Microsoft AI Tour keynote session by Satya Nadella | Bengaluru | January 7, 2025
[Music] [Music] [Music] >>> Please take your seats, the show will be starting soon. [Music] [Music] [Music] [Music] [Music] [Music] [Music] >>> How do we empower every person in organization on the planet to achieve more? >> It starts with one. >> From one employee to 18 collaborating with another team. >> From one partner to their customers, to their customers customers.
Expanding opportunity. >> Earning trust, protecting fundamental rights. >> Advancing sustainability. >> For our society. >> Our economy.
Our world. >> That is the impact each of us can have. >> That is our collective opportunity. >> 220,000 employees. >> Empowering 8 billion people on the planet.
>> We are in a new era of A.I. . >> Where we are able to build as fast as our imagination. >> Unlock creativity. >> Innovate responsibly. >> As we take on society's greatest challenges. >> Together we can ensure these benefits reach every country, every industry and every individual.
>> When we empower every person in organization on the planet to achieve more, we empower the world. >>> Hello everyone. All right, it is fantastic to be back here in India and back here in BANGALORE, it is always unbelievable to come back and see the energy and excitement.
Especially at a time like this. We are perhaps entering this next phase where we are going to go from talking about A.I. and admiring some of the new capabilities weather they are infrastructure or models to doing things with A.I. that are bold and big.
That is to me, what is really in the air when I come here and see people excited about what is happening and what you are doing. I want to spend the next half hour or so giving you at least what I see out there as the possibilities and what we at Microsoft are focused on in terms of building platforms. To me Microsoft has always been fundamentally about two things.
We are a platform company and a partner company. Even in the age of A.I. that is what is true. Whenever you talk about these platforms and platform shifts, you always need to be grounded in what the foundational forces that is driving the platform shift. When I look back at my 35 years in text, it has been about one fundamental force, which is Moore's law.
Bill would bring us a bunch of us together every year and he would say what is happening with memory and just fill it was software. That was the single instruction to the company and it is true even today. When you think about the scaling laws empowering A.I.
and pretraining in particular, it is Moore's law at work again. It started in 2010 with the an end's and then obviously the GPU's. They inflected again perhaps with transformers because of the efficiency of data parallelism with transformers of what was happening with doubling the capacity every 18 months and then every six months. That is really what the scaling laws were. By the way there is debate with what is happening with scaling laws for pretraining and will they continue, we fundamentally believe the scaling laws are absolutely still great and they continue to work but they do become harder as the sizes of data become higher and the parameters become higher and the systems problems are bigger. These synchronous data parallel workloads are new workloads so therefore that will continue but more interestingly, you are starting to see another scaling law with the inference time or test time compute scaling law.
At some level pretraining had the sampling step. This is always about using that sampling step. So it is about being able to scale even with inference time that will take this to the next level. We are very excited about it.
To me copilot think harder is the thing I go to all the time and that shows not just for sampling for additional pretraining but to be able to use it during inference time to think harder and get you better results so I think these are capabilities that are just going to increase ultimately three things. One is a multi-model capability we now have as the interface to all software. I recently set up, I do not know what they call it, the action model now on the iPhone, it is set up to copilot for me and now I can confidently speak and it is beautiful.
It understands me. It is like speaking to my high school buddies. The fundamental ideas that you can now have that familiar, simple interface with all of computing I think will change every software category.
Then you couple it with planning and reasoning capabilities. Whenever you bring up copilot workspace, that thing is invoking that ability to think about planning and executing that plan. As a multi step process. The beginning is what is truly Genentech tree behavior. The other side of it is to be able to stitch these things that are outside the system or outside the model.
Memory, contexts, tools used, and entitlements. So in fact if anything in the next 12 months, every developer is going to be really focused on how to take the model and make it aware of the tools it can use. Just beyond even function calling. How can I make sure it has the right understanding of its entitlements? How do I make sure it has memory.
