Go Dragonwing. A very warm welcome to all of you. We have over 500 people in the audience, many people watching online. And this is really incredible. You know, we've been in the process of transforming our IoT business over the last two and a half years. And as a part of this transformation, we are actually transforming ourselves. We're transforming the company. You've been working with us for so many years across so many different products, so many different ecosystems. And I think what is true that I'm sure all of you will agree is that the world is going through digital transformation and now physical transformation. As you start to think about robotics, as you start to think about what it means in this new space, everything is fundamentally changing and we are on a mission to really be able to help you accelerate. You are our partners. You are our ecosystem. Help you accelerate how this change can be brought to what is in front of us.
We have so many different fantastic customers and partners in the room today and so first of all big round of applause to all of you and thank you very much for being here. We have a packed session. We have a lot of stuff in front of us and I'm going to dive deep. But before I do that, I have the privilege of inviting our president and CEO and my boss Cristiano to the stage. Cristiano.
Thank you. Thank you a lot. Good morning everyone. I'm very, very happy to be here. First of all, I know all of you are very busy and have a lot of places to be. So we take it, you know, with incredible gratitude that you're here spending time with us. I've been in the company for probably more than 30 years and I've never been more excited about Qualcomm and technology as I am right now across a number of different businesses. And I think what's really happening is we're building a new Qualcomm, and we're building a new Qualcomm not only with new products and technologies but also with new partnerships. So I am super excited about the number of companies and all of you here because we can build this incredible future together. So what I'm going to tell you, I won't spend a lot of time, but I'll tell a little bit about the vision for the company, what are we doing, and some of the opportunities that we have in front of us, especially in this area of industrial, I think the evolution of IoT and robotics, and that it's an area that has been incredibly transformed by technology and AI.
So for the ones that have been with Qualcomm and working with Qualcomm for a long time, probably know what we have done in the mobile industry. In the mobile industry, we were the most focused company in mobile technology, and that was actually a good thing because the mobile device is an incredibly challenging engineering problem because you have to increase significant amount of compute, significant amount of capabilities on sensor data, connectivity, and you have to make it fit in a small form factor. You have to manage thermals. You have to manage energy. And we realized that that technology could be very disruptive and powerful across a number of different industries. And it kind of went alongside, I think, the desire that we had to grow and diversify the company. And it kind of... that's... I'm going to walk you through this journey. I think, and we have from mobile, we realize that what's happening with automotive is a big transformation with technology. Cars are becoming computers on wheels, and we built an automotive industry. With that, we got a lot of capabilities about building industrial systems, safety-grade systems, deal with complexity of a supply chain, and we're very proud of what we accomplished. A lot of the credit goes to Nakul and his team for building the automotive business, and I think it actually set a very important foundation for what we wanted to do in industrial.
We also looked at all of the different devices that we interact with every day, and we saw that those devices are going to fundamentally change with AI. And we went in to build a new category of PCs, a new category of personal AI devices, because workloads are going to change as UI takes a role in the human-computer interface. And we apply all of this to where we are right now: how we can actually use all of this technology to transform industrial networking, robotics, embedded IoT. And I think that is what we're building with the Dragon portfolio. As Nakul outlined, we had to change the company, including the way we develop our own products, to co-develop with many of you, make our technology easy to access, pervasive. And that's some of the things we're also doing to change the company with software, with the recent acquisition of Arduino. We're actually building an open-source ecosystem for AI across multiple devices in the cloud. And we most recently... it was a natural evolution of Qualcomm to go into the data center. And into the data center, we actually bring a different philosophy coming from the assets that we have built, which is we don't assume that energy is always available. It has to be efficient. We assume scalability. We assume hybrid AI, and we assume that total cost of ownership matters.
