All right. Good afternoon and welcome to JPM organ's 44th Annual Healthcare Conference here in San Francisco. My name is Harlan Sur. I'm the U.S. Semiconductor analyst for the firm. For the seventh year, we have the team from NVIDIA presenting. As all of you know, NVIDIA is the leader in accelerated computing and AI semiconductors software systems enabling the development and deployment of the world's AI foundation models like large language models, enabling next-generation reasoning and agentic-based frameworks, and now moving the industry adoption curve to physical AI and driving compute innovation for cloud hyperscalers as well as large vertical markets like healthcare and life sciences. Here with us today from NVIDIA is Kimberly Powell, Vice President and General Manager of Healthcare at NVIDIA.
She's responsible for the company's worldwide healthcare business, including hardware and software platforms for accelerated compute and AI that power the ecosystems of imaging, genomics, life sciences, drug discovery and health care analytics. Kimberly, great to have you back again. Let me turn it over to you.
Thank you, Harlan. Thank you so much. Thank you. Good evening everyone. This is the first time I've been between you and cocktails, but I'm going to make sure it's as entertaining as humanly possible. Just a few words that this is an absolute once in a generation platform shift for the healthcare industry and I am so honored to be invited back for the seventh year. We're in our 17th year of working on healthcare at NVIDIA and I thank the conference very much for this opportunity and all of the partners that we work with day in and day out, which I hope I'm gonna be able to share what the future's gonna look like. Before we get into it, please have a moment and look at our forward looking statements and, action.
Okay, 2025 was an absolute breakout year for agentic AI. Many things came together in just the last 12 months. You've heard the words reasoning, AI models that can reason. You've heard the words tool use, software that can actually use tools on behalf of the human user. You've heard the word retrieval, being able to attach language models with trusted information and trusted knowledge. Agentic AI is here, alive, and being deployed faster in healthcare than any other industry. The ChatGPT moment Jensen just described to us last week has arrived in physical AI.
The amount of progress we are now able to make in robotics because we've closed the loop in a very important domain called simulation where robots and embodied AI will learn in a computer first before they're ever deployed to the real world is here and it's having profound implications across the entire healthcare and life sciences industry. And then thirdly, and we've been working on this for some time, and many companies are here sharing with you that AI is starting to understand and learn the laws of nature. Biology in particular, you might call us at the beginning of the transformer moment of biology. Let's start though with thinking about what's happening in AI. Let's take a minute to understand how AI is really making such rapid progress.
One of the most important things we need in the world is open models and open software. Just like you could think about Linux back in the day as an operating system that created brand new markets, we're exactly here at this time. And what's amazing to think about is open models are now reaching the frontier, which is giving an opportunity to every startup company and actually every enterprise to participate as fully as very well stocked AI labs around the world. Open models and reasoning models that really came into light at the beginning of 2025 are absolutely the backbone of innovation. These are models that can think and they are much more relatable to humans and they can create the essence of transparency and explainability. They can break down very complex tasks that otherwise were just untouchable or had to be hand coded in the past software.
So 80% of startups today are built on open models and it's a very, very important strategy to NVIDIA. Over the past several years we have been amassing a huge body of work in the open source. In 2025, actually, NVIDIA became the world's largest contributor of open source AI o n Hugging Face. We have over 650 language models that have been contributed, 250 data sets, and those are not only in language, they're also in biology, in chemistry, robotics, in vision. And so, key to developing an ecosystem is to not only provide open models. When we say open models, we actually mean three things. One is the model itself. Two are the open data sets for any company or any industry that is regulated. You need to understand how these models came to fruition. You might even need them for auditing purpose down the road.
So open models, open data set, and the third is open tools. Just like all intelligence, learning is never finished. Every single user interaction you have with your software, with your application is training data to enhance the system going forward. So you need to create a whole tool chain for the end-to-end AI lifecycle which is essentially a never-ending life cycle. So when we say open models, yes, the capability of open models are extremely important, but it comes with open dataset and the open tools. And so we've been pioneering in some very important places. We just announced the third generation of our Nemotron language models that are absolutely at the frontier for agentic AI. We've recently announced our physics AI models Earth-2 things that we can do in climate and weather simulation.
