Keynote: Welcome + Opening Remarks - Jonathan Bryce & Chris Aniszczyk

This keynote session at KubeCon Amsterdam 2026 highlights the significant growth of the Cloud Native Computing Foundation (CNCF) community and its continued impact on modern infrastructure, especially with the integration of artificial intelligence (AI). The speaker notes the increase in cloud native developers now nearing 20 million and over 230 active CNCF projects. Key updates include the introduction of newly graduated projects such as Kyverno and Dragonfly, as well as advancements in AI infrastructure, particularly through collaborations with Nvidia and the launch of new tools for optimizing AI workloads. Moreover, the need for continuous innovation in Kubernetes capabilities to support diverse workloads, especially AI-based ones, is emphasized. The session concludes by encouraging attendees to engage with the community and explore upcoming KubeCon events worldwide.

Good morning and welcome to KubeCon Amsterdam 2026. We are >> [applause] >> We We are glad to have you here. Um the theme for this KubeCon is all about let's keep cloud native moving. And so to kind of kick things off, uh let's move a little bit. So, if this is your first KubeCon Europe, stand. Please. For So, it's That's a lot. That's about 40% if you

are or or so. Uh thank you for for kind of joining on this on this cloud native open source uh uh journey. So, today we have uh a little bit over 13,500 uh attendees making it the biggest KubeCon cloud native uh con event that we have done, which is awesome. Thank you so much. Uh represents a little bit of 10% growth over last year. We have over

100 countries represented here uh for you know for attendees, over 3,000 unique organizations coming to learn about cloud native. And we have about 900 sessions for all y'all to kind of learn and nerd nerd nerd out about uh this week. And we have a one glider. And we'll And we'll get to that a little bit later. >> [laughter] >> So, as you can tell, the community is

is still growing. Uh it's incredible. We have over 230 projects in CNCF, 300,000 uh contributors worldwide that are contributing to our projects. And you know, we continue to grow. You know, a lot of people may not realize, but our modern digital infrastructure, almost everything you use from taking a train, calling in a calling an Uber, um you know, going through airport security, a lot of these technologies

power are powered by uh CNCF projects. And we're currently in this uh you know, I think what what Jonathan and I refer to as like we're in the AI and agentic, you know, era. And with this, there's more development, more services, more infrastructure manage. And of course, our community is growing along with that. So, we have a report that we published at the last KubeCon, and we

did a refresh uh in you know, last 6 months. And now we If you look at the total amount of cloud native developers out there, we're almost at 20 million. So, it's amazing kind of see what our community is coming together to grow and help uh truly not only build the modern digital internet, but also power the uh AI infrastructure revolution that we're kind of going under.

So, if you're interested in this report and learning a little bit more about details of the cloud native development community, go check this out. There's a lovely QR code here for you uh to poke around and learn at. The other thing to mention is, you know, we are in Europe. Uh there's a lot of folks that truly care about digital sovereignty here. We had our first uh

open sovereign cloud day yesterday at KubeCon. And sometimes there could be misconceptions that uh you know, CNCF uh you know, there's maybe one country or one one company or one organization that does the majority work. But that's actually not true. We are truly a global and diverse organization. And we're putting together some data. Jonathan and I were poking around, and we're actually a little bit surprised. You

know, Europe makes up the largest, you know, dominant contributor across all of our CNCF projects. So, super impressive for all y'all. So, give yourself a a hands. >> And and of course, you have, you know, the US and India, China, Japan, and many other parts of the world that come together and do this. So, if you're interested in poking around at this data, we have an excellent

uh developer dashboard tool called LFX Insights. You can go scan that QR code or go to insights.lfx.dev to go play around. we continue to grow. Uh we've expanded in terms of uh community and cloud native developers. We've also continued to expand the cloud native project landscape. Um we've graduated some recent projects in our community. We have Kyverno, which graduated, which is a great project focused on security

and policy in the cloud native landscape. You have Dragonfly, which is really all about um distributing large uh binaries, containers, and even AI models uh at scale, which is awesome. We have Fluid and Tecton, which entered the incubation level, which are focused on continuous delivery and accelerating AI usage in a Kubernetes cloud native context. And we continue to add innovative sandbox projects that focus on making Kubernetes

