it, we've seen how production is changing. We saw preparation changing. We saw the whole transaction of platform distribution changing, but more importantly, how consumers consume, how consumers discover, will also change. The whole value chain is starting to really look at a complete change because of this whole AI technology starting to really impact everybody in that particular equation, right? It's a tremendous value, big change that's starting to happen. Not only that, if you look at it, AI not only supports change from an agentic standpoint that we talked about, but it can also help you to really make your content better, extend its reach, create entirely new sets of content. With GenAI, obviously, we're going to start creating a lot more content, which we're going to create, for example. That's only the first stage in this whole equation. What we believe is that there's going to be a big transformation in how we create content or even repurpose an existing content, for example. That's going to be the big next step in the equation that's starting to happen. What is the fundamental hypothesis then, starting to look at this whole thing? For example, if you look at it today, this is happening across all the different media businesses that you will see it. There's a rich reality of storytelling that's there, be it sports, be it news, be it content. There's a lot of reality we're capturing. Think about it. In a sports game, for example, there is close to anywhere from 20 to 100 cameras today on a sports field capturing a particular moment in time in sports. There are sensors, there are microphones, there are so many different gadgetry that actually support a very rich reality of the interface. Unfortunately, because of the current bottleneck of both humans and the hardware that was kind of scarce, you see what I call a bottleneck in all of these capabilities today, for example. This bottleneck was because we could not scale as humans, and we cannot scale the hardware that existed, for example. That, what it did enable is it actually created a homogenized mechanism of how we watch content today. All of us watch very similar content. It doesn't matter if we are watching on OTT or cable or any other platform today, we watch a very similar set of capabilities. The same set of camera switches, the same sort of commentaries, the same sort of news content that you're watching, for example, on a TV, for example, on a live TV, for example, or even entertainment content exactly is not changed. What would AI do for this is a big change of being able to transform this capability dramatically, for example. One example, for example, think about it. If you had a single match that you're watching, if you're watching a cricket match or a football match, for example, a single football match now can be if you remove the human cost of somebody deciding to switch cameras, if you remove the hardware costs out of the system, for example, now you have a world where you can create many, many stories out of the same match. Think about you like analytically look at the game, you look at it very differently. Tactical fans, you can look at all of this. If you're a home fan, maybe I'll give you a complete different storyline of through a home game equivalent. The cameras I switch, the commentary that I do for you, the players that I actually show you, for example, the statistics that you see on the screen, the graphics that you see, everything will be specifically tailored towards a particular persona and what you want to watch, for example. Today, this is very expensive. If I want to do it through humans, if I want to do it with a lot of hardware, it's a very, very hard problem today, for example. AI fundamentally shifts this whole thing, from a cost standpoint, to afford and enable in real-time, the ability for us to kind of be able to change this, for example. The future is where you can have the same match, but you can actually see multiple stories. If you're a Gen Z, maybe you have a particular screen on which you're actually watching content, you're actually chatting with your friends, and you're able to do this. You're able to do it at multiple camera positions that you're able to see simultaneously, for example. You are able to create native storytelling across different things, AI is a very big enabler for these sort of capabilities that we can actually be able to enable as we move forward as a company, right? This is one big change that we're seeing in the industry. The other thing, for example, is if you look at it, I'm sure lots of you are starting to look at micro-drama. Today, if you look at it, thanks to GenAI and others, micro-drama is something that's getting created today, right? Micro drama, lots of teams and companies and production houses are creating micro drama from scratch. Now with AI, there are so many stories that we've told in the past. We are able to take our stories of the past, over the last 50 years, I'm sure we have done so many storytelling, so many movies, and so many soaps and episodes and serials that we've done, for example. We are able to bring it to the new world. Bring it to the new world. Example is a three-and-a-half-hour Sholay movie. The Sholay movie was actually available as 3-minute micro dramas in 10 episodes. In 30 minutes, you're able to see the whole story, for example. That's sort of an interesting sort of play that we can actually bring on board, where we are able to bring in an entirely new set of content and existing set of content to a new format that we are able to see. Same story, now you could do multiple storytelling formats. Again, this is not possible pre-AI because humans couldn't have taken millions of hours of content, converted into vertical from a storytelling fashion. Now with