Jensen Huang: Why companies need open agent systems

I'm excited to be here with Jensen. There has been a ton of advancements in AI and agents over the past year, but a few months in particular, I feel. We've seen a lot of these advancements come in the form of better performance, but at the same time, we've also seen that openness and control and trust in a lot of these models and systems around them has become more and more important. And so the first thing I want to start with is how and why are you guys at NVIDIA investing in an open agent ecosystem and stack? Well, first, before I answer, I want to congratulate you for all the work that you do. In fact, if you look at the last six months, we could both agree that although we've been working in AI for 15 years, the last six months changed everything. And so all of the technology, all the large language model advances, all the scaling, all of the breakthroughs, all the omni models, multimodality stuff, all that stuff is fantastic. But in the end, it was the last six months that where everything kind of came together. And now finally, AI is useful. And when AI is useful, every company in the world, every enterprise in the world wants to get their hands on it. And now the question is how, and this is where LangChain comes in. And so, you know, you always had a vision that the large language model was the essential ingredient, the essential enabling technology. But in order to turn it into a useful product, you have to surround it with what is now known as a harness. There's so much more. So much more. And back in the old days, we use LangChain to help us turn a large language model into a promptable API. And we return, we use LangChain to build our rags and we use LangChain to, you know, step by step by step, which led to today's agents. And really what happened in the last six months, the big breakthrough are these agentic systems that are grounded on knowledge that can use tools to do search and has memory that it manages and it has safeguards and has the ability to iterate until it gets the job done. But it ultimately needed some models that have reached a level of capability where everything comes together into that flashpoint. And that's where Cloud Code really kind of brought the imagination of agentic systems. Open Claw, of course, was a big deal and all the work that you did, right, with Deep Agents and we use that ourselves. And all of that kind of came together and bam, here we are with agentic systems. The reason why we do it, we've dedicated ourselves for many, many years to build open systems. And the reason for that is because ultimately, AI is a fundamental technology. It can only be useful if applied in a whole bunch of different use cases. Now, of course, it's the first use case is just language and cognitive intelligence. And that's very important, of course. But, you know, we imagine a world where scientists and digital biologists and designers and, you know, roboticists and, you know, students and researchers, enterprise IT, all of us could use agentic systems, AIs, to solve domain specific problems. And many of the problems that we want to solve, either we have specialized domain knowledge that is just simply not available outside that we have to embed into, imbue into our AI, or it's because we believe that AI becomes ultimately great, become a super agent when we put it into a flywheel where we use it, it gets smarter, it becomes more useful, we use it even more, it gets even smarter, kind of like us, kind of like humans. Learns over time. And so we kind of imagine this future where AI has a foundation, and the work that Anthropic and OpenAI and Google is doing is all fantastic. But there's specialized AIs and domain specific AIs and proprietary AIs that people want to build, and we want to enable that world to happen. Maybe digging into that for a second on this topic of specialization. How exactly do you think it's best to specialize these systems? Is it going to be purely the model? Is it going to be the harness as well? The context outside? Like what goes into the specialization? Well, the specialization starts with you need to have intelligence that's good enough. And this is why we worked on Nemotron and really love the fact that you're part of the founding team of Nemotron Coalition. We made Nemotron Ultra pretty incredible. Now, Nemotron Ultra is a great model as a start, but it becomes an incredible model when you put the Lang chain framework around it, the Lang chain harness around it, so that you ground it on information that is domain specific. And a person is an intelligent person becomes super useful when we give them access to particularly important information. And so access to information is important, putting it into a flywheel where maybe you're even training the model, post-training the model inside the Lang chain harness against the harness so that the model becomes good at applying the harness around it. What you want it to do for that task. What you want it to do. Yeah. And so I think that this moment has now arrived, but we need a open harnessing system that we can build ourselves, that we can apply, and then of course, improve against over time. I love what you said about the model being good enough. I feel like that threshold was crossed, I don't know, maybe a year ago by some of the Frontier models, six months ago by some of the open weight models. You talked about Nemotron 3 Ultra. We've done a lot of work with that to make that really good in DeepAgents. Some of the things we did is tweak the harness to make it best for this model because we found that different models need different prompts, different tools. And with that tweaking, we managed to get Nemotron 3 Ultra in DeepAgents. We have an internal benchmark and it managed to get up to like 86% on that, Claude Opus for comparisons at 87. You've got DeepSeek and one of the Minimax models at like 82, 83 down there. So we're starting to see that some of the more recent open weight models are really getting to Frontier performance. I know. I am so proud. It is so incredible. Thank you. It is so incredible. One of the just as important thing is it's 10 times as cheap as Opus. And I think open weight models are starting to really strike a good balance between performance and cost. So I'd be curious how you see this cost part changing the equation for builders. The benefit of cost, it comes in a