an amazing place to run gaming servers. And you're going to hear about that from the keynotes later uh today uh from you know, making inference run amazingly in a Kubernetes. So, there's kind of continuous innovation that is happening in CNCF. And you could always go to the fun landscape that we have at landscape.cncf.io to kind of check out all the innovation that is happening in We also

have some new members that continue to join us, which is always amazing. So, I'm happy to announce that we have two new gold members that have joined us recently since the last KubeCon. We have F5 and uh Viettel. So, let's congratulate them for joining and supporting our you know, ecosystem. Uh great great organizations. Um uh and of course, we have a lot of other new members, uh

end users, nonprofits that kind of continue to join and support CNCF. One great thing about our community is we have a lot of end user companies that are involved from, you know, uh telecommunications companies to uh automotive to train infrastructure. And one thing that we love to do is to We love to work with our end user community and develop essentially what we call end user reference

architectures. So, how do you actually deploy all these projects that do something cool, right? And so, we have some new ones that we are announcing today where we have a reference architecture from Swisscom of how to deploy uh a sovereign cloud that they're using. We have Zeiss, which kind of built a really cool order fulfillment system. And then CERN, of course, who has done a really cool

uh AI uh you know, scientific focus uh cloud. So, you can go check these out on architecture.cncf.io. I encourage everyone out there, if you work at a cool company and you're doing some cool things with CNCF projects, go submit these end user reference architectures. KubeCon and the CNCF community is all about learning from each other and sharing lessons. So, check them out. We'd love to see more

more of these. you know, as as I talked a little about, it's it's truly hard if you work in software engineering and and kind of work in technologies days, it's almost impossible to escape uh AI, right? It's it's it's all around us. Um you know, I use both, you know, AI tools, Claude, like to help me with coding assistant and also as a therapist. It's great. Great

great great tools. Um but you know, this is what the new world is going to be about. We're going to integrate these things everywhere. And you know, I'm really excited to in CNCF that one of the companies that kind of has been at the forefront uh of of this AI revolution, and one of the largest uh and most valuable companies in the world uh has decided to

join and support CNCF uh at the platinum level. And I'm very excited to welcome uh Nvidia as our latest and newest platinum uh member. So, let's give them a round of hands. >> personally uh really excited to go uh welcome an old colleague uh of our of ours on stage to talk a little bit about why Nvidia is joining and supporting CNCF and open source. So, let's

go uh hand it off to Aaron Boyd to come on stage and talk a little bit about Nvidia and what they're doing. >> [music] >> Good morning. How's everyone doing? Get some coffee? Awesome. Yeah, so the future of AI is community-driven and open. Now, let's look um a little bit in the past to think about where we're going. And think about the technologies that have reshaped computing.

The operating system, the internet, virtualization, the cloud. But Kubernetes started smaller. It was a scheduler. It was a way to run containers across environments reliably. A tool for teams dealing with scale. But then something unexpected happened. Developers didn't just adopt Kubernetes, they standardized on it. Operators and service providers didn't just run it, they built platforms on top of it. And organi- organizations didn't experiment with it. They

bet their business on it. And somewhere along the way Kubernetes stopped being just infrastructure and became the de facto programmable control plane for modern distributed infrastructure. And here we are today, over 13,000 individuals who are part of an ever-evolving ecosystem of open source engineers who are running mission-critical systems, globally scaled services, and increasingly, as Chris mentioned, AI workloads. And that's why it keeps expanding, from containers to

databases, from stateless to stateful, from apps to platforms. These AI workloads are driving innovations in infrastructure that Kubernetes wants to standardize and adopt and simplify operations, but also enable the developer like never before. Nvidia has been part of open source at the lowest levels for many years. But in the past 4 years, we started to accelerate our development to open And more recently, contributing directly to the

cloud native ecosystem. Schedulers like Kai is officially now in the CNCF sandbox. Woo! Yeah. >> Last week, we open sourced AI cluster runtime, or we affectionately call it ACRE, because as engineers, we simplify everything, which delivers runtimes tuned for AI. We are able to verify the configurations are conformant by running it against the Kubernetes AI conformance test suite for inference using a distributed infrastructure workloads like Dynamo,

which just was released to 1.0. Anyone who is using an Nvidia GPU today is definitely using one or more of our tools within their stack. But I am most excited to announce today that we are donating the Nvidia GPU driver directly to Kubernetes Sig Node. The driver provides a reference implementation for the vendor neutral Kubernetes DRA API, which helps standardize AI infrastructure. But you know what developers

need most? Access to compute. And as part of our commitment to developer enablement and the cloud native ecosystem, we're pledging $4 million over the next 3 years to ensure that all projects in the CNCF that need Nvidia GPUs will get access to them. >> The future of AI will be built in the Just like Kubernetes is. Why? Because the hardest problems ahead are not just model problems.