Our own Clara models in healthcare for biomedical AI that spans everything from target discovery to molecular design and medical AI reasoning. We're at the dawn of this ChatGPT moment for physical AI. A lot of that physical AI has come from the foundation models we have pioneered at NVIDIA. Last year's CES was actually the breakout moment for Cosmos, which is the best innovation of the show for understanding the world. World foundation model understands the laws of physics, understands spatial awareness, can create digital worlds of all kinds with millions, billions of permutations in which robots and physical things can learn in these environments. GR00T is for robotics so that you can train robots to operate in these physical worlds.
All of the different training policies, all the different tasks that needs to take us from very special specialized robots to more generalized and they can complete really amazing tasks. Just last week, Alpamayo, which is for self-driving cars, the first time we've ever open-sourced this. This is a model that is essentially a thinking autonomous vehicle model. It has large language models at the root of it, and it's an end-to-end driving system. Incredible work that is going to lay the foundation for much of the physical AI to come. What I also love about 2025 and us moving forward is agentic AI has become hireable. I would hire this man. I think all of you would as well. What do I mean by that?
All of those breakthroughs that I just described, the ability to reason, the ability to call the tools necessary, the ability to interact with antiquated systems, whether they're scheduling systems or otherwise, all of that has largely now become solved in the age of agentic AI. And so healthcare systems all over the globe are recognizing that they can start hiring these agentic systems and platforms essentially as digital coworkers to close this extreme gap we have in terms of healthcare services and the number of healthcare professionals we have. As you know, the World Health Organization predicts going to be tens of millions of healthcare providers short by 2030. We can offload our amazing healthcare professionals who've dedicated their life to their profession and offload a lot of the clerical work that isn't necessary clinical work.
I love this report from Menlo Ventures that describes that I would say for the first time in my history and my being in this industry that healthcare is leading the pace at a technology enterprise deployment and adoption. It's actually at three times the pace of the U.S. economy. And that is because it's solving such acute challenges. And so it absolutely is here a $4.9 trillion U.S. market and we are deploying AI at this incredible scale. These are paid and enterprise- grade software systems that are now being hired. And the psychology of CIOs and health systems all around the globe are recognizing this as an opportunity. It is just not possible to go out and find another 500 doctors that you can hire into your system.
But by offloading your amazing doctors with systems that I'm going to share with you in just a moment, we have an incredible opportunity to do so. I've talked about this, we've talked about this for quite some time, that the way software is being built has fundamentally changed. This is in essence what all agentic systems and what all software- as- a- service platforms will look like when they are agentic. They are prompted with some input and this amazing reasoning system kind of understands the user intent. You will always use frontier models and you will also augment these frontier models with specialized models because the work that gets done in industry is exquisite work.
It's specialized work, there's subject matter expertise involved, and so you have to call upon a lot of different tools in order to connect these agents and connect these otherwise generalists to become specialists and deliver the value that's necessary in the industry. Let me share a couple amazing examples. I think many of us here have heard of Abridge and actually they're on the conference schedule this year. Abridge is a clinical conversation AI platform. Their platform again looks like those systems. By connecting these systems in such exquisite ways, understanding workflows in a building block sense to transform workflows, they're giving 30% or more of doctors' time back at the end of the day, helping them generate reports and prior authorizations.
Deployed in over 200 health systems already and that number is growing dramatically faster even as we speak in the last six months. Corti is a healthcare agent platform that is helping Europe and the NHS deploy agents of all kinds. And similarly, we have Speechmatics and Sully who are creating agents to triage, creating agents to check you in. Agents that can be deployed all over the hospital for again, not necessarily clinical work, but amazing workflow that creates a win-win situation. It's a win situation for the healthcare systems because more patients can come through and it's a win for the patient because the experience is far improved. Now these agents are going into another high-stakes, very high-cost area of the industry and that is in clinical development.
This is a part of the drug discovery and medical device process that is absolutely necessary, but it's a very challenging part of the system. It's very labor-intensive, it's very manual, it's very frankly error-prone. We work with amazing companies like ConcertAI which is helping to stratify these clinical trials and even simulate their outcomes so that you can do much, much better planning, the amount of money, time and resources that can be saved and the precision to get to where these clinical trials need to go faster. CytoReason is essentially building the capability to do drug development by building disease models, using knowledge graphs and otherwise to really understand and help build better modeling with all of that real-world data.
We've been working with IQVIA now for well over a year, and they have agentic systems being deployed from the commercial deployment of commercial teams who can give you a sense of in what region, what physicians you should call on with very relevant data so that they can have much more productive commercial teams as well as into all of the clinical trial, finding the right start-up studies and building those at a much, much faster pace than before. This agentic digital health ecosystem is being built on NVIDIA. Our open models, our tooling, and our ability to help them connect and build out these agent systems to do incredibly wonderful things.