Long-term memory. That to me is what will truly help us create this rich tapestry of agents. We think about agents used that's the multi-model capability, planning and reasoning and memory and tools used in particular plus entitlements, you can start building personal agents, team agents, enterprisewide and across enterprise agents. That world is what we are looking forward to building. At Microsoft for us it is never frankly about any of these technologies on their own.
It is a means to a and, it is about empowering every person and every organization on the planet to achieve more. That sense of empowerment that this platform can provide I think is going to be next level and that is what we are really focused on. To that end we are building three platforms. Copilot, copilot, A.I.
stack as well as copilot devices so I want to give you the broad contours of what the three platforms all entail. To conceptualize copilot, it is the UI for A.I. One of the ways you think about, even in a rich and Jen Drake world, remember the A.I.
needs to interface with us, that means a UI layer and that is why I think this organizing layer of copilot becomes even more important in a world where there are many agents doing autonomous work. I think that is the best way to think about it. The approach we have taken is to build the copilot into the existing workflow. One of the best examples I have seen frankly is one of the high- stakes things of knowledge work.
There is a doctor, she is getting ready for a tumor board meeting. It is a high-stakes meeting. It means you would have had to have read all of the reports, know exactly how much time to spend on each one which means the creation of the agenda is a reasoning task. So it creates a agenda which knows which one is the more complicated case that needs more time and then you go into a teams meeting. So all of the doctors are now having a conversation on all of the cases.
They are able to focus on the case as opposed to taking notes because there is a A.I. taking detailed notes on all of it. Then at the end of it she is a teaching doctor so she wants to be able to take what happened at the tumor board and go to class which means she is able to take the notes, put it into word and then into PowerPoint and go to class, that is simple workflow that doctors everywhere are doing. It has impact on lives. It can be enabled with A.I. being built into the workflow so that is a good example of how to think about A.I.
being infused into current workflows. Now let's take it to the next step. Now with pages and chat, with wet and workscope, I think it is completely about thinking of two types of workflows. Fundamentally I am now able to access information whether it is web information or word information inside my Microsoft 365 graph, one quarry at a time I can get the data and I can then promote the data into this interactive A.I. first canvas called pages and once I have it in pages I can then use copilot in-line in pages to keep modifying it. I use this as a metaphor when I am thinking with A.I.
and I am working with my colleagues. So think about that. That is the new workflow where I think with A.I., I promote things into pages, I invite others, collaborate with others and by the way A.I. is present even on the canvas so chat plus pages is going to become the new A.I.
hub for just how Word, Excel and PowerPoint changed how I worked, now chat plus pages will be a new module that effectively enhances how you work with A.I. Now we are not obviously stopping there. The next thing we are thinking about is extensibility.
So how do you extend A.I. going forward? It starts with something called copilot actions. For those of you who are big users of Outlook rules which I was for a long time until the complexity was too much, think of it as rules for the A.I. age but they do not work one application at a time, they work across the entire 365 system so that is the beauty of actions. If you think about so much of the workflow is gathering information, distributing information.
It is about connecting people to other people to other artifacts, that is what a lot of knowledge work is about. I can set these up essentially as copilot actions. That is the first extensibility. Of course you can build full agents and we ourselves are building many of these agents that have scopes at group level and process level.
You can have a project agent. We have agents working inside of teams like a interpreter or a facilitator. It is like having a additional team member that is helping you with your tasks inside of your team. SharePoint, in fact every SharePoint now has a agent. Think of it as a intelligence layer on top of SharePoint that is just built in.
I just want to roll a video to give you a flavor for the agents being held in 365. >> New agents and Microsoft 365 are transforming how humans and A.I. collaborate. Meet agents and SharePoint which unlock high-value insights that are connected to your organizations documents.
Created with just a few clicks the agents enhance knowledge sharing. They can be customized with additional data sources and copilot studio and can be shared anywhere like teens chat. Next is the facilitator agent. Joining meetings to manage tasks like the agenda, real- time notes and action items.