They're infrastructure problems, scaling problems, interoperability problems, trust and transparency problems. And no single company can solve those alone. Open source is how we share innovation, standardize platforms, build ecosystems instead of silos, and it's what made Kubernetes the foundation of modern infrastructure, and it what will be makes AI the foundation of the next generation of compute. Thank you and have a great KubeCon. >> Great job. >> Thank

you, Erin. And I just, you know, I think it's so awesome and exciting to have Nvidia joining as a platinum member and then also supporting the community with that $4 million grant to help us test on on the latest hardware that's going to be put to good use, I'm sure. Um I want to talk a little more about AI 4 months or so ago we had KubeCon

North America in Atlanta. Um and there I talked about uh where I thought we could really have an impact in the world of AI with our cloud native community and our projects and technologies. I talked about three pillars, training, where we take data, we encapsulate it, and uh kind of put this intelligence into a model, inference, where we take that model, we serve it so we can

make predictions and answer questions, and agents, which are really where we see that intelligence become accessible uh to humans, to other agents, to software, to systems. And this is something that's um you know, I think is is amazing to see how much progress there's been in just those 4 months. At that event, uh I I talked about inference all week. Uh I you know, had so many

great conversations. A lot of those conversations were talking about "Well, I've been trying to put together an inference system." Some of those conversations were "What is inference? How does it work? Why is it important?" The conversations I'm having now, though, are really changing in a significant way, and a lot of times the conversations now are "How do we scale inference?" And this is not just something within

our community. If we look at the global macro environment around IT and AI, we're seeing this in all of the areas that we can measure. Um if you look at how AI compute was distributed in 2023, 2/3 of it went to training and 1/3 of the compute went to inference. And by the end of this year, that's actually going to flip, where 2/3 of AI compute is

going to be dedicated to inference workloads. And by the end of this decade, which is just a few years from now, uh the amount of of compute capacity that's going to be dedicated to inference is over 90 gigawatts. 93.3 gigawatts. And this projection is actually that that will be greater than all of the other compute loads combined. So, when I say, you know, inference is going to

be the biggest workload in human history, it's not an exaggeration, and it's not something that's 10 or 20 years away. This is happening right now. And we also see that this is driving new markets and new opportunities, and I think that's what's awesome for our community is we have a huge growth potential ahead of us for our projects, for our companies, for our developers. Uh and this

is something that we can act now to help take advantage of this. And so, what is driving this? What's driving this insane demand for Chris mentioned Cloud Code earlier. How many of you have used Cloud Code or Code X or Open Code or Goose? You know, one of those coding agents. Exactly. Uh what what about Open Claw? >> Is anyone willing to admit they've got their claw

running? Yeah. >> Yeah. Yeah. Okay, we've got a few brave souls out there. Well, you know, when you look at agents, these are super users of inference A lot of times people think about AI as kind of the the chatbot experience of chatting with uh with ChatGPT or something like that. But when we move to the agentic world, the usage skyrockets many, many multiples. The good news

for us is these are the expertise that we have in this community already. How do we take distributed systems, deploy them, observe, scale, secure? These are the right skills that we have, and this is exactly what the AI world needs right now. And I think that the other good news that we can look at, we have a great start in this. Uh we have a huge footprint

of Kubernetes in most of the organizations in the world, and many of those organizations are already running inference workloads on top of And as Erin alluded to, you know, this isn't because Kubernetes was built for AI and inference. It was built for distributed systems, and this is becoming the most widely used distributed system in history. I [snorts] think as we look forward, what we need to be

thinking about is how does cloud native support the new and right here AI native era? How do we take the primitives that we've already created and built and expand on them to support AI workloads? And to do that, we're going to need updates to existing technologies. Um we've heard about DRA today. Uh the the inference gateway that's in Kubernetes, I think, are great examples of where our

projects are progressing. And we'll also need new projects and new technologies. When I was in Atlanta, I mentioned an example of of a project like that called LLMD, which is a distributed inference uh system built around Kubernetes. And what I'm really excited to talk about and announce today is that LLMD has just entered the CNCF sandbox. So, please help me welcome uh the team who's working on