couple of different ways. I happen to think that when you have cost effective intelligence, people just use more of it. When you have a cost effective agent, then you can iterate across a larger search space. And as a result, the answer could actually be better. And in the case of Nemotron, it's cost effective because it's so fast. It's so computationally efficient. When it's computationally efficient, it could explore larger spaces. And it's no different than when somebody can think fast, you could explore more space. When you can try things more quickly, you can find a better answer. And so this is the incredible benefit of Nemotron 3 Ultra inside the Lanchain framework and the Lanchain harness inside DeepAgent. It could think so quickly, it could explore so quickly, it could iterate so quickly and efficiently that it's going to find better answers. And so I'm just really, really excited that we created a model that was near the frontier. But adapting the environment around Nemotron, you made it deliver frontier capabilities. Now insight for humans, it's kind of the same. Of course, we'd like to hire the smartest people in the world. But beyond that, we also give them access to tools, we give them access to information, and we also create the world around them so that we allow them, enable them to create the conditions for them to achieve their full potential. And so you adjust the environment, not just the model. And this is where Lanchain came in. What you said about using more of the intelligence as it's cheaper and faster. We see that to be so true. I like to think I'm AI forward. One of the things that I've underestimated is just the demand for intelligence and for tokens and how big and massive that market is. And I think that's become especially true recently. With these models getting good and being really fast and really cheap, how should we think about using frontier models? Should we just use these open source models all the time? Is there a time and place for both? Well, the frontier models are getting better all the time. And I fully expect the frontier models to be unbelievably good. And they still have a long, long runway of improving the models. Scaling laws, of course, are going to sustain. Their harnesses are improving all the time. Their technology for dealing with memory, whether it's working memory or long-term memory, is advancing incredibly quickly. The compassion technologies, all of the advancements in retrieval, augmented generation and knowledge graphs, and there's still a lot of incredible advances that are being implemented into these frontier model APIs. The way I think about it is, frankly, I always start all of my work starting with the frontier. And the reason for that is because it's useful. I know what's the potential. It costs a little bit more money, but it's incredibly—my time to getting the work done is fast. However, over time, I find that I want to add sub-agents to them. I want to connect sub-agents that are super-agents at certain skills. And so we have optimization problems inside our company that relate to supply chain. Maybe it's related to chip design optimization, floor planning optimization. And these problems, these optimization problems are insanely hard. And so you're not going to just have a general AI go off and crunch on it and think that you're going to find a great answer. So we create super sub-agents. And these super sub-agents we would create with deep agents, Lanchain deep agents, with Nemotron 3 inside. And we'll even connect them to specialized tools. And that thing is built for one job. That super agent is not trying to book me travel appointments. It's just trying to optimize our supply chain. And in that case, I really do need to have Lanchain. I really do need to have Nemotron 3 Ultra. And I connect it to a lot of proprietary knowledge and proprietary skills. I've got a whole team who's just dedicated to refining that. Now, I think that that defines a company. A company is really about a collection of a whole bunch of these super, super proprietary, super important workflows. And now we can have Lanchain with deep agent and Nemotron 3 inside. And it gives them all of the control they need, super efficient access to incredible tools. That's the future. Do you have any advice for enterprises? If they're following your practice of starting with the frontier and then starting to specialize, when should they think about specializing? What are some triggers that you look for? As soon as it gets good enough. I would start with Cloud Code and Codex and use it for as long as I can. And frankly, for a lot of things, you never have to replace because they're getting better all the time. And they're on a trajectory that's going to bring capabilities insanely fast. So I think that in the future, just like companies are today, we have employees that we hire because their domain specialization and the refinement of the work and the work process and all of their life learnings here in the company is just too valuable. But we also hire consultants and we license external tools and we outsource work to other people and so on and so forth. I think this is the future for AI. And so are we going to continue to use frontier models? Absolutely. And tons of it. But are we also going to create specialized super agents with Lanchain and Nemotron 3 Ultra that, in fact, arguably could be your crown jewels? And the answer is absolutely true. I think even for the consultants that you bring in, just like when you bring in a consultant, you need to get them up to speed in your organization and give them context on the organization, how things work, what tools do they need that have access to data that's only inside your organization. And so I think one of the things we've seen is as enterprises start to adopt AI, there's all of these systems that they have to build around them in order to make the agentic systems as a whole trustworthy and safe and proper kind of governance. I'm curious, how do you see- And just to add on that, today, most companies are built on business processes. In the future, most companies will be built on harnesses. And so the idea, Lanchain will just become the tool that creates the operating system for the company. And everybody will be using Lanchain to create their specialized harness, which represents a workflow of the past. And now this harness inside that workflow becomes