Nvidia Just Dropped NemoClaw And Other Huge AI Updates

So, Jensen Huang just introduced Nvidia's Nemo Claw and we have to talk about it. Now, I'm pretty sure you probably have already heard of Open Claw. I will play the full talk in a moment, but if you haven't heard of Open Claw, it was basically that open source AI tool that basically lets an AI run on a user's machine and basically do pretty much anything. Now, of course, this was acquired by OpenAI, but people were wondering what would happen to enterprise AI. So the companies that had built on openclaw were concerned and so Nvidia actually saw the opening and moved fast. And of course today Jensen Huang introduced Nemoclaw and this will let enterprise companies send out AI agents to carry tasks on behalf of their employees. And this platform comes with built-in security and privacy tools. And it's also hardware agnostic, meaning that companies can run it regardless of whether their products are built on Nvidia chips. And I think this entire thing just goes to show how much activity there is going into this open claw phenomenon. So of course take a look because I think this is going to continue to evolve and computer agents are clearly the theme for 2026. — Open claw is the number one. It's the most popular opensource project in the history of humanity and it did so in just a few weeks. It exceeded what Linux did in 30 years and it's that important. It is that important. It will do well. Uh this is all you do. Okay. We're announcing our support of it. Uh let me just quickly go through this. I want to show you a couple things. You simply type this you type it this into a console and um it goes out. It finds open claw. It downloads it. It builds you an AI agent and then you could tell it whatever else you need to do. Okay, so let's take a look. — That open source project just dropped. — Andre Carpathy has just launched something called research is a huge deal. — You give an AI agent a test. Go to sleep. It runs 100 experiments overnight keeping what works and killing what doesn't. — I really love what my stuff enables that person to do. And we had like one guy, he told me like he installed it at his 60-year-old dad and like they made beer, connected the machine via Bluetooth to Open Claw and then we automated everything including the whole website for people to order — the lobster. — Hundreds of people are queuing up for lobsters in Sanen. — Open. — We want to build openclaw with open claw. — Everyone is talking about openclaw, but what is openclaw? Believe it or not, there's already a claw con. — Incredible. Now, um I illustrated effectively what open claw is in this way and so all of you can understand it. But let's just think what happened. What is open claw? It connects. It's an a it's a system. It calls and connects to large language models. So the first thing it has resources that it manages. It man it could access tools. It could access file systems. It could act as large language models. It It's able to do scheduling. cron jobs. It's able to um uh decompose a problem that a prompt that you gave it into step by step. It could spawn off and call upon other sub aents. It has IO. You could talk to it in any modality you want. You could wave at it and it understands you. You could talk to any modality you want. It sends you messages, it texts you, sends you email. So, it's got IO. Um, what else does it have? Well, based on that, you could say in fact it's an operating system. I've just used the same syntax that I would describe an operating system. Art Open Claw has open sourced essentially the operating system of agent computers. It is no different than how Windows made it possible for us to create personal computers. Now Open Claw has agents. The implication is incredible. First of all, the adoption says something in all in itself. However, the most important thing is this. Every single company now realize every single company, every single software company, every single technology company for the CEOs, the question is what's your open claw strategy? Just as we need to all have a Linux strategy, we all needed to have a HTTP HTML strategy which started the internet. We all needed to have a Kubernetes strategy which made it possible for mobile cloud to happen. Every company in the world today needs to have an open claw strategy and a gentic system strategy. This is the new computer. Now this is just the exciting

part. This is enterprise IT before openclaw you know and I mentioned earlier the way enterprise IT works and the reason these reason why it's called data centers is because these large rooms these large buildings held data held the files of people the structured data of business. It would pass through software that has tools and you know systems of records and all kinds of workflow that's codified into it and that turns into tools that humans would use digital workers would use. That is the old IT industry software companies creating tools saving files and of course gsis consultants that help companies figure out how to use these tools and integrate these tools. These in these tools are incredibly valuable for governance and security and privacy and comp this openi toolkits and the first part of it is techn experts and we worked with Peter to make open claw enterprise secure and enterprise private capable and we call that this is our Nvidia open cloud reference for Open Nemo Claw which is a reference for OpenClaw and it has all these agentic AI toolkits and the first part of it is technology we call open shell that has now been integrated into open claw now it's enterprise ready this stack with a reference design we call Nemo cloud neoclaw okay with a reference stack we call Nemo claw you could download it play with it and you could connect to it the policy engine of all of the SAS companies in the world and your policy engines are super important, super valuable. So the policy engines could be connected Nemo claw or open claw with open shell would be able to execute that policy engine. It has a polic guard rail. It has a privacy router and as a result we could protect and keep the clause from executing inside our company and do it safely. We also added several things to the agentic system and one of the most important things you want to do with your own claw custom claws is so that you can have your custom models and this is Nvidia's open model initiative. We are now at the frontier of every single domain of a our model is valuable to all of you because number one it's on the top of the leaderboard. It's world class. But most importantly it's because we are not going to give up working on it. We're going to keep on working on it every single day. Neotron 3 is going to be followed by Neotron 4. Cosmos one was followed by Cosmos 2. Groot at generation 2. Each and one of these we're going to continue to advance these models vertical integration horizontal openness so that we can enable everybody to join the AI revolution number one on leaderboard across research and voice and world models and artificial general robotics and self-driving cars and reasoning and of course one of the most important one this is Neotron 3 in Open Claw. This is Neimotron 3 and Open Claw. And look at the top three. There are the three best models in the world. Okay. So, we are at the frontier. It is also true. It is also true that we want to create the foundation model so that all of you could fine-tune it and post-train it into exactly the intelligence you need. This is Neotron 3 Ultra. It is going to be the best base model the world's ever created. This allows us to help every country build their sovereign AI. And we're working with so many different companies out there. And one of the most exciting things that we're doing today, I'm announcing today is a Neotron coalition. We are so dedicated to this. We have invested billions of dollars of AI infrastructure so that we could develop the core engines for AI that's necessary for all the libraries of inference and so on. But also to create the AI models to activate every single industry in the world. Large language models is really important. Of course, it's important. How could human intelligence not be? However, in different industries around the world, in different countries around the world, you need to have the ability to customize your own models and the domains of the domain of the models is radically