AI, these are all becoming a possibility of transforming content. Not only it's becoming agentic, the transformation of the content itself is a reality that's starting to happen. What I wanted to kind of leave with you is that we saw that not only cost operating levels can be brought in because of all the agentic automation that you can actually bring on board, the smartness which can actually deal with things. More importantly, the content can be transformed, the storytelling can be transformed, which I think is a very big step in terms of how we see this whole transformation starting to happen. For all of this, if you look at the technologies that are evolving, it's a very fast evolving space like everything else. The content business technology is slightly more complex. Why is it complex? Because it has audio, it has video, it has text. Unlike businesses which are just a typical enterprise businesses where we are transacting everything through only a text or a document, video businesses actually have to deal with audio. When I say audio, it's not only about speech that happens, it's music, the background audio, everything has to be understood. This is where humans are spending a lot of eyes and ears to look at all of this and hear all of this, for example. You take a video, for example. I need to understand the background, the characters, the action, the emotions, and I'm sure if you look at a game, I need to be able to understand a bit about what is a basketball dunk to a goal, to an injury, to a foul, to a yellow card. Every possible thing that I would like to kind of understand, for example. Similarly, if you look at text, I need to understand what's happening, how is the summary creating, what's the scene descriptions, and everything else in that part that's happening. What you see essentially is that as you start creating technologies, for example, the AI technologies, it is very complex to solve these problems because you have to solve all of this together. Already this is a field that's actually started evolving much faster, right? Obviously, large language models, great in text. You can definitely use it. That's your base of what you do from a text standpoint. If you look at it, obviously, you build small language models because your reasoning, it may not be very expensive, so you create those capabilities as well. More importantly, you are talking about video and audio, which are very rich in terms of the stories they tell, the emotions they carry, and the information that's available, for example. We are in a very early phase of that transformation. I think we've done leaps and bounds over the last two years in terms of obviously the GenAI piece of video generation. You also need video vision language models, which are nothing but VLMs, what we call, is to extract that information as well. I want to have an eyes and ears model, right? You want to be able to understand from our eyes and ears of what is happening on the scene and be able to extract that because that's the way you understand the multidimensional aspect of a particular movie or a content that you're actually watching, for example. What we call VLMs are actually driving that particular part of the house, right? Very early, very few VLMs have come in which are starting to engage, but they are very expensive and very slow today, right? What's happening. The next step that's happening is what we call world models. I'm sure some of you have heard of this whole world models. World model is trying to predict from the current situation that you see, what is the next step that a particular real world environment would have taken. Think about it in a-- obviously, this started in the autonomous car scenarios. If I know where I am, what is the next moment in time and what would happen? I don't know. A car in front of me would move, or should I actually put my brakes? You are actually predicting what's the next step in the equation that you want to do because you understand the real world and you simulated the real world in front of you, for example. That's a big use case for video as well. If you look at content business, for example, you can create, for example, sports scenarios, for example. Take a sports scenario. If I know an example of a particular location or a time of a person kicking a ball, I can start to predict how the ball is going to really flow. Because the system starts to understand the physics of the ground, the physics of our planet. It can actually start to understand the air drag. It can actually start to kind of predict all of that, for example. Exciting new possibilities if world models start to work the way it is. In the next couple of years, I think this will be a big change for the industry. You can create much more immersive experiences that was really impossible today. Think about the camera positions, and I'm sure lots of you watching World Cup today. I would love to see it from the gloves of a goalie catching a ball, rather protecting the ball, for example. I would love to see it from the shoes of a player when Messi kicks that goal, for example. I want to see how fast the ball goes, for example. I want to be in the ball to kind of see how the ball is actually transferred. The immersiveness of the experience is now driven by the cameras that we have. In the future, with world models, you're going to see the immersiveness of the experience is going to be driven by the ability for us to track every part of a game or a sport or a scene or whatever you want to see, for example. Immersive video truly is dependent on how world models will evolve. This is a big area of research that's happening worldwide, and we're truly excited because this is something that I think can dramatically change how video and AI in video can actually