And what is interesting when you think about a data center business, we've been talking for a long time about hybrid AI. It's very interesting. I'll tell the story. I often remember to the multiple generations of wireless technologies, different instants in time where people thought that you didn't have to have a lot of computing at the edge. I go all the way to the fact that companies like Microsoft, you know, thought that the phone will never become a computer. And I think you saw the change, the opportunity that got created for companies like Apple and Google. I remember when webOS was launched and people said every single app was going to be on the web, on the browser, and you don't need a computer on the edge. And I look at today the conversations about is this edge or is this cloud. But there's something very interesting that happened: open-weight models and on-prem happened. And once you accept that you may have an on-prem or an open-weight models and you have some big models in the cloud, by definition you're accepting that AI is hybrid. And then how much AI you do at the edge is just the kind of computer you want to build. And we're building technologies to be able to, in industry like in robotics and industrial, to have the ability to run things on the edge, not only to keep industrial data or your data private, not only for the ability to have a more economic approach to processing, but for resilience. And it's not about edge, it's not about cloud, it's about making, you know, the computer and AI run where it's more efficient with incredible connectivity and networking in between. So that's kind of what we're doing.
I was just finishing my conversation to say we're very, very happy to have this event right now. As Nakul outlined, we spent more than a year preparing for this: how to change your company and our products so that everyone could develop on our platforms, can scale on our platforms, and design our products and solutions to exactly what you need. And I think that's the mission that we have when we think about what we're doing in embedded IoT, industrial, and robotics. And I'm just going to finish this with a comment about robotics systems and how we think about the computing on the edge. It was fascinating to us when we started to think about industrial systems and robots that you can think about this problem as one that you have to have a gigantic world model that you train machines on everything and everything runs on the cloud. Or you can think about this problem the way we work ourselves: we go about the world and we have to adjust and we have to learn in real time. And therefore you needed to design those systems in the same approach with different specialized compute and subsystems. And then once you get those subsystems, you see that they can be applicable to a bunch of different industries.
The opportunity is really endless. This is one of those areas that I actually believe is an unlimited addressable market, unlimited because of the value that exists of transforming with compute and AI entire industries, transforming the edge, transforming the systems. And what is great about this exactly creates opportunity for innovation regardless of the company's scale. Companies, you know, in the 80s and the 90s, you needed to have a very big scale play to be able to be competitive. Now with technology, you can innovate regardless of your size and your industry sector. And we wanted to be a partner that enables you to do that. Thank you so much for being here with us. We're very proud of having partnerships with all of you, and this is an exciting time. We are super focused in this. I think it's very easy for semiconductor companies right now to be chasing the shiny objects of the data center only. We are very focused on this. It's very important for the future of Qualcomm. It's the same exact process we took into other industries such as automotive, as an example. And I can't wait for the future we're going to build together. Thank you so much. Have a great summer.
Thank you, Cristiano. Thank you very much. You know, we are a technology company. We have been... the basis of the company, the foundation of the company has always been tech. And so we have seen our share of disruption, whether it's in wireless, today it is in AI. And when you go through disruption, you have to be able to predict, you have to be able to imagine what the world is going to look like as you get through the disruption. And for us, we spent a lot of time, we spent the last couple of years starting to think about what is actually changing. Because, you know, when you start to think about IoT, the first thing people say is fragmentation, like, oh, this is a bunch of different things that don't talk to each other. And that was true if you talked about this 10 years ago. It was mostly about connecting things that were not connected, but it was mostly about connecting them to things that were more or less the same: IT networks, things that were essentially inaccessible, they were locked up, etc. And this has been going on for, you know, at least 15 years, maybe 20 years. So we've all lived through IoT in the context of connectivity for a long time. And so people started to think about connectivity as IoT and vice versa, and compute wasn't really a thought process.