LLMD to tell us a little more about this. >> Check. Hey, Rob. So, we've heard Chris talk about inference. Or sorry, we've heard [laughter] uh Chris, we heard Jonathan, we heard Erin. Um and we're just getting started, right? So, can you help us understand more about why inference is so important right now? Thanks, Corina. Let me start from the beginning. When ChatGPT launched in late 2022, it

was honestly a bit of a scary moment for the open model ecosystem. Proprietary API-based models were simply an order of magnitude better. However, since that day, we have seen an absolute explosion of capability in the open ecosystem. Starting in 2023 with the first models like Bloom to 2024's Llama 3 moment, the first usable open source model, to 2025 with the introduction of large reasoning MOE models like

DeepSeek and Nvidia's NeMo tron, the open model ecosystem [music] is simply on fire. However, all of this progress creates a huge challenge. How do we scale inference against simultaneous 100x increases in model size, context length, and request level token intensity? As the lead developer of vLLM, we knew that we needed to invest in the next frontier of optimization to deal with these challenging agentic workloads, distributed inference.

And so, while vLLM optimizes a single node, squeezing as many tokens as possible out of every host, LLMD optimizes a whole cluster of vLLMs, implementing distributed performance optimizations like LLM-aware load balancing, KV cache management across the storage and memory hierarchy, prefill and decode disaggregation, and multi-node expert parallel deployments. Thanks, Rob. Abdullah, now, okay, load balancing, routing, how do they need to evolve to really support all of

So, traditional load balancers were built for the stateless web, routing traffic based on simple metrics such as number of open connections or round robin. But LLM serving breaks this model because it's inherently stateful. If you route a prompt to a random GPU, you significantly reduce your chances of KV cache reuse. So, combined with the wildly unpredictable compute costs of autoregressive decode and variable sequence length, traditional routing

practically significantly guarantees cache thrashing and stranded GPU capacity, leading to higher request serving latencies. To fix these inefficiencies, inference gateway, which is an LLMD component, it uses LLM load and prefix-aware load balancing. Inference Gateway inspects incoming prompts and sends them to the specific GPU that already holds a significant part of the context in its KV cache. So, to achieve this allows us to achieve much better load

distribution, much more uniform KV um utilization, but it also significantly reduces prompt compute and and and slashes time to first token. But, not only that, it also frees up critical high bandwidth memory on the accelerator, which allows us to have much bigger batches during decode, resulting in much higher throughput. Thank you. This really sounds challenging. And uh thank you, Matthieu, for being here with us. Uh can

you give us a concrete real-world example from Mistral AI on how challenging this is? Yeah, so one concrete example we wanted to share is disaggregated serving. Um the core idea is simple. You split the prompt processing phase, also called prefill, which is compute bound, from from the token generation phase, also called decode, which is memory bound. Each phase, one on its own little worker set. This approach

is becoming essential, uh especially to serve large mixture of expert model like Mistral Large 3 or Mistral Small 4 that actually got released last week. Um so, by adapting parallelism strategies, you directly improve your MOE throughput. And on large deployments, even even on four dense models, it stabilize the decoding speed and reduce your tail until the token latency. So, your quality of service improves using fewer GPUs.

Yes, the deployment is more complex, but the improvements in performance are hard to ignore. At Mistral AI, we think that open collaboration on these issues is essential to building future-proof infrastructure and setting new standards. For example, we identified the need for disaggregated site operator that will be released within the little worker set project. Essentially, it synchronize the prefill and decode phase by creating a tightly coordinating rolling

update. With this, frameworks like LLMD can ensure only compatible versions communicate with each other, making such deployments safer to deploy. Amazing. Thank you, Matthieu. Now, Carlos, yeah, you can clap. You can clap. >> Right? >> And Carlos, let's let's bring it home. Can you help us understand how this is all coming together and helping solve these challenges in a production environment? Absolutely, Karina. Thank you. So, these