be a big change for us, for example. If you look at it, because of the videos also, as I told you, very hard problems of video, and you want sometimes very low latency, you want your cost to be reduced, you want your performance to be very high. Think about if you're talking about, I talked about games where we're talking about a football game, and you want to create multiple different productions, different cameras to be switched to different people, for example. Now, a human can see that and quickly in their mind decide which cameras to switch, for example. Think about if I put a machine in front of it to do that. The machines, if I put a, I don't know, a language model of any fashion, the vision language model, it'll take few seconds to even understand what the scene is all about. That's not acceptable in the content business, for example. We see that this is an important aspect, is there is going to be hundreds of custom audio-video models which are very specific to things, right? Some things can understand maybe just a basketball game or a soccer game. Some things can understand just the environment. Some things can actually only do a modeling, a great ocean scene, for example, for you. The world models and the custom models are a big important part of the technology evolution that's starting to happen in this particular world and stuff, right? Audio-video drives much, much larger needs than just the LLMs. It's not a Claude or a GPT or a ChatGPT or Gemini that can solve problems in the video and audio business, because it needs a lot more. Not only for generating of video, but also for extracting information out of video to take any sort of reasoning decisions. It's a fairly large and complex problems. We are in a very early phase of the transformation of those technologies that are starting to happen. I think it's truly an exciting phase because audio and video is the next biggest, because like humans and how we kind of take in and reason our systems through our eyes and ears are a very, very important part of the whole phase of how we access content and react to content. I see the same thing starting to happen in the systems as well as we move forward. In a sense, if you look at it, I just want to kind of stop. AI transformation is happening across the value chain of this content business. It's not one with another. We're seeing this change, foundational transformation. Like every other business that I'm sure you folks have been kind of interacting with multiple different companies in multiple different businesses, media business is going through that transformation. It's truly an exciting time because this is a new era that's starting foundationally, I think this is going to change the players, change who creates content, changes the personas, changes the way you're going to consume content, changes the whole storytelling economics completely. That's exciting because this sort of a transformation happens once in a lifetime. That's where I think the opportunity lies in terms of being able to build not only technology, but entirely new order of businesses and the value chain getting reconfigured completely. With that, I would like to kind of stop this presentation for you to tell you that, hey, this is an extremely exciting opportunity. AI in media is one of the most compelling, challenging opportunities that we are seeing. Obviously, I'm biased because I'm part of a media company. Truly, this is a big opportunity, folks. The change is extremely palpable. The technology is complex, but that makes it the most fun in terms of doing it. The transformations in front of us, the experiences that you and me as audiences will see is going to be dramatically different. With that, thank you for this time that you all spent with us. We'll go to questioning. Amoolya, what's the next step that you want to do? Just before we go to questions, there's a QR code on the page here for you to leave feedback to us, so that we can improve. I want to reiterate that the discussion is going to be strictly focused on AI in media. All questions, please be limited to this topic. We will not be taking company-related questions in this session. Right? The first question, Baskar, is from Pratibha Dita. Given that we have strict SLAs up to 99.9999 with clients, agentic AI can be prone to hallucination and be at times unpredictable. What are the safeguards we have in place to mitigate such risks? Okay. Salia, I wouldn't want to talk about the business. To make the systems not hallucinate and be able to deliver value to all their things, right. A lot of work that our company, and I'm sure a lot of companies are doing, is to bring determinism to an indeterministic problem, right. You need to put a lot more guardrails. You build what's called evaluation systems. And when you're talking about a factory of content, hundreds of thousands of hours of content, the predictability to bring in is the most important thing. And I'm sure if you talk to any companies which are really in the cutting edge of building AI solutions for customers, the challenge is not demos. Demos and POCs are the easiest. The hardest is to get them to production and make it work realistically in all this manage. That's part and parcel of the key value of building the technology and engineering of these companies to make it happen. That's what it is today. Got it. Got it. A question from Shankar Narayanan S. Regarding the prep work, are we seeing any improvements in the speed in work due to AI? Does it in turn reduce pricing because of AI-led deflation? Primarily, if you look at it, this is a human cost reduction advantage. Right. If you look at it, the fundamental idea is the core technology remains exactly the same. We need to be as factors and transit content. I think