What we started to spend time on was, first of all, are you going to be able to do anything at the edge without powerful compute? And compute I divide into general-purpose compute, machine learning, and AI. And these definitions have changed, are changing massively, and I haven't even included robotics in the mix. So it has to be connected, it has to be able to compute, it has to be able to understand data, and it has to be able to understand what is the outcome you're looking to get and being transported. So there is a fundamental reimagination of what is actually happening at the edge. I give you four examples here, and they have nothing to do with each other, and yet they have everything to do with each other because they're all the same. Every single one of these examples, you know, I have James here from Zebra. We work with Caterpillar. I'm sure they're in the room. No commonality in their businesses. And yet, if you think about it, you want to be able to make decisions at the edge in real time because they will be able to improve your productivity. They'll be able to reduce risk. They'll be able to make your workflow efficient. But you have to be situationally aware. What does situationally aware mean? It means time-series data. It means image data. It means the ability to be able to sync these things together. It means being able to collaborate. And that is what I call both digital transformation and physical transformation. Because in the real world these two things actually coexist.
With AI, the big thing that we are seeing that is changing is that people are reimagining what can they actually do with AI. We will, through the course of this presentation, talk to you about robotics quite a bit, and there is a lot of focus on robotics. It is unbelievable how quickly tech is moving. You know, all of you are using cloud or ChatGPT, and you are all living a very different life than you lived three months ago, even six months ago. We are all living this at the same time together. We are all mid-career, late career. We are all seeing that this is very different from what it ever was. Now imagine what the world looks like five years from now. Imagine what our kids are going to want to experience as they become adults in these spaces. The other big thing to take away is there are a lot of jobs people don't want to do. Nobody wants to go clean the toilet at, you know, at the grocery store. That's not a job anybody wants. Nobody wants to be a security guard. There are jobs that physical AI is going to change. And in some parts of the world, we all travel, in some parts of the world, this is actually much more advanced. Societies accept what are the things that they are willing to be able to actually enable in their ecosystems, in their societies, because it is okay for automation to move faster. This space is going to accelerate. We break this down into this pattern. There is stuff that's happening at the edge. You have to be able to extract information from the edge. You have to be able to process it to where it's concentrated, it's aggregated, apply logic, apply AI, and then send it to a decision-making framework. I will keep coming back to this because you will see this common thread.
I really like this slide because I have spent a lot of time thinking about how to tell this story from Qualcomm's point of view. Look, we are a smartphone company, and that's our heritage. And before we were a smartphone company, we were a wireless company. So before there was anything called a smartphone, it was called a feature phone. And before there was a feature phone, you just wanted to go... you know, I started with the company in 1995. We used to do 9.6 kilobits per second of data, and we used to make a voice call. So there was no smartphone or feature phone back then. So if you go back long enough, what you realize is that the world is advanced, but there is a human aspect. What do humans do with their devices? What do machines do with technology? And then the latest one is what will robots do? And it's more or less the same, right? We have people that are wearing body cameras. Body cameras are worn by law enforcement officers. But, you know, Walmart associates also wear body cameras because you have an earpiece. You want to be able to see who the customer is. If it's a loyal customer, it's going to give us information. This is something that is part of what you are willing to carry on your person in your physical space with AI. What does that mean? We are all seeing what Meta is doing with Ray-Ban. We've been their partner with Ray-Ban for a number of years. This is a new thing: wearing glasses and actually looking at the environment around you. When did that suddenly become a normal thing? So this shift on the left is actually going to start to become more and more relevant. But it is a constrained space.
When you look at machine-facing intelligence, this is really interesting because machine-facing means there has to be infrastructure. You actually have to deploy it. You have to be able to have the presence of a machine in the environment that you're trying to automate. Whether it's a store, you know, we talked to a number of retailers, and one of the big things is, hey, I want to be able to keep the store open at night. There are no associates, but I want to be able to still be aware of risk about, you know, about the health of the store, about the safety of the store. That means you have to build new infrastructure. That is something that is now a new thing. And we will talk a lot about robots. This to me is probably one of the most fascinating spaces because it has to do with really automating what humans do, and that in and of itself is a complicated topic because what exactly can... what part of a human's job should be automated? What is difficult to automate? Where do you need to use a tremendous amount of intellect versus where is it simply physical labor? So we'll talk about that.