Now agents are at a also very exciting inflection point where they are accelerating science. This is the loop of science that is emerging not only in life sciences, but particularly in life sciences, is having a very, very accelerated effect on how we're thinking about doing science. AI scientists are agentic systems who you could imagine could go off and read the literature, you can go back and forth and reason with them. They can help you design the experiment, they can call upon tools, they could be foundation models for, for protein structure prediction or do virtual screening in the digital lab, or they could actually go off and kick off an experiment in a physical lab.
You can also think about the computational dry lab as this connective glue to close this loop as we described, as you have the agentic system, you have the physical space, but you need to constantly take every experiment and build that into digital intelligence of the R & D work that's being done all over the world. There is a new emerging ecosystem of a category of AI science companies who are building on, again, NVIDIA's Nemotron, a huge additional breakthrough of last year that's come into vogue in order to create these agents that go beyond generalist understanding and into science and technology. It's called reinforcement learning. Using experimental data to reinforce these models and tune them into very particular science tasks. Edison is the new commercial company that came out of Future House. This is a stunning AI scientist.
This scientist can go off and read 1,500 papers, write 40,000 lines of code and synthesize a research report in about 16 hours, work 16 hours straight and essentially do the amount of work that would otherwise be four to six months by a researcher. Pretty mind boggling. LILA is building a super intelligence, a completely integrated autonomous lab and agentic system. So literally all experiments come back to feed the superintelligence and creating a complete closed loop system. And OWKIN is combining biological language models with deep patient data to really help biopharma teams have a higher confidence in their decisions. So these science agents are really here and they're making a profound impact on, there's really a new paradigm in science that is emerging.
As we know, life sciences is one of the largest science domains and pharma R& D is the largest of that. So a $300 billion industry of R& D is going to be reinvented with this paradigm. I want to share with you how agents are entering the lab. They're not only going to be co scientists along with you that you're kind of talking with, working with, hypothesizing, they're creating reports for you, but they're actually going to do work for you on behalf of the scientists or with the scientists in the lab. Let's take a look.
For centuries, lab instruments have allowed us to observe the machinery of life. Today they're doing something more profound, generating the data that's fueling the next generation of digital biology. As lab environments grow in complexity, AI agents are stepping in not just to measure, but to act. Thermo Fisher and NVIDIA are embarking on a journey to build this fundamental AI lab infrastructure. AI agents created with NVIDIA, Nemotron and NeMo Agent Toolkit help scientists plan and run experiments and then autonomously call AI tools to analyze the results in real time. For example, Thermo Fisher's Attune CytPix Flow Cytometer uses quality control and analytics agents to help scientists interpret data as it's generated.
Like this example where the instrument agent identified a data quality issue and recommended an instrument adjustment to improve the next experiment. The benefits are clear. AI agents scale essential data and insights with less manual work. Together, we are building the future of autonomous labs, accelerating science, delivering breakthroughs.
We're super excited to announce today that we're working with Thermo Fisher Scientific, the world leader in lab instrumentation and services, to build what we're calling the fundamental AI infrastructure for the lab. You can see there, that little gold box is essentially a benchtop AI supercomputer called DGX Spark. This DGX Spark can run any workload of AI and accelerated computing. You can hold it in the palm of your hand, and so we pioneered this first agentic system where you essentially can make the instrument intelligent, right there with that amazing gold box and some agents that we build together, and you can close the loop from the, the scientists is there. Sometimes you can close the loop right with the instrument. The instrument with an automatic quality control agent can understand, "Oh, I need to go clean something in the instrument".
And it can autonomously self-relieve it and come out with much better experimental data. And so it is very clear that this is going to drive, this is going to scale the throughput of labs, it's going to increase the quality of experiments, and no longer are humans going to be kind of the thing that's bottlenecking the amount of data that we can come in and do science. So this is just an amazing partnership and we're delighted to be partnering with Thermo Fisher. N ow getting into physical AI, labs are one of the most chaotic and bespoke physical environments that are out there. And so we really want to think about scaling labs with robotic intelligence and into those real worlds of physical labs.