autonomous, agentic, much more efficient. I think we see that there's the harness, there's the model, and then there's all the context around it. And all of these can be optimized at different points in time. That's right. And so the work that we did with Nemotron 3, I think was a great example of doing some pretty high ROI things around the harness, changing the prompt, changing the tools. One of the things we're looking forward to is experimenting with post-training Nemotron. It takes a little bit more time, but I think it really raises the ceiling of what this overall system can do. This is incredible. This is the big breakthrough. And so what you just described is a future where once you get the harness built, and it's doing the work, and it's now part of the business process, and it's very successful. Now the question is, how do we get it even better than that? Of course, you can keep improving the information that you provided. You can tune the harness, but you can now also improve the AI model, the large language model, Nemotron 3 Ultra, inside the harness. I think that's a complete breakthrough. That capability has never existed before, and I'm super excited about that. And it's really going to take all of these enterprise-specific business processes and really start to tune this flywheel. And I think one of the things that we've heard when talking enterprises is the demand or need for this to be built on an open ecosystem. This is all of this enterprise's knowledge and processes that they're putting in there, and having full control over that seems paramount to a lot of them. So I'm curious if you can touch on how you see open stacks really empowering enterprises going further with AI. Every company is built fundamentally on domain-specific or some specialized intellectual property. The reason why we call it intellectual property, you know, intellectual, it's intelligence. Every single company is built on intelligence, some foundation of intelligence that's specialized. Our company is specialized in something. We're not good at everything, but we're very, very good at one thing. And every company is built that way, you know? That specialization, your company's intelligence, is who you are. You can't possibly not continue to control it, improve it, make it better, right? And somehow outsourcing that intelligence, whether you're a person, company, country, makes no sense to me. And so, of course, there's general intelligence. And there are general things that we all do. And it's a lot of the economy, you know? And for example, software coding is actually a general thing. You know, we all program in Python. We all program in C++. We all program, right? And so you're applying it to different things, but the skill of coding is largely the same. And that's a general skill. Writing is a general skill. But those are foundational skills that we then apply for our specialized domain intelligence. And that's where Lanchain and Nemotron comes in. You know, I think the foundation of society is going to have these foundational models, and they're going to be general, and they're going to be, you know, available in the cloud, and it's going to be incredible. But on top of that platform, we're going to have to build our own specialized capabilities, and you need open tools for that. You know, you can't outsource it. You can't, you know, I can't imagine calling a third party when I need to enhance my intelligence. You know, I need to enhance it right here inside the company. And so I think that that future is not one or the other. It's a completely complementary vision. And really what we're doing is just making sure that automated intelligence is integrated into all aspects of everything that we do. And as a result, you know, we're all going to be better. Completely agree. And I think it's still hard to get that integration up and running. And so one of the things that we're announcing today is a blueprint with DeepAgents and OpenShell inside of the Nemo Claw blueprints out there. And so this will let enterprises run DeepAgents with Nemotron 3 Ultra inside of OpenShell, which is a secure and open runtime and take advantage of that. This is one of just... Such a huge deal. Hopefully it makes it way easier for enterprises to get up and running. All of the key ingredients necessary for you to build your personal, domain specific, proprietary, you know, your super agent, all of the technologies, all the components, all the tooling, all of the harnessing, everything. And the blueprint, a great example, all put together for you. How do you guys think about blueprints? You have many of them. This is obviously the best one. I won't make you say that, but I'll say that this is the best one out there. But you have a ton of blueprints. Why? Why invest so heavily in them? Well, because the tools are arcane still, you know, and there are a lot of pieces to it. You know, building an agentic system, building AI is not simple. And there's a lot of different pieces of technology. And we already talked about some of them. There's the large language model. There's the tools it uses and, you know, the knowledge graph that it has to deal with. It's memory system and it's guard railing system and it's fine tuning system. And now, you know, the technology you're going to create, the post-training against the harness. And then, of course, there's the harness itself. But what about the runtime? When you're done, you still have the runtime. You have to keep it in a sandbox. So it's secure. It's private. That is access control. It's something that IT organizations can control. Is that the hardest thing about the runtime? You think inside of enterprises, all the security things that go alongside it? Without solving the security, the access control, it's impossible to deploy. It's no different than it's impossible to hire a new employee into the company. If you don't onboard them, give them access control, we don't give every employee access to every file in every network, right? And so you have every single employee based on their job and their responsibility and what they need to have access to. We give them access to tools, the laptops and, you know, design tools and, you know, programming tools and whatnot. We give them access to certain parts of the network. We give them access to information. We connect them to other agents. We connect them to other colleagues that they work within, and we