Allowing the team to stay focused on the discussion and the facilitator agent can streamline communication in chats by providing real-time summaries and responding to questions so the team can focus on what matters. With the new interpreter agent, language barriers are a thing of the past. Enabling real-time speech interpretation so everybody can speak and listen in different like witches. >> Marketing about the upcoming campaign, if we get a game plan -- >> The project manager creates project plans and assigns tasks and creates them on behalf of the team keeping everybody informed and collaborating effectively.
Finally for specialized business processes in HR and I.T., the new employee self- service agent in copilot service chat enables employees to get instant answers and take action such as locking a help desk ticket and it can be customized in copilot studio using prebuilt workflows and more. New agents and Microsoft 365, supercharging productivity to reinvent how work gets done.
>> Said that is just a example of agents we have built into the system. The real exciting thing is of course you all being able to build agents and that is where copilot studio comes in. Our vision with copilot studio is simple. This is the low code no code tool for building agents. Think of it like when you had Excel and you could build spreadsheets. To us building agents should be as simple as building spreadsheets so copilot studio is about helping everyone of us to have real agency, to shape and reshape the workflows around what we are doing as knowledge work.
That is essentially what we want to build. A swarm of agents around what we do that is helping us get work done. Create more flow, less drudgery.
Copilot studio, take something like field service. It is as simple as first giving it a prompt, giving it the instructions on what the agent is all about. Then it is about grounding it in knowledge sources. In this case it is about pointing it to the right SharePoint source. Then once you do that it creates a agent out-of-the-box for you. So that simplicity of being able to create agents that are essentially low code no code programmable is what we are doing with copilot studio.
Soon now you have this copilot UI for A.I., you have the ability to extend it with actions, you have the ability to use agents that are built-in and build your own agents. You have a complete system. Now the question is, ROI and measurement. That is the other question which is, one of the fundamental things we also want to ensure is that there is real motivation for change. After all what am I doing that is better, that is helping improve not only my own productivity but my organization's outcome? That is where this measurement comes in and we are building out this copilot analytics so every individual, it is not just a top-down thing.
A sales territory manager Conoco in and take a output metric, something like increased sales, increased yield, and correlated back to specific usage of all of these copilot features so to meet that is another one that adopts the cycle. You are not waiting to see in real-time how with increased usage were able to drive business results. So that is really the first platform we are building. Copilot is the UI for A.I.
with extensibility and measurement. Now we are already seeing fantastic results inside of Microsoft. Basically we are baking in double digit, strong improvements to productivity across every business process. Customer service, HR self- service, ID applications, finance, supply chain, marketing. Think about marketing where there is sophistication in buying but there is a lot of places where there is a lot of inefficiency around content creation, there is massive amount of leverage and operating leverage we get there. So significant use cases across the length and breath of our own company and of course when I come to India and I get a chance to meet with everyone here it is no longer the diffusion is so fast.
It is no longer about waiting multiple years before it becomes mainstream. I learned a lot by watching many of you deployed this at scale. This morning I had a chance to talk to folks at cognizant, they were telling me how they deployed it across the entire employee base.
One of the things that Angie Grove back in the 90s talked about was knowledge turns, about being able to create knowledge fast and really be able to then defuse the knowledge, just like supply chain turns, people talk about them in set of retail. This is about knowledge turns in any knowledge industry another example is what persistent is doing, the I building contract management agents. In fact you can go address it at persistent whatever contract A.I. inside a copilot you have access and that agent is available to you throughout the entire lifecycle of the contract management or even a single change can have significant impact. So these are two examples of people already deploying this copilot system at enterprise scale.
So now I want to talk about the next platform which is the copilot stack and A.I. platform. To us, we always conceptualize and build AZURE as the world's computer because one of the fundamental realizations is A.I. does not sit on its own so we are building it out at a worldwide level.