You know us for Snapdragon. Snapdragon is what I call the 'I know you and you know me' type of business. It's a very intimate business. It's a business where, you know, that's my automotive business. I know every single customer and their families, and we've known them for a long time. But that is not the world that we are in anymore, right? And so we made two key decisions in the transformation of the company. The first one is Dragonwing, which is... I love the fact that there are so many people in the audience, and you are our customers, but you're also our partners, because what that means is as we get more close, as we get more intimate, you become the family that we are building, and you carry this message forward. And to do that, last year we decided that we actually needed to be very developer-centric. We are not a developer company. We never have been. So we made the decision that Arduino was one of the best platforms globally. Fabio, who's the CEO of Arduino and now part of our team, is here in the audience. We decided Arduino would actually be a fantastic addition to the company because they touch so many different developer ecosystems. They have this mantra of openness, of Linux, of upstream, of community-based development, and it's really about empowering people to be able to go build the projects that they need to. So we've taken that to heart. You will see over the course of the day and the demonstrations, etc. For us, we don't really think about an Arduino product as a Dragonwing product or not. It's an Arduino product. We will enable them with the technology, with the software, and allow them to do what is needed. But the dividend that we get by doing that is what you see in the middle, which is the transformation in the company is you can't actually speak out of both sides of your mouth. You have to be able to embrace that same mantra internally. And so Dragonwing for us now is we enable hardware for you, it's running Linux, you can do what you wish to. If there are things that we have to open up, if there are things that we have to simplify, we will go do that, and that's what the teams are working on. And I think many of you have worked with us on IQ8 and IQ9. I know it's a great experience, even if it is not... it is a great experience.
But look, I think this journey is actually on one slide, the transformation of the company, because we really are trying to be great at everything, and not every customer needs the same thing, and the same customer might need different things. We just have to be pragmatic and practical and sensible about it and be able to meet you where you are headed. The other piece is how will software be developed next year? I don't know. I mean, what you were seeing super agents being able to do, frontier models being able to... we don't actually know what it will look like, but there will be this framework that will still exist because there'll be customers who will want to build hardware by themselves, and there'll be customers who will just say, 'I just want to go run my application.' We've acquired seven companies in the last two years. So, you know, we do walk the talk, and a large part of my team is actually bringing in cultures, people that could not be more distant than the traditional Qualcomm mindset. Folks that are working all over the world. I think I probably have people that are in the most number of cities. I was looking at it and I was like, 'Holy cow, where is that city? I haven't even heard of that city.' But what it does is it kind of forces you to kind of think about what does it take for us to be able to go change. So it has been this experience where we've gone into the deep end to figure out what must we be doing differently, and then how can we actually accommodate, how can we actually adapt to what this new world looks like. And so I'm super proud of the fact that the entire team has come together, we are working as one team for a number of years, and we speak many more languages than we used to. Maybe that's the best way to describe it.
So I think these are things that you are aware of, but I keep coming back to it because this is what I believe makes us very different. You know, Cristiano said something in his remarks that is important to keep in mind. We are one of the few companies that have always excelled at the edge. Always. We understand edge workloads really well, better than anybody else. We've been doing this for a long time. We do it for every type of device, and we can do it because we understand the technology that is needed to be able to make that edge possible. You can't do this if you don't understand it, and you can't do it if you can't have a serious conversation with your customer or your partner. We build all types of silicon. We support all types of software. Those are things that are becoming more and more natural to us in terms of how to go about doing business. We built many different types of products, every type of connectivity product, every type of processor, and we start to do a lot of segment-specific software, either by ourselves or with our partners. And of course, we are massively focused on developers, which we will talk about. The other thing that we've spent a lot of time, and this is still nascent, but we will keep at it, is to think about the world in the context of solutions, because with AI now, people are just looking to solve problems. They don't necessarily need to know every single step. And this makes it very interesting because across this room, I'm sure there are people that have no interest in knowing what the process technology of silicon is. And then there are others who want to make sure that we are CRA compliant and we are 20-year life cycle and we have supply chain understanding. And we also have people who are saying, 'Hey, just give me the artifact and I just want to go automate my workflow.' So we are changing ourselves because we have to be able to speak more than one language. And so our teams are organizing now very differently to be able to go set up for that. I would love to invite James from Zebra, who's been a fantastic partner of ours for a number of years, to share his thoughts about how Zebra is doing in partnership. James.