provide them a skills file. You know, we essentially, you know, give them a document about this is your mission. This is how it's previously been done. And now, you know, help do it even better than that. And so in a lot of ways, we are creating an HR system, if you will, you know, for AI that allows the IT organizations and all of the different business units inside the companies to be able to build, improve, and deploy these agents inside companies. This is more of a philosophical question, but you're talking, and I think a lot of people talk about these agents and anthropomorphize them a lot, bring them into human systems, but agents aren't human. And they have some things that are better than what humans are. And they have other places where they are, you know, very different and maybe not as good as what humans are good at. What is the right level to anthropomorphize these agents? Well, it's electrons, you know, it's electrons, not atoms. And it's not biological, has no consciousness. You know, it's not awake. And so it's not any of that. It's a tool that, it's kind of like my vacuum cleaner that's roaming around the house. And it's, of course, roaming around the house, cleaning up the house, doing something that I used to do. And you now have autonomous lawnmowers and you have, you know, and so I, you could just imagine a hundred years ago when the first dishwasher came along and now it's doing dishes by itself. It must've been magical to watch it. And we call it a dishwasher, which is a little bit like a human, you know? And so we have dishwashers. When my first job, I was a dishwasher. And so, you know, and so in a lot of ways, I mean, you know, we'll get used to it. I think it's right now, we tend to imbue too much human properties to it. It's nothing close to that. It's software, it's computers. We know exactly how it's working because obviously we created the harnesses around it. We obviously know how it works because it's getting better all the time. If we don't understand how something works, how do we make it better every time? And so if we don't understand how something works, how do we improve it? You know, how do we fix it? And so obviously we understand how these things work. And I think that we ought to keep it there, you know? And meanwhile, one of the things that we know is that the more AI we use, somehow the more people we have to hire. And the reason for that is because these agentic systems are new skills. And now we have a lot of software engineers building agents. They used to code software, but now they're building agents. If you ask me, every one of my software engineers prefer to be building agents than to be writing Python code. You know, coding is like typing. And so they're going to do less typing. They're going to be more systems engineers, more building and creating these autonomous systems that are super cool. They're creating evals. They're creating benchmarks. They're creating guardrails. Isn't that right? And so the amount of work that we have to do to bring AI into the world is really quite incredible. And so it's creating a whole bunch of jobs. And my software engineers love this. I think we've seen, you mentioned evals briefly. I think we see that being a key part to unlocking a lot of agentic usage inside an enterprise. You need to have some sense of how it's doing. And quantifying whether it's good or not is oftentimes best done by subject matter experts who already live inside the enterprise and can easily give feedback and work with these systems to automate a lot of the tedious parts of their job and then spend time on the really intellectually stimulating parts and the creative parts. That's right. In a lot of ways, whether you're a doctor or a designer or a software engineer, you're creating an agent and you're taking all the mundane work and you're trying to get this agent to do it. But meanwhile, we're all trying to get our agents elevated to do things with us that we couldn't do before. And so that requires imagination. That requires creativity. You know, a lot of technology. I think that's spot on. Currently, a lot of the best usages that we see of agents are giving ourselves more leverage to do more things. But I think a lot of that approach is thinking about what did we do previously and can we automate that? But I think a lot of the unlock will come in the future of what couldn't we do before that now we can do. Ambition helps. A hundred percent. Ambition, agency. Ambitions help. Maybe on that vein, wrapping up, as you think about how to help drive towards this future, what are some of the missing pieces of this agentic stack? Well, today we're announcing a very big deal. I mean, this is a very big deal thing that we're doing today. We are providing the basic building blocks, the foundation, all of the key ingredients to build super agents. When I say super agents, I mean they're domain specific. They belong to you. You build them. You improve them. You refine them over time. You give them access to proprietary information, knowledge. Maybe it's super private to you. And as a result, this super agent will be able to do things that you can't imagine. And it will be extremely, extremely good. We've created all of the key parts, a world-class language model, a framework called Lanchain Deep Agents that has now been also fine-tuned to expose the full potential of Nemotron 3 Ultra, a blueprint that helps everybody do that. And of course, the runtime, the open-shell runtime that keeps it secure, the acceleration stacks that are all integrated into it. And so every company in the world should be able to, every developer in the world should able to now create these super agents, deploy it anywhere in the cloud, on-prem. A good friend of mine just built one for DGX Spark. And so now you have these agents running on DGX Spark right next to your laptop. You could have it running on a DGX station. You could build your own supercomputer inside your company if you like, or do it in the cloud. We now have agentic capabilities that you can now build for yourself everywhere. All the pieces are now here. There are no excuses not to engage it. I think that's a perfect way to end it. You got me so pumped up when you were speaking. That was a great motivational speech. So I'm going to go out and build some, I'm going to go out and build some agents. Thank you, Jensen. Congratulations. Good job. Proud of you guys.