what was the human element. Take an example, for example. I'm sure you folks have seen that today, for example, speech to text is at a point where all our sub-packaging needs are going away. Dubbing is becoming a reality. Take any sort of transformations of any artwork creation or promo creation, for example, which is a human job somebody has to do, is changing. This is actually a net impact in terms of, we are seeing this as a net incremental that's starting to happen because this is a human cost that was there, which is either not possible or couldn't be expanded. That's where I think newer business and possibilities are starting to happen for us. That's what we're seeing. Got it, Baskar. Another question from Shankaranarayanan. Can AI become a lever for getting production and pre-production workflows to be hosted in cloud? Absolutely. One of the trend lines that we are seeing across the whole customer spectrum is that AI, by definition, is driving a progression towards more scalable cloud platforms. The reason why it's happening is fairly straightforward, because I don't have access to GPUs and servers in my own facility. Clearly, I need to move from on-prem to on-cloud. That progression is happening because of acceleration of AI workloads that are being deployed in my digital office, and that's a big driver. Absolutely. We are seeing lot more progression happening with new tests. Question coming in from Anmol from DAM Capital. Have we seen increase in OTT cable-based content because of AI? Any real-life examples on cost that has been reduced? In bits and pieces today, Anmol, I don't think there is a big factory approach to the whole system. I'm already seeing a lot of content creators telling us that, for example, the VFX cost has come down. That's the first step. They are going and changing the whole movie content or storytelling completely. Parts of the story is now getting told through an AI, either background creations or in specific locations where they're actually starting to use it for storytelling. That's where we're seeing it. It's early days, I'm sure given the directions and what we are seeing, in micro-drama, I see a lot more activity. In the OTT platforms and the content creation platforms, although small today, but definitely the direction is very clear that it's going to be a big tsunami of sorts in terms of how content is going to be created in future. Got it. A similar question, Baskar, on cost-saving metrics from Ayush Shah. Is there any evidence on cost savings for production prep, et cetera, especially given the higher token costs? Again, going back, see, token cost is only a very small part of the cost of AI today, as it only loads video. Videos are primarily where a lot of customer models are doing the job today. Yes, there is GPU cost. I wouldn't call it token cost in that context of that currency, because token cost is very specific to the language models that only do this. In the work of multiple GPU cost and expenses, it is there. What we're definitely seeing, for example, is in some of the capabilities that we came through, humans wouldn't have done this job. Just the volume of jobs is so high that people would have done it. That's number one. Clearly, we're seeing possibilities for our customers where they can create new revenue possibilities. That's one place that we're seeing. The second one we're seeing is really cost savings because humans are very inefficient in doing some of these capabilities, for example, and we think tremendous savings in those capabilities. The cost of GPU is nowhere compared to the cost of the human cost that has to be applied if you're going to do that, right? Most of our customers are looking at more of an expansionary aspect, lot of the cost-saving capability as it stands today. Got it. I just want to reiterate that a couple of questions coming in on Amagi performance and so on and so forth. This is not the forum for it. We'll probably take it up at a later point. There's another question from Rohan Nagpal, Helios. Are there any network effects that can come from agent-to-agent communication opportunities you discussed? Is there any benefit to being the first mover here? Okay. I'll give you a generic business answer for this rather than trying to do anything with Amagi specifically in this case. Clearly, if you look at it, when you look at any ecosystem players, if there are two parts of the ecosystem that you are able to connect to an agentic infrastructure, the value of the network effect is extremely high. One is, if you look at any agentic infrastructure which works on one part of an ecosystem, it's a more productivity enhancer. It's a cost efficiency and operating leverage enhancer. Anything that actually is communicating and coordinating across two players or two different distinct entities of an ecosystem, for example, or tomorrow, multiple entities across ecosystem, I think it has a dramatic multiplier effect in terms of network effect that will happen. Truly, that's going to be a productivity enhancer. Very early days in every industry. In terms of agent communication and protocols and standardization and how this is going to happen is something that I think every industry is going through this whole process of change. I think by defining it for specific industries, we can start to attack these problems. I think that's where the biggest value multipliers in terms of newer programmatic interfaces through which these transactions happen. I think first there's going to be a time compression of all these things, right? Think about today, just negotiating legal agreements between two parties and two different ecosystem partners, for example, is extremely high. These agents can technically bring it down dramatically, in orders of magnitude of time