are really cross-layer challenges. They cannot be solved independently. That was the motivation for the creation of LLMD. So, last year, we brought together a coalition of industry leaders with one common goal, to make inference a first-class citizen in Kubernetes. The design principles were simple. Make it composable by building open standards like Gateway API inference extension. Deliver proven paths from experimentation to production for state-of-the-art inference. And make

it flexible. Run any model on any hardware in any cloud. We've come a very long way. We've proven MOE serving disaggregated a production reality with significant performance gains. That's leading LLMD to be cited and adopted in leading industry forums. And that's why we're taking the very next big step. We're very pleased to announce that LLMD is joining the CNCF Foundation. We truly believe this will be a

catalyst to grow the ecosystem and with the help of you, the community, tackle the next big frontiers. Be it workload-aware KV cache, multi-tier caching, scaling reinforcement learning, and doing autonomous configuration. So, come build with us the future of Thanks so much. >> All right. Uh so, I I think that that's going to be a very, very important project for enabling all of the incredible workloads that uh

that we see out there. Um and I'm I'm so happy to uh to have it in in uh the CNCF sandbox and see where we can all take it together. There's another trend that I think is is starting to emerge that's also very interesting to me, and that's around specialized intelligence. Uh I think the last few years we've seen uh dominance from kind of the uh the

the foundation models, these frontier models. And a lot of times, you know, that's still what people think of as AI is that kind of chatbot experience. But, this next phase of AI, which has already started, is going to be a little bit different. I think we're going to tens of thousands, hundreds of thousands, even millions of models which get embedded into all kinds of environments. And rather

than just the pretraining on a set of public data, like a lot of the this is where we're really going to see the value of private data unlocked by taking those foundation models and post-training them, fine-tuning them, by doing reinforcement learning, and by doing continuous interact with a product, there are many, many ML models running in the background. So, that obviously powers things like our marketplace, so

dispatch, pricing, uh matching, so on. And then personalization. So, if you open your Uber Eats feed, you might get a different view than someone else. Then also, kind of very fundamentally, other parts of the platform you might not be aware of, like our risk and safety. For example, we want to make sure that the person picking you up is indeed the person who signed up on our

platform. So, AI/ML kind of powers a lot of that. And then with GenAI recently, we've now integrated quite a lot into our platform to just take things to the next level. So, what's what's the challenge? What make Why why are we here today? So, fundamentally, the big challenge at Uber for ML in one word is scale. So, we are live in over 70 countries, over 10,000 cities,

and that translates to actually well over 33 million trips per day. Then if we take that down to the ML world, that means we as a platform have to support well over 30 million peak predictions per second across a thousand serving nodes, including CPUs and GPUs. So, how do we do that? Michelangelo is the Uber ML Ops platform. We've been building it since 2016, so as of

now, it's about 10 years. I'd say it's our 10-year anniversary. In the early days, it was all about linear tree-based machine learning models. We had a simple UI that kind of abstracted things away from ML developers. We focused on our feature store, workflow orchestration, and quality. From 2020 onwards, deep learning became, you know, the the new technology. And then we had to both support machine learning, support

the more complex use cases, so making sure our developers could use a code-first way of model development, but also just maturing our platform. So, that's where we introduce model performance, feature monitoring to make sure that we can manage this at scale for Uber, and also tearing and just other critical parts of this of this platform. And then from 2023, GenAI and obviously more recently [clears throat] agentic

AI became a thing. This is a new flavor of AI for sure, but fundamentally the challenges are kind of the same. Building a proof of concept is relatively easy compared to taking a GenAI application or agentic AI application and doing it at scale. So, the same team has been kind of working on how do we do that? Different things like model gateway, LLM serving, uh and then

now we've done a lot on the agentic side, which I'd be happy to talk about uh separately. So, what this means is Michelangelo has been able to support 100% of our mission-critical ML at Uber. Translates to 20,000 models trained per month, 5.3k of those are in production, so live heading the different trips every day. The 30 million peak predictions that I mentioned, and we operate with serving

reliability of four nines. So, how do we do that? This is a very, very short and simple representation of Michelangelo. So, at the fundamental we have the data plane, and we also leverage a lot of open source technologies there. So, from PyTorch, Spark, Ray, TensorFlow, so on. And this is across both CPUs and GPUs. And then fundamentally our control plane is where Kubernetes is the is a