different from biology to physics to self-driving cars to general robotics to of course human language and we have the ability to work with every single region to create their domain specific their sovereign AI. Today, we're announcing a coalition to partner with us to make Neotron 4 even more amazing. Now, the next thing that Nvidia introduced here was, of course, the DLSS 5. Now, this was one of the most controversial releases because some would argue that this is just an AI slop filter. I've seen a ton of different videos saying that this is just AI slop, but I actually think this is one of the most interesting innovations because what it essentially does is applies a generative AI filter to specific elements of games to provide you with, I guess you could say, upgraded graphics that would otherwise be, you know, unrenderable. Like, it's pretty insane when you actually think about how much the details to increase. Now, of course, some gamers are arguing about the original artistic style of the game and of course the ability for it to fail sometimes, which is of course true, but think about the precedent for the future. I mean, you're going to be able to upscale many, many old games into modern looking games simply by running them with DLS 5 on. So, it's super interesting. I think this will be probably the future of certain games where you can literally just upgrade it, of course, by using the actual ground source. But of course, I will leave Jen Sang Huang to talk about this. And I would love to know your opinion on what you think about this controversial feature. — Is that incredible? Computer graphics comes to life. Now, what did we do? We fused controllable 3D graphics, the ground truth of virtual worlds, the structured data, remember this word, the structured data of virtual worlds, of gener generated worlds. We combine 3D graphics, structured data with generative AI, probabilistic computing. One of them is completely predictive, the other one probabilistic yet highly realistic. We combine these two ideas. Combine these two ideas controlled through structured data controlled perfectly and yet generating at the same time. And as a result, the content is beautiful, amazing as well as controllable. This concept of fusing structured information and generative AI will repeat itself in one industry after another industry. Roll out of physical AI at scale is here. Autonomous vehicles. And with NVIDIA Alpamo, vehicles now have reasoning, helping them operate safely and intelligently across scenarios. We ask the car to narrate its actions. — I'm changing lanes to the right to follow my route. — Explain its thinking as it makes decisions. — There's a double parked vehicle in my lane. I'm going around it — and follow instructions. — Hey, Mercedes, can we speed up? — Sure, I'll speed up. — This is the age of physical AI and robotics. Around the world, developers are building robots of every kind. But the real world is massively diverse, unpredictable, full of edge cases. Real world data will never be enough to train for every scenario. We need data generated from AI and simulation. For robots, compute is data. Developers pre-trained World Foundation models on internet scale video and human demonstrations and evaluate the models performance to prepare them for post-training. Using classical and neural simulation, they generate massive amounts of synthetic data and train policies at scale. To accelerate developers, NVIDIA built open-source Isaac Lab for robot training and evaluation and simulation. Newton for extensible and GPU accelerated differentiable physics simulation. Cosmos world models for neural simulation and Groot open robotics foundation models for robot reasoning and action generation. With enough compute, developers everywhere are closing the physical AI data gap. Paratas AI trains their operating room assistant robot in NVIDIA Isaac Lab, multiplying their data with NVIDIA Cosmos world models. Skilled AI uses Isaac Lab and Cosmos to generate post-training data for their skilled AI brain. They use reinforcement learning to harden the model across thousands of variations.

variations. Humanoid uses Isaac Lab to train whole body control and manipulation policies. Hexagon Robotics uses Isaac Lab for training and data generation. Foxcon fine-tunes group models in Isaac Lab. as does Noble Machines. Disney research uses their chamino physics simulator in Newton and Isaac lab to train policies across their character robots in every universe. Oh, because I gave you your computer Jetson. — What is that? — Well, it's in your tummy. — That's going to be amazing. — And you learn how to walk inside Omniverse. — I love walking. This is so much better than riding on a reindeer, gazing up at a beautiful sky. And it was because of physics using this Newton solver that runs on top of Nvidia Warp that we jointly developed with Disney and with Deep Mind that made it possible for you to be able to adapt to the physical world. Check that out. — Not to say that. — That's how smart you are. — I'm a snowman, not a snow clip evil.