and effort that will come down. Obviously, the key aspects of what us consumers would like to have, I think the ecosystem will drive that. Agents will support it. The key decisions are going to be humans, all other capabilities will actually be agentified. I think that's a huge opportunity in front of every company which is looking at an agentic infrastructure. The question on the depth of engagements, Baskar, from Bharat Gulati, Velas Rocha. Does AI help strengthen the business with deeper engagement with clients? Does this create a threat to horizontal SaaS or cloud companies like hyperscalers, et cetera? See, fundamentally, again, two parts. There's no one single answer to these sort of direction aspects, right? I'll tell you the vantage point of vertical, which is what we are, and horizontal. If you look at it from a vertical standpoint, because it is very, in the view of at least the vantage point of the media business we see, this is mission-critical software. It's mission-critical services and capabilities that you're actually delivering. That provides a significant moat for all these companies to understand. Because context, I'm sure you're hearing. The reasoning might become commoditized, but the context of the enterprise, the context of how you do things becomes the most important moat for any business. Whoever owns that context and the moat is the one which actually can drive the whole workflows of the future. Obviously, the agentic infrastructure, the reasoning system exists, but important is the business context, the technology context, and the operating context, and how you bring the creation of that. Long story short, whoever owns it in whichever kind of businesses that you're looking at it, that is going to be the biggest drivers for these businesses going forward. Great. There's one question just coming in now, folks, we have about five minutes left, any questions on the industry, on the topic, please post them in. This is from Sharad Goenka, Bhaskar. With the rapid adoption of agentic AI across media, do you expect it to materially reduce operating costs for your customers? If so, could this create pricing pressures as we go about things? Look at it from a seller's lens. What are we seeing? Like everything else in life, for example, is what I call the Jevons paradox starting to play out. Jevons paradox is simple. If you had more automation, more capabilities, I think as a civilization, we've always done more things. We've never done less things, right? The conversation I'm having with my customers today is that they're saying, "Oh, now we have this. How can I expand my capabilities? How can I expand my revenue options?" The fundamental idea is more the technologies and automation and the capabilities of reasoning that comes in, more the business expansion that we're starting to look at, right? In terms of how people want to look at this trip. More than deflationary trend on a per job, that might be eventually a deflationary trend. The amount of jobs that people want to do, the multiplier of things that people want to do looks only expansionary for me, the way directionally that I see. It's nothing about Amagi. I'm just talking about as a whole industry trends that we're seeing. That's some of the direction future that I see. Another from Chintan from Girik Capital. How do you see industry commercials between vendors and clients change from its current form as industry moves more towards agentic AI deployment? I think there's going to be. Again, it's very early today as we see, right? This is a relationship that's starting to. I think people are starting to explore change in this particular direction. Obviously, a lot of conversations on outcome-driven change, right? I'm sure you've seen it in every industry, where outcome-driven pricing models, be it a call center today that we're seeing, that on a per transaction basis or per results basis or from a per success criteria basis that you're trying to do. We are starting to see that sort of conversations across every industry happening, and media is no different in that perspective. We see this as a directional aspect where you will have the core gut of the system continuing to be what it is, because that's based on the value equation that we are already gaining. Everything else from a human cost operating leverage or a new revenue share possibility that whenever there's an expansionary trend, I think the expansionary trend is going to be driven by some sort of an outcome. The question is, how is the pricing going to be determined linear to the outcome is something that has to really play out in every market, and media is no different. We don't see that really play out as much today, but very early to say how that's going to play out going forward. Great. I think we are at the end of the queue, Baskar, in terms of questions that are non-company related. Folks, I think we wrap the webinar for today. Any other questions that you may have that you may need a one-on-one engagement, feel free to write to us at ir@amagi.com. Thank you all for joining in today and encouraging this whole new form that we're trying out. Thank you. Thank you very much, folks. Feel free to bring your feedback in terms of what we could do better, new topics that you would like us to share. Happy to go through this process. Given the ringside view that we as a company, we're very fortunate to have it worldwide. Happy to bring in specific curated content for you folks in terms of how we do it. It's nothing about Amagi, particularly an education session that we would highly be happy to do this. We'll bring in experts worldwide to help you to understand this better. Happy to do that. Please give us feedback. We'd be happy to do that. Thank you. Thank you. Thank you very much, folks.