And so there's a journey here that we want to have not only specialized robots that really understand a given instrument. We want them to also be generalized. And we actually, we want the best of both worlds. And so to get to that best of both worlds, NVIDIA has created the physical AI three-computer platform. I was describing this earlier, where you use simulation and our Cosmos World Foundation Model to create digital worlds to train these robots in. You can vary the lighting, you can move beakers, you can practice all sorts of different tasks and train your robot in simulation f irst. You can use Isaac and the training platform to train all sorts of different types of robots or for tons of different tasks.
Sometimes they need to be contamination tasks, sometimes they have to have different perception because they're looking for barcodes or they're looking for a glass like this size, or they're pipetting and then we have also the edge computer for you to go off and deploy this. S o AI and lab automation is reaching far into the physical world, and so again, a new class of companies is emerging into not only lab automation, but robotic lab automation. We're working with some fantastic companies in this space. Multiply Labs is using our Isaac platform to train their robots. They're doing amazing work in cell and gene therapy biomanufacturing labs where they're using the Isaac system to train literally thousands of different tasks. Because as you're going through some of these more complex therapies, it's many, many steps involved.
And these are very precise steps, the precision, and you don't actually want humans involved because of the contamination aspects of it. And so they've made some tremendous breakthroughs where, you know, take a cell therapy that costs $100,000 to manufacture and they're reducing that down to $30,000 over 70% with their robotic systems. And they're essentially putting 100 times the throughput in a given square space, square footage lab environment. These are the breakthroughs that are going to scale medicines to where we need to go. Similarly, HighRes Biosolutions, complete lab automation at very large scale. Exquisite robots that are learning again in our environment, using Isaac and Cosmos to train them and learn all of these different tasks to take automation to yet another level.
Then Opentrons, you know, very, very well known for their liquid handling, you know, deployed in 10,000 labs around the world, again using our platform to build the simulation environments and increase the velocity at which these robotic systems are able to tackle more and more complex tasks in the lab. All right, we are in the final chapter here of AI starting to learn the laws of nature. It took a few headlines. The NeurIPS conference is the flagship AI conference of the year. There was over 30 workshops, developers of a kind. There was 50 or so biology and life science company parties at this event and it was written up as biology's transformer moment.
AI-driven revolution in drug making is well underway now and we're really starting to see AI-enabled medicines reach the later stage of clinical development, which is extremely exciting. A lot of those companies are here. We are working really hard to help push the frontiers in this area of biology transformation models. We don't aspire to necessarily be a biology foundation company, but all of the methodology, all of the challenges in which it takes to scale these models at a domain-specific level. You know, language is, you know, short words assembled into a sequence of a sentence that looks very different than a three billion-character-long DNA. So we need to think about context length when we're talking about biology. We need to have different model architectures. These models have to get graphical, grounded in physics. So there's a lot of interesting challenges.
And so we've been adding to our Clara open models to help the entire research industry really accelerate the ability to train larger models as we go here and multimodal models. We're really proud of some of the work we're doing in La- Proteina which allows you to, at the atomic scale, to essentially design proteins. Our very new one that we're announcing is RNA Pro for RNA design. The first that we've had. Merck was just up here. We did some exciting work with them on that KERMT model which is all around predicting toxicity. And so we're really trying to work across the drug discovery process with these open models. We have a reasoning model for molecular synthesis in our version 2. Really exciting about these models. And so we also announced today a pretty massive extension to the NVIDIA BioNeMo platform.
Not only are we investing heavily in these open models, but additionally, as I said, it's not just about the model, it's about the data sets and we have a roadmap to continue to invest in data. This industry, like other industries with self-driving cars, will benefit from synthetically generated data. And so we've generated some synthetic proteins. We also care a lot about doing data processing things like cheminformatics, workflows like RDKit. We now have a GPU-accelerated NeMo kit that is 100 times faster in chemistry processing. And so this platform expansion is really, really foundational and it essentially, as I said, it's part of that glue, it's that digital dry lab that is going to take all of the intelligence from experiments and continuously enhance these models going forward which can then be called by the agentic systems as their tools.
So what's exciting is we're seeing enterprise adoption of BioNeMo and some pretty exciting platforms. Basecamp Research is an AI native company who announced their Eden platform today here at this conference. This is a GPT4 size biology model that was trained on 10 trillion biology tokens and it's able to now do things what they're calling gene insertion and their lab result, their validation results of what it's able to do in antimicrobials and in cancer is really pretty groundbreaking, amazing Eden platform. We're working with Natera who is for cell free DNA. They're training their own models and then also building into their platform agentic systems to advise in clinical development, advise in the clinical decision support and then TetraScience is a scientific data cloud platform again to try to connect all of these.