critical piece. So, we have a Kubernetes-based API, and that just manages everything, make sure that we're doing it we're achieving our reliability goals. It just manages the workload across different compute clusters. Be happy to speak about that further, so please come to our booth and we we can share our reference architecture of Kubernetes. And then most importantly what this means is that all that infrastructure is just

abstracted away from the developer. So, as an MLE or a data scientist using Michelangelo, all you have to focus on is the machine learning. So, with that I'm going to wrap up. Thank you for having me. We'd be more than happy to chat more about this at our booth. And yeah, thank you to the team. >> Thank you, Melda. Uh just to reiterate a couple of things

that I love about that story. 30 million predictions per second, 20,000 training runs, 5,000 models. Uh I think that that is one of the best examples that I've seen of truly an AI-native company. And I love how how much is built around open source, not just CNCF projects, but PyTorch and vLLM, which are in the the PyTorch Foundation, and all kinds of other tools that they bring

together to enable that. So, I mean one of the things that CNCF is is famous for and and some people may even take for granted is, you know, Kubernetes has evolved over the last decade and truly is supported on every major public, private cloud out there, you know, in every almost geography. What we've done essentially for Kubernetes for traditional cloud-native workloads, we are doing the same thing

for AI workloads. And last KubeCon we announced the launch of our certified Kubernetes AI conformance program, which is going to go replicate some of those features around ensuring Kubernetes is consistent across different platforms, clouds, and so on, but for AI workloads. And so, we had an initial batch of companies representing some of the largest clouds and even us, you know, smaller neo clouds out there that were

part of this program, and we continue to make progress, and it would couldn't be a a KubeCon without a a fun live demo. So, I'm excited to bring on Janet uh Cow from Google to do a little demo of kind of what's next for AI conformance. >> So, I'm going to show you what's new in With the importance of inference and how to scale inference, the community

decided to add a few new requirements in AI conformance around inference. And let's see how it looks. Let's Let's bring up the demo. So, let me warn you, this will be a live >> Thank you. Yay. So, I'm going to use a script to help me do all the typing, cuz I'm not very good at speak and typing, but let's see. So, this demo is running on

a AI conformant cluster. The first requirement I want to show you is that it must support gateway API for advanced traffic management. That's the low balancing bit you just saw. So, first I want to verify there are gateway classes in the cluster. So, gateway classes show me all the um available gateway classes in the provided by the infrastructure, and the controller implements the gateway. And then I

want to look at the namespace to see if there's any gateway available So, there's one inference gateway there using the L7 external um gateway class. Moving on to the next requirement. AI conformant platform should support gateway API inference extension for advanced inference routing. This is for um inference-aware low balancing that you also just heard. We first verify the same gateway that I just showed you. We want

to see there are routes attached to it. So, I can see that there are two routes. And those are actually HTTP routes. Because we're using inference extension, it's um routing traffic to inference pool instead of a service. That's how it's uh inference-aware. So, as you can see I have those both routes using the same inference pool. So, what is an inference pool? An inference pool defines the

set of model serving pods behind the gateway. It also has the endpoint picker that will route the inference request to the optimal um model serving pods. So, something like um KV cache-aware routing, that's handled by it. And the last requirement I want to show I want to show you is that the AI disaggregated inference. This is about scaling inference. It breaks the prefill and decode um phase

into separate scalable components, and then you can self-scale them differently and scale them on different a dedicated hardware pool. So, let's look at the model serving pods in the First, we have the prefill pods. They are there for handle handling the prompt processing, which is very compute-heavy. And we have one prefill pod. Next, we have the decode pods for handling the token generation, which is very uh

memory-heavy. So, we can scale them differently depends on our need and put them on different nodes. So, we I have two uh decode pods. And this example uses um LMD, but the platform is free to choose any other disaggregated inference solution they want to support. And the community is also exploring the idea of having a common API for inference. So, now we have all the layers in

place. Let's see it end to end. So, I'm going to send a live inference request. It's going to be sent to the gateway, to the inference extension for the inference routing, and it will be routed to the prefill pod, and then be routed to one of the decode pods, and finally will return me a response. So, I'm going to ask a pretty big model a question. Tell

me how Kubernetes help run AI workloads at scale. Okay, it tells me why and how. And that gives me confidence that Kubernetes is the best platform for running AI workloads. >> Let's go back to the slides. So, um I'm very happy to be here to share with you the new advancement in AI conformance. If you're interested, please scan the QR code or take a picture to get