We have 60+ regions, 300+ data centers around the world. In India we are excited about all of the regions we have, central India, South India, West India. We also have the capacity we built up with Geo. So we have a lot of regional expansion happening and I am really excited today to announce the single largest expansion we have ever done in India by putting 3 billion additional dollars into the expanding of our AZURE capacity. I had a chance to meet the Prime Minister yesterday, it was fantastic and it was great to listen to all of his examples. His vision around how he wants to drive the A.I.
mission but it is the combination of really the -- He has, the India stack, the entrepreneurial energy in the country and the demographics both on the consumer and business side that are all working into the psycho which is why we feel fantastic about bringing core compute capability for the next generation A.I. So now with infrastructure, there is in some sense a new formula quite frankly for any country or company. I think of that formula as tokens per dollar per watt. That is it. Two years from now, five years from now, 10 years from now we will be talking about the correlation frankly between GDP growth in any community or country, any industry or even at a company level, fundamentally their own growth on how efficiently they are able to drive that equation.
To that and it means infrastructure. Infrastructure, infrastructure needs to be the highest priority and we are innovating at every level of it. At the Dana center level, these data centers, everything from how we think about even the construction of a data center that is optimized for liquid cool A.I. accelerators.
It is a new engineering feat. So that is what we are doing. Everything from how we then work upstream from us with renewable energy folks to get onto the grid-based load that comes to our data centers that then has the right cooling infrastructure with zero waste and zero water usage.
How do we really make sure all of that gets built into it at a system level and then of course silicon innovation, we are innovating with Nvidia, in fact today we have our first GB 200 clusters live in one of our data centers. Excited but what that will mean. We are then working with AMD building out our infrastructure , we are building our own silicon, all of these, Meyer is taking a lot of the customer service traffic in Microsoft's website. So we are building world-class A.I. accelerator infrastructure but the entire system stack, optimized for training, optimizing for the kernels for inferencing, that is significant investment and innovation that is happening through us, our partners, so we are very excited.
I think of this as a golden age for systems when it comes to innovation. Now if you have the infrastructure, the next big consideration, in fact today I met many partners, many customers and the first thing nobody wants to talk about is how to get my data in shape. Data is the only way to create A.I.
It is not just for the pretraining, we know for rag you need data, we need data for post training, we need data for doing sampling, inference time compute retraining, data pipelines is everything. The first thing to do is to rendezvous the data with the cloud and that is where we are building out our data estate such as you can bring all of your data, you can bring whether it is snowflake, data breaks, overcall, our own SQL, what have you, bring it to the cloud, we have fantastic operational stores being plumbed for A.I. whether it is Cosmos database source SQL hyper scale or fabric for analytic workloads. All of these are the are ready. In fact if you look at chat GPT, there are some of the biggest users of Cosmos database. Where is all of the users state of chat GPT, it is in Cosmos.
So to me the data layer is a super important later layer and we are doing everything to ensure that we can help get the data in shape for you to be able to then use in conjunction with the models and build models. Those are the two things. There are models being trained on the data but you're also doing things like retrieval augmented generation using data so that is why the proximity on the data gravity is huge and the locality of data will matter. Now once you have the infrastructure and data the third thing you have to do is to have that A.I. application server. When you look back at the web, we built the application server, when the cloud happened we started building the cloud native application server, same thing with mobile so every generation has required the application server and that is what we're doing with foundry and with foundry it starts with models.
We have the rich and obviously with open A.I. and its innovation. We are excited about what will come with 03 and 40, all of that is available in all of the open-source models whether it is from llama war others, we have industry specific models. Models being built out of India. Where people are building for languages, India's specific needs, it is fantastic to see the amount of innovation people are doing around models all over the world.
So we want to have the richest model catalog and some of the more popular models will be available even as models of service so just under a facade of the API that you can then go access. Now what you have the models you want to deploy the models, you want to be able to fine- tune the models. You want to be able to distill the models. You want to be able to do evaluations on the models, you want to do be able to a ground test, you want to do safety, all of that instead of building them separately we are building them into the applications. Emails will be the most important thing. The guidance I give our teams is simple, stay on the frontier of the new model and make sure you have agility in the application server layer so you can keep moving with models.