As we know, science is done oftentimes in very narrow but when you can try to now start to learn across many different data sets to ask all of these scientific questions, we're working with them. They're deploying BioNeMo models inside. They're deploying Nemotron and it's an amazing platform for scientists. And so this is the vision, this is the new paradigm in science. And as I said there's an amazing group of new markets, new companies that are being built all around this vision of AI scientists that can call tools that are constantly getting smarter by the experiments and these experiments have automation that will come from the agents setting it up. But you're still going to need robotics and otherwise to help you execute that in the physical world. To bring this whole vision together.
We are so excited to announce an extension to our partnership with Lilly. Today we announced a first-of-its-kind co-innovation AI lab with Lilly. This is the first time we are going to be joining together world-leading scientists with world-leading AI researchers in South San Francisco here in the Bay Area, co-locating with the amazing science and lab understanding that comes with doing drug discovery. We'll be investing $1 billion over the next five years to really push the frontier in this new paradigm of science and new paradigm and acceleration of drug discovery. This is building upon their deep belief that they see this transition of 90% wet lab to 10% compute. Imagining the paradigm where that's, that's deeply, deeply flipped in the next coming years and it's going to accelerate the breakthroughs.
We're going to work on clinical development and we're going to work on manufacturing and lab automation just like we described that they're doing. Lilly is world class in manufacturing and accelerating the ability to deploy physical AI throughout labs. And manufacturing is also going to continue to be transformative and help them meet the amazing demand that they've created for medicines in the world. So this has been a phenomenal kickoff to the year. I'm going to leave you with one last video and then I'm going to join Harlan over there for some Q& A.
Traditionally science has been a manual pursuit of knowledge. Now we're able to accelerate discovery with the power of computation. We're no longer just observing the natural world, we're designing it with accelerated computing. We've learned to read the source code of life at atomic resolution, seeing into the molecular machinery of a cell. But reading biology is just the beginning. Generative AI has now learned the laws of nature to program biology, designing new proteins, molecules and therapies inside a computer with intention and precision. AI is emerging as a critical collaborator in scientific discovery. One that explores in the digital world and learns in the physical. With agentic AI, intelligent tools can perceive reason and adapt, turning every instrument and lab into an autonomous system that scales discovery in real time.
This is the transformer era of biology. Powered by NVIDIA and open to everyone. Ready to build what comes next.
Great presentation. Thank you, Kimberly. I'm going to kick off the Q& A. You know, we've seen the deployment of massive, what Jensen Huang calls AI factories by the leaders in your segment of the market like Amgen, Genentech and recently Lilly with their Blackwell Ultra based DGX SuperPOD. Right, and this potentially signals a shift from pilot programs to industrial scale development and deployment. Can you walk us through the economic conversations you're having with pharma CFOs today? Are we at the point where they view GPU compute investments not just as an R& D expense, but as essential capital infrastructure that integrates AI agents, AI tools that directly determines their pipeline throughput and probability of success?
Yeah, I do think, as I was just describing, this is a new paradigm in science completely. And the amazing scientists that, you can think of t his as, a scientist or an employee, you really want them to be as productive as humanly possible. And so if there's a new scientific method sort of emerging with the ability to take all of, think of Lilly and many pharmaceutical companies, hundreds of years of science is written down in electronic lab notebooks and it's kind of stuffed all over the different parts of the company. You actually have the ability now to build all that back into the corporate knowledge of the company. Every scientist that works there. It's a very transient industry, frankly. But why lose all of that deep understanding of a scientist when they leave the company? You can actually inject that back into the system.
So from that respect is taking all of that amazing high value data and doing something with it to empower the whole organization. And then this transformer moment that we're in is becoming very clear. I mean we're in the fifth year now, post AlphaFold in its true initial impact. And it was the inspiration that has driven a lot of work in the area of models. And now there's thousands and thousands of biology and molecular models being built every single day.
And with the sort of democratization, if you will, of what we're doing with open models and the data sets and the tools, we're giving the capabilities for science teams who are not OpenAI like they are scientists, you know, for the job they're hired for, but they can now become AI scientists and AI researchers just the same because we're making it much more accessible for them to develop these things. And so this, that I think that the visual of you have agent scientists working along with and then you have a dry lab, wet lab. I mean, you said it is going to be exactly like the wet lab and you're going to have the dry lab be as intelligent and reasoning with you and calling upon all of the data that you've ever built and all the new data that you're building.