certified and design and contribute to the Kubernetes AI conformance with us. And thank you so much and enjoy KubeCon. >> [snorts] >> So, just as we're kind of wrapping up this this first segment here, I want to talk a little bit about how important it is that open source really does win for the AI era. If you go back just a couple of years ago, most of

the AI work that was being done was being done not in the open. There were open tools underneath like PyTorch and other things. Um but what we've seen in just the last couple of years is a true acceleration of open source at all layers of the AI stack and I think that is so critical and this is why it's great that you're all here this week working

on this. I truly believe that AI infrastructure has to stay open because this is going to be the intelligence layer for all of us and for the world. And if you go through this week, you'll see that we have a lot of experts here already that we can all learn from and that's how community works. None of this is going to happen without a great community. I

also encourage you to look at other open source communities in the AI space because AI is huge and so within the Linux Foundation, we have the PyTorch Foundation doing great work at the low level layers of AI. We have the Agnostic AI Foundation building MCP protocol and other other kind of Agnostic type technologies and so join all of these communities so we can make sure that open

source wins. We got a lot of content this week. You know, look at your schedule and see. These are just a few highlights where we've got GPU ops and Agnostic ops and more more and more about Kubernetes as well. But I think that this is so important. I'm so happy that we have our biggest KubeCon ever here while we are poised to go take advantage of this

opportunity and make sure that that AI does stay open. Thank you, Jonathan. I definitely think that's super important. You know, our community has grown and there are so many different ways to contribute both from a code level to even a non-code level. We have a great amount set of projects you could contribute upstream. We have a technical technical advisory groups. The TOC is responsible for a lot

of technical decisions. All these meetings are open and available for you to kind of learn and join from. If you're not a developer, we have a lot of non-code contribution options, too. You could host a meetup. You could contribute to a variety different documentation. We have a glossary. There are many many ways to contribute within our ecosystem. We have a great ambassador program. Many ambassadors are here

on on stage. I've seen uh there yeah, there they are. Find them. They're very nice. They'll help you out. We have our kind of educational ambassador program, KubeSernauts. We have about 500 or so in Europe. These are folks that are domain experts in a variety different aspects of Kubernetes. So, go find them. They have these cute little blue jackets that you'll see. We have a variety of

mentorship programs available that we do a few times a year that we actually pay you stipend to contribute. And of course, we have meetups all over the world on community.cncf.io. So, there are many many ways to contribute. A lot of new folks here. Make a friend. Learn something new. That's what we're all about here. we do these events. KubeCons are fun. Very little stressful sometimes planning these,

but we're excited that we have four other KubeCons coming up this year. We have KubeCon India, you know, for the second time up in Mumbai, June 18th 19th. Oh, I got some fans. >> We're back in Japan in Yokohama, July 29th 30th. Yeah. >> China with our friends from Open Infra and PyTorch doing a little combined uh swaray with them in Shanghai, China, September 8th and then

back to Salt Lake City for KubeCon North America in November 9th through 12th. We plan these things in advance. So, we want you to save the date cuz I know all y'all love KubeCons. So, we're going to be in Barcelona next year. Back there. It's going to be great. New Orleans, Louisiana for North America 2027. A little closer to home for me, which is going to be

great. And then we're coming back to Berlin in 2028. So, we'll hope to see you there. >> Save the date and you know, Jonathan and I both started, you know, this this event with the whole theme is let's keep Cloud Native moving. We're in a whole AI Agnostic revolution. We're going to evolve and kind of continue and build infrastructure that everyone depends on in the world. So,

thank you for being here and have some fun. Learn something new. We'll get on with the program. Yeah, we're going to [applause] So, thank you. And to to keep the program moving, please help us welcome one of your co-chairs for the event, Lin Sun. Let's do it. Come on, Lin. [applause] >> Good morning, Amsterdam. Welcome to KubeCon Cloud Native Con. I'm one of the newest co-chairs of

My name is Lin Sun. I'm head of the open source at Solo.io. I'm also a CNCF TOC member and ambassador like many of you. I'm also a maintainer for many of the CNCF project. I should say a few, not many. Istio, Gateway and K-Agent. I'm so impressed with our opening keynote today. So many new announcement already happening.