Thanks, Nakul. Thanks. You've done a great job building up this business. Certainly, we're grateful to be a partner, and you're a very strong partner with us. I know a number of you in the audience probably don't know who Zebra is. I know that sometimes I struggle with my own family, 'What do you do?' But I can guarantee you that everyone in this room has been impacted by the front line of operations of our customers, right? Whether you're being served or delivered a package, whether you're shopping in a retail store, whether you're in the hospital, you're being served as a patient, whether it's logistics, every one of our customers relies on our products every day to deliver against the front line. And so the fact that many of you don't know Zebra, you might be surprised to hear that Zebra is a 57-year-old company. And our heritage started back kind of like Qualcomm. We started back in older technologies. Our heritage is industrial printing and data capture, fixed and mobile data capture for scanners, mobile computers, RFID technology as well. We actually invented the barcode reader. So not the barcode itself, but the ability to read the barcode. We invented the barcode reader in 1973, and we were the first to start creating and capturing the digital voice of inventory and assets. And, you know, so this is something that we believe makes life for our customers, life for us consumers, better every day. And so since that time, we've continued to focus on frontline operations. So we believe, and it's our vision, that frontline operations everywhere must be digitized, must be connected, and by doing so, we can make them ever more intelligent. So this is a really, really important mission, and we believe that focusing on the front line is important because 80% of the workers in the world are frontline workers. 2.8 billion or so of the frontline workers is our opportunity to serve now. And unlike most of us knowledge workers, they're largely underserved by technology. And there's a couple of good reasons for that. I mean, you guys know what token anxiety is. When we talk to our customers and talk about AI and talk about putting AI in the hands of 10,000 associates in a retail store, you get nervous about what could that cell phone bill look like, right? So this token anxiety is a thing. And so, but serving the front line and making them more efficient is absolutely a critical, a critical job. So my team focuses on augmenting the front line, and our formula for doing this is mobilizing software on AI-powered devices. Thank you. Welcome. And combining our, what we've branded as our Zebra Companion. These are on-device AI models that run at the edge. And for us, running at the edge is absolutely mission critical. And I can tell you that our partnership with Qualcomm, we've invested heavily in the 6690-based platform. And we've released a number of products based on that platform. And we're really focused on building products that are multimodal and support multimodal data capture. The ability to capture data and capture signal in the form of barcodes, images, videos, NFC, UHF, RFID, BLE, voice, location. All of these signals we convert into an understanding of the physical environment, the physical world model. We use that model and the context of that to then prompt models on the device to create more efficiency for our associates. And let me give you an example of why that's so important. So by taking these models and integrating them into our customers' workflow applications, you can collapse multi-step workflows into single instances of effort. And I'll give you an example of a big customer of ours, Global Courier Company. They deliver about 15 million packages a day, homes, businesses around the globe. And they require to capture proof of that delivery. It's an important part of their business model. And so we've endeavored to automate that entire workflow. So, and what does automating the workflow mean? It means capturing only the data that they need to the quality that their standard operating procedure requires. Only the data they need, because not all data to capture is good. Faces, people, dogs, we don't want those. But we need this data because we need to prove that we made the delivery so that we can combat in cases where the delivery is challenged, claims. The other piece of this is that because you're collapsing this entire workflow into a single click, single operation, one instance of the AI, we also improve the efficiency of this. So we can add one or two more deliveries per driver per day. And that's where you start to see the ROI become very, very incredible. And what's interesting is the quality of this has been very, very well received because we've taken the responsibility of good proof of delivery out of the hands of the driver. The driver is not now making that decision. The AI is making the decision of if