You will use the latest sample and then you will cost optimize it, Lindsey optimize it and then you start fine-tuning it for your specific use cases for emails, so that is the loop you are constantly going through and that is the idea behind foundries, to just streamline all of it. Fantastic momentum again in India when I look at customers who are already deploying this, using it, in fact a lot of good feedback. I am getting from a lot of people. People are pushing even on the multi agent deployments so we are learning a lot frankly from some of the ambitious things you all are doing and we are definitely very grounded in how we will progress on that roadmap. I think next year we will not be talking much about models but we will be talking about model orchestration, emails and how you're able to deploy these model forward applications.
That I think will be the big shift across the industry. So today, when I saw the folks at the bank, they showed me three things, they built a self- service agent, for the new customers they built a relationship manager agent, they also then built a agent for their own employees. I had a chance to see this fantastic startup, clear text, I should be able to do my taxes on that, it is simple. You submit your receipts I guess and then you get refunds.
I love that part in particular. Then I had a chance to see the long but folks, one thing I realized in healthcare for example, you do not have standardized claims forms. So each one is a different entry and somebody has to go read it and that is where I think you can improve. You think anytime you improve healthcare efficiency it improves the economy because then you'd then have insurance of your insurance being taken care of. To make my trip I had a chance to meet with the team and they are doing phenomenal high ambition work of being able to take one of the most sophisticated industries around travel, really whether it is hotels, air, or other transportation, how do you really have a multi agent framework that they can deploy. One of the other things is, it is not just about the big broad companies were startups doing it but it is the diffusion rate of the technology in India, that is what is exciting.
To that and I wanted to show you the video from the cooperative. >> In India we are producing some of the major crops in large quantity like sugarcane, wheat, rice, and cotton but when we compare -- Yield with other countries it is too low. -- Is the major problem in India today. >> -- Working for the community -- Improve the farmers -- Saving the lives of millions of farmers.
>> -- A.I. -- Use science -- To make the right decisions and make them successful. >> In this project we have selected 1000 progressive farmers across the --, We have installed weather stations -- And provided satellite support to all of the farmers. We are collecting real-time data from the soil every day.
>> Agro pallet -- For agriculture for making the decisions, it is running more than 20 algorithms that can lead to accurate results based on a historical pattern. The A.I. allows the farmer to ask questions on local languages which can be delivered on what's up. >> -- Irrigation and pesticide practices on their farm.
>> To me it really connects all of the dots, it connects the dots even between all of the technologies we are building from AZURE IOT, and the dedicated connectivity to the data plane and something using AZURE to empower a former to do their farming with high yields. That I think in some sense speaks to the power of this technology and what we can do with it. The last layer I want to talk about, you have inference, data, the A.I.
application server, it is tools. I always go back, Microsoft started as a tools company and we continue to be super passionate about our tooling. It is fantastic to see what is happening. We now have 70 million members of get hub for in India.
It is the second largest community next to the U.S. In fact it is projected to be the largest I think 2028 so I cannot wait. Three years from now. 2028 is when the crossover will happen for India will have more developers. It is exciting to see that.
We also have contributions from India to A.I. projects that are second only to the U.S. so it is fantastic to see the active involvement of the developer community out of India in making progress on all of the open source projects on get hub when it comes to A.I. It speaks to the talent there is and the energy there is in this community.
We are continuing to make great progress on get hub copilot. One of the features I was looking forward to eye with excited to see we have. Multi file edits.
We started with continuations and then we added chat, we brought continuations and chat together and then brought it to multi file. That is fantastic so I can do repo level edits. The other thing we are also doing is bringing a three-tier, in fact we launched it in December and in India it is the place where it has really taken off. We are very excited about bringing get hub copilot three- tier and seeing that broadly being distributed. The feature or product area, I forget now, 2019 now, maybe 2020 is when I first saw get hub copilot. It is where my own conviction on what LLM's can do changed when I started seeing it.