I absolutely think that it's going to be thought of exactly like your wet lab. I see this 90%-10% flip starting to go the direction and it's not going to be less lab expense. We're just going to do much more science. That's what this is all about. You know, this is not a paradigm in which if you, if you flip it to computation, you don't, we don't need more of that. Absolutely not. Just think about radiologists and the reports are coming out. Everybody thought, because you can read, you can do the task of reading an image and finding something in it, which is one of the tasks that a radiologist does, that we weren't going to need radiologists soon. That was what one of our godfathers said. In fact, reports are just out.
We've increased the number of radiologists that are being hired because there's more work to do. And so we should just think of this all as we're going to do fundamentally much more science, which essentially will also lead to many, many more breakthroughs.
You know, last week we held the Consumer Electronics Show, right? Jensen had a big NVIDIA live event where he announced his new, the team's new GPU computing platform called Vera Rubin. Right. And one of the key highlights of that was Vera Rubin is going to continue to drive cost per token or cost per inference lower by up to 10x. Right. In every generation of GPUs that the team has brought to market, cost of inferencing, cost per token is going down somewhere between 3x to 10x. This is per year, right. So with that in mind, in hospitals, you've highlighted the labor shortage crisis. And introduced agentic AI as a solution for everything from patient triage to administrative coding.
For a hospital CEO operating on razor-thin margins, what is the immediate ROI of deploying NVIDIA powered agents compared to traditional staffing? In other words, is the cost of inferencing finally now low enough to make this viable for mass market sort of health care adoption?
Yeah, and you're right, in the last four years we've had Hopper to Blackwell to Rubin, and in those four years we've reduced inference by well over 100 times. So if you were paying $1 to run an agent, you're now paying $0.01. And you need this, you need this for rapid adoption. And so there, these companies that I just described Abridge has hundreds now of millions of users, right? Open Evidence has hundreds of millions of users and they're using it constantly. And so we have to continue to drive the cost down. Now, the return on investment is very clear. If a doctor has 30% of their time back, that's either 30% of life that they can return to having with their family and keeping them, you know, employed and safe at work, or you can also see 30% more patients.
I mean, it comes with all sorts of benefits. It's a win for the patient, it's a win for the health system. And so you can measure a lot of those companies that we talk to, they're literally measuring how many clinical minutes they're giving back to the organization. And I think it was the Sully and Speechmatics there, there's something like 57 years they've already measured since the platform's been in deployment that they've given back to the health system. So that's, that's clearly measurable in ROI because the more you can give back of free minutes is essentially the more patient throughput you can have.
When we think about accelerated compute in AI, we typically think about use cases, customers as being the large cloud, cloud hyperscalers, your corporate and enterprise partners, but Jensen always reminds us, right, that there's a sovereign AI opportunity. It's a $20 billion per year market opportunity. We've seen Japan's Tokyo-1, we've seen Denmark's Gefion supercomputers launch with a very heavy focus on healthcare and genomics, driven by the need for data sovereignty, national competitiveness. Do you view sovereign healthcare clouds as a standalone growth factor for the team separate from enterprise, and do you expect every major economy to build a similar type of infrastructure build out?
Yeah. So to answer your last question first, yes, I expect every country to be able to take advantage of this incredible again, once- in- a- generation opportunity. Some countries will go from zero healthcare services to complete AI-native healthcare services, and that's a fantastic opportunity. And now that we've made it so accessible to do so, we've done several things. NVIDIA's platform is inside of every public cloud. NVIDIA has also pioneered essentially a generation of what they're calling neo clouds, so clouds that are residing within the walls of certain countries, giving every country the opportunity. I mean, if you think about what AI infrastructure is, it's just as important as roads, electricity, water. It is a necessary infrastructure for any country to prosper in the future.
And so they can get it in the public cloud, i f that's good for them, they can start building their own. A lot of telecom companies are transitioning themselves into cloud companies that can be hosted. You can build it inside your own enterprises if you like. And so to answer your first question last, no, it's not a separate. It is all part of our enterprise business. We've just now created the conditions that everybody can, should and will build their own infrastructure to serve their own country, to prosper.
Perfect. Well, we are just about out of time. Kimberly, thank you for your participation. Looking for another strong growth year from the team.
Thank you.
Thank you.
Thank you so much.