you've got a good image and if you've got the information that you need. So this has been a really, really important part of our strategy. So when you combine the rest of Zebra, this is how we start to address a more intelligent operation. If you start up here in sense, we have a number of products that sense all of the signal, all of the digital voices of products, and we can convert those digital voices into our decision matrix. So all of the insight from that we feed into the orchestration layer. The orchestration layer then makes decisions about what are the next best actions for the enterprise. We convert those next best actions into intelligent tasks, and we send them down to the augmented worker so that they can then act on it and execute. In the process of executing that, they collect more ambient data, more ambient intelligence about what's going on in the physical world, and that physical world data feeds the digital twins that continue to learn and continue to improve the operations. So this is kind of a flywheel, a flywheel that creates a lot of value for our customers. We're really super excited about it. And I was asked, you know, kind of to round things out, you know, what does the partnership with Qualcomm look like going forward? And here's what I'd say. I'd say that we need to continue to drive more intelligence at the edge. We need to continue to instrument and augment our workforce. And so to do that, we're going to rely and we're going to need even more compute at the edge. More TOPS per watt efficiency. We're going to need more power efficiency because, of course, we're at the edge. We're running on batteries, as we discussed. And lastly, we need smaller form factors, smaller footprints because we see building even more and more instrumentation on the bodies of our workers. So that's like a little insight as to kind of where we're going. But the wearable category for us is kind of that next bastion. It's the next frontier. So with that, I would say we really look forward to this journey with Qualcomm. We're excited to have been here. We thank you for your time. And I'll hand it back to Nicole.
Thank you, James, and thank you, Zebra. I think, you know, prime example of somebody that has been a fantastic partner for over a decade. And James ends his comment with wearables is where the world is going in their world. Makes my point about what we said as far as the first part of the journey.
Look, we're a chip company. A lot of this work that we're talking about ultimately has to be deployed on chips. And chips are complicated because they're all designed for many different things. And the complexity of IoT is really being able to ultimately find a very good product-market fit for the problem statement that you're trying to solve. Right? Today we talk in the context of TOPS, right? Nobody talked in the context of TOPS two years ago, three years ago, but TOPS equal to power, equal to size, equal to cost. It's one more dimension across many dimensions. People want TOPS and connectivity together. These have nothing to do with each other, but they're sitting on the same chip. So what we have done is to spend a lot of time trying to figure out how to organize customers' needs, the ecosystem's needs, in terms of the language that we speak internally, because one of the complicated things when you're a big company is how do you organize your teams and your processes and your investments such that they make sense to the ecosystem. So we have a connectivity tier, and James mentioned all of the various wireless technologies that they use. Fantastic, and we support those. We have a commercial and consumer business. Again, Zebra is a great example of that. We will talk about camera later in the day. We picked camera as a dedicated category, and I will have Matt from Cognix come and talk about this in a little bit. We've taken industrial and edge AI as a dedicated category because we believe that there are going to be a lot of, for lack of a better word, headless devices that are just running analytics, all types, camera analytics, time-series analytics at the edge, and then robotics and drones. We have streamlined the development tools and software. So there are four or five major operating systems that are needed, and we've streamlined that. There are a bunch of different chips, and then there is underlying technology. So for us, what is very powerful is the ability to be able to think about a use case, pick a specific chip, and then trace all the way down to what technology roadmap applies to that specific conversation. It's not an easy job because these things are always changing, and there is no pattern to them. But once you start to understand the pattern in the context of what people are trying to do, you start to look for what will make the most sense. And that to me is one of the most complicated things about IoT.