Similarly, when I first saw get hub copilot workspaces when I felt the time had come for us to make the next leap beyond chat to real agents because that is what it is. Copilot workspaces the first and Jen take peace where you now can take a get hub issue, create a spec which you can edit, you can create a plan, you can edit the plan and then you can see it execute across the full repo. To show all of this I want to invite my colleague on stage.
>> My copilot offers contextualized A.I. existence in the developer environment. Copilot workspaces the next evolution a inch intake A.I.
native developer platform that helps you turn your ideas into code using natural language. Let's see it in action with our computer. All right we have application here that lets us so sporting equipment online. However this new way to add new products through a admin page. Let's see if we can build when using copilot workspace. I have a issue here that describes my anger requirement which is a admin page to add new products.
Usually I would create a new branch and start thinking about how to implement it myself but with copilot workspace I can get started right here from a issue. Copilot workspace gets to work right away on how to solve the issue. I am in control so I can make edits to the plan as needed.
With the new brainstorm agent you can use copilot workspace intrinsically. Based on the concepts uses a few transgressions to get started or I can ask my own. On the admin page we are creating I would also like to add a requirement to upload product images. So let's ask brainstorm with copilot workspace to see if he can make it happen and this time I will ask it in Hindi.
All right there it is, just like that it is listing a few ideas for me. Using a drag-and-drop area or a library. Since I already have existing component I'll use that. I can click add to task and add it to my context right over here.
It looks good so I will click generate plan to move forward. Workspace then generates a list of what changes to make in which files. When I am ready I can ask copilot workspace to implement the plan and make the necessary changes. There it is. That is nice.
Copilot workspace has made changes to multiple files including writing a new page. With copilot workspace you can now keep changing your plan innovation by asking for a revision. Let's ask copilot for a revision. I would like it to ask it to show me a preview of the image I upload and the some I will ask it in Canada. There it is.
Copilot workspace understood what I meant. It updated the plan and also implemented all of the necessary changes. That is great right? Before we proceed further let's run some tests to make sure my code does not turn into a horror story production.
We made great enhancements to the commands you can perform in copilot workspace. Build, test and run our common scenarios. I can execute the test command which will run the test for me right here within copilot workspace.
It looks like my test failed. My bad I thought my code did not need test but it is copilot to the rescue. I can ask copilot workspace to fix this for me. The build and repair agent within copilot workspace comes up with a solution on how to fix this error which I can apply. Then it goes back and makes the necessary edits. In this case modifying the test.
So let me go back and rerun the test hoping that I am as good as the test writing things I might be in there it all works. Great so I think my feature may be ready and I want to see a preview of my application. I can execute the run command right here within copilot workspace which will bring up a development server that is accessible right over here and I can open up a preview of it. There is my admin page.
Along with the image upload functionality I needed completely written using copilot workspace. So in the past few minutes we went from a idea described on the issue, brainstormed with copilot workspace, implemented code and also fixed errors. All while working in natural language. And this is the A.I.
native developer environment. A inch intake workflow that moves as fast as your creativity. Thank you everyone. >> Yes it is really exciting to see the progress in the development toolchain.
As of today there is no more of a wait list for copilot workspace, we are excited about that. To me personally, perhaps the vaguest game changes were Windows 3654 I have my desktop plus get hub copilot workspace and code spaces, you put them together and put me anywhere in the world and I am a happy person so this has been a wonderful change in terms of development productivity everywhere. The last thing I want to talk about is the copilot devices. We talk so much about all of this innovation and infrastructure. Starting with silicon in the cloud, now that is coming to the edge.
We are excited about the work we are doing with QUALCOMM, AMD, Intel, when it comes to the MP use, in fact Jensen talked about some of the next generation GPU's coming to regular PCs. Which will be able to really run the entire NVIDIA stack locally so we are excited about what is happening with copilot in copilot PCs and even traditional PCs with GPU's. But we are also excited about the fundamentals. When I use my copilot PC, having the battery life last through the day, having these new A.I. features that are built in like copilot being built and.
Third-party developers are beginning to start using it whether it is Adobe or Capcom or others. So it is a real beginning of a new platform on the edge that will be as exciting as what is happening in the cloud and in fact we do not think of this as the old client server, it is not about disconnected local models, it is about hybrid A.I. The idea that you now build applications where you will be able to do a bunch of the stuff on the local MPU as helpers classify in here and calling LLM's in the clouds or any application will truly be a hybrid application it is not about running locally are all in the cloud and that is what we have to look forward to.
Let's play the video to give you a flavor for everything that is happening with copilot devices. >> [Music] [Music] >> Now in order to really ensure that these three platforms get broadly distributed and used, the key consideration is trust. Trust around security, privacy and A.I. safety. So we have a set of principles but more importantly these principles and initiatives are grounded in making real engineering progress so we can effectively in short, ensure trust along the way.
Take something like security. How do you protect against adversarial attacks like prompt injection. That is the key thing we are building in. Take privacy in A.I., what does
it mean to think about confidential computing not just for the CPUs but in GPU's? That is what we now have with everyone whether it is AMD, Intel or NVIDIA . Or when it comes to A.I. safety.
One of the big things people talk about his hallucinations. How do we insure groundedness? A groundedness service with eval support is one way for us to make real progress on A.I. safety. So we are taking trust as a first-class engineering consideration. Having a set of principles but more importantly translating the principles into essentially the toolchain and run times that allow us as developers to build more trustworthy A.I.
Now I want to close were I started. Which is our mission. To empower everybody but before I go there the thing I want to talk about is, all of this is about driving A.I. business transformation, changing customer service or about changing your marketing or sales or your internal operations, at the end of the day it is about business results. The three consideration I would submit are around copilot as the UI for A.I.
It is about being able to make sure you are thinking about the application server as the platform around which you build your A.I. applications which for us is foundry and your data fabric. These are the three key design decisions that need to get made, more so than any given model because models will come and go every year, every month, you will have a new model but the three foundational design choices is really the UI layer, how do your agents interface with the UI layer, how you think about your data and how you think about the application server which gives you agility on top of models, those are the three considerations. That is what you hopefully get through what we have done throughout the platforms.
Our mission to empower every person an organization right here in India is what drives us and to that and ultimately it is about being able to ensure the human capital of this country is able to continue to scale to take advantage of the immense opportunity to potential the technology has. So that is why I am very excited to announce today that our commitment which we always had, now is to train 10 million people in India around A.I. skills by 2030. To me the thing that is always most important is to not think of the skilling in the abstract but to see the skills translated into impact, right here, one community at a time, what industry at a time so I leave you with a video of the impact all of the scaling is having here in India and thank you all very much and thank you for all of the great work you are doing on all of the platforms. Thank you.
>> I was always depending upon the help of cited friends. >> I gained more technology knowledge. I learned many things including A.I. and copilot. >> When I joined I learned A.I.
tools. >> If I am stuck somewhere copilot helps me solve the problem. >> With A.I. I can be more independent. >> No I work in a multinational company. >> -- Visually impaired teachers and students -- >> It is a proud feeling to become an engineer.
>> They provided this training and now I have this confidence. I am able to do this. >>> President Microsoft India development center and president of Microsoft India. >>> Good afternoon Bangalore, how is the energy? Yes, welcome folks. We are so excited to have you, we are thrilled to have you and I want to start with a bit of a apology. The expedience coming in was not great thank you for your patience, think you for coming and spending the day for us.
We are also live streaming this so in case you missed any content we will make sure to get that to you. Thank you for being here. This will be fun folks. We will make it fun and interesting. I am with two of
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