One thing that we take pride in is that the chips that we build are world-class. And over the last decade or so, we have gone from building chips for consumer applications to chips for safety. Right? We put chips into cars that you buy that are responsible for the safety of the vehicle. We support temperature ranges from -40 degrees centigrade to plus 125 degrees centigrade. We support automotive safety and integrity standards. We support industrial safety standards. So what we are putting inside our chips, not just from a hardware perspective, but from a software, from an IP, from a compliance and auditability perspective, is a different level. And what this allows us to do is to be able to bring tremendous performance alongside all this capability, which basically means as the world starts to think about tech in the context of industrial transformation, digital transformation, you're essentially saying I'm going to need chips that I can rely on. And so we've been building this for a number of years.
We've spent a lot of time over the last 12 months on streamlining how do we deploy software. And, you know, when you think in the context of knowing your customer, you orient your teams towards what does the customer need, what is a similar customer need, what are their specific timeline requirements, what is the customization, what are the optimizations. We've actually done an about-face. We've said we actually can't do that. Everybody's a customer. So let's actually build on standard practices. Let's build to scale. Let's...
Align such that when we put something out there, we don't have to go communicate to customers. They already know because we are following a well-defined cadence. We are following well-defined norms. And so we have embraced Ubuntu. We've embraced upstream Linux. We've embraced Debian. All of these are things that we are now making standard in addition to what we do in Android and Windows. And this approach up the stack is what we take across the board. So things that we believe we must upstream for mass adoption, that is something that we have started to go to at scale.
Connectivity is an area that Qualcomm has always excelled at. And one thing that is very interesting about this space from a connectivity perspective is that there are just such broad set of requirements around connectivity. James talked about connectivity in the context of sensing and how do you use connectivity as an input into a multimodal model, but there are just fundamental workflow requirements: putting RFID into a cell phone because the handheld requires to also be a barcode scanner, we did that; building Bluetooth Low Energy for shelf labels so that you could offer remote pricing management at the shelf, so we figured out how to go do that; how do you manage indoor location inside a store where you want to actually provide the picker the easiest way to traverse through the store. We have capabilities around that. So this is a really broad space and we will get into details.
This is also a very interesting space in terms of the complexity involved because you have to deal with RF, you have to deal with fragmentation, you have to deal with how do you actually design your chips, what is the infrastructure dependency, and so this is where we would love to partner with many of you who are thinking about how does their environment actually evolve as you start to go deploy more wireless.
The other area that I'm very proud of, because this is change that we can all feel inside the company, is the ability to be able to ask you to click on a link, order a box, it shows up at your doorstep. We don't know that it did, and then you are up and running. You can do what you need to. This took some work. This isn't Qualcomm. This isn't how we have traditionally grown up. And so now the approach that we are taking is this is the bar. If we can't actually meet the bar where we can enable any of our customers to do what they wish to with our products without us having to pick up the phone and call them. The documentation is there, the forums are there. Your ability to be able to reach out to us if there is something that you haven't seen or connect into a community that we have created. This is something that we've enabled and we'll talk about this in the very next session in detail.
And then I think the other piece that has been a great learning for the company, certainly for my business teams, is what does it mean when you connect into an open ecosystem that is designed for anybody to be able to do whatever they wish to do. Right? We introduced Arduino Uno Q in October of 2025. We introduced it on the same day that we actually announced the acquisition, and then 10 months later we announced Ventuno Q with IQ8, and it was our second product with Arduino. And the feedback that we get now from customers is they essentially think of this as an Arduino product where they can do things that are much more than what an MCU could do. So everything that I talked about in the context of the use cases, etc., we actually have to make it real in terms of putting the hardware and the software and the ease of use and the ease of access in your hands, because if you don't actually have that, none of this is just a nice event and good networking, but we expect you to leave with you actually know what to go do with this. So this is a big shift that the company's gone through and I'm super happy that we've been able to go do this, and I'll have Manny and Gonit and Fabio give you a lot more detail on this in the next session.
Watch this with our joint product, the Arduino Uno Q.
