Model N

Fortune partnered with Great Place to Work to analyze survey feedback representing nearly 80,000 U.S. employees.

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Employees say:"Model N's strong commitment to diversity, equity, inclusion, and belonging isn't just lip service; the executives and senior leaders really care about creating an inclusive workplace where all voices are heard and respected."

75 Best Small and Medium Workplaces in the Bay Area

Fortune partnered with Great Place to Work to analyze survey feedback representing nearly 100,000 U.S. employees.

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"Everyone is approachable. Literally everyone is, and that is the most amazing thing about this company. It's as if the culture carries over to the attitude of the workplace. There are so many avenues to learn, so many things to learn from, and so many ways to improve your knowledge."

Fortune partnered with Great Place to Work to analyze survey feedback representing nearly half a million U.S. employees.

GPTW Millennial Small & Medium 2023-Model N

Employees say: 
 
"Everyone is approachable. Literally everyone is, and that is the most amazing thing about this company. There are so many avenues to learn, so many things to learn from, and so many ways to improve your knowledge."

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In 2016, Philip Morris International announced a bold new mission: to deliver a “smoke-free future.” Its U.S. division—Philip Morris U.S.—is now leading that charge with a portfolio of smoke-free products, including the widely known nicotine pouch brand ZYN. Yet globally, Philip Morris International continues to sell cigarettes, fueling the very addiction it says it aims to eliminate.
In this episode, Ruth Umoh sits down with Seth Kaufman, chief commercial officer of Philip Morris U.S., to discuss what success looks like for a company trying to move consumers away from cigarettes, the ethical tensions embedded in its business model, and whether a truly smoke-free future is possible.

Can a tobacco giant build a smokefree future? | Next to Lead 

Leadership

With production ramping up and a new wave of models in the pipeline, Rivian is entering its most critical chapter yet. CEO RJ Scaringe talks about how AI, autonomy, and bold engineering are powering the next evolution of the company and the EV industry itself.

Rivian's Next Charge

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Smartphones, data centers, connected vehicles, wearables—is there any category in which SoftBank-owned Arm doesn't play? As the AI boom drives a need for energy-efficient compute in more places—and increases competition across the globe—Arm's chief executive outlines how the company's advantageous architecture and unique business model will drive it to new heights.

Within Arm's Reach

Renee, welcome to the Fortune Stage. Thank you. So excited to have you. So before we dive in, I want to give the room a sense of scale because I don't think everyone here is familiar with how pervasive arm is. You're in our phones, laptops, fridges, stoves, you name it. So give us a sense of how pervasive, um, arm is. Yeah, so what, what we do, uh, is, uh, a product called a CPU. Which is the brain of every modern electronic device. So I would say that each person probably maybe uses 50 to 100 armed CPUs on their person or in their home. I can assure you everyone has one today because we are. In literally every single mobile phone built we're in all the iPhones, we're in all the Android phones, but we're in Ford F-150s, we're in Teslas, we're in data centers, we're in washing machines, the brain that powers everything. So when you turn on your smart TV and all those apps come up and you're basically trying to pick whether it's YouTube or Amazon Prime. That whole operating system that's running on the TV, that's all running on an AM CPU. Uh, company's been around 35+ years. No, we're just talking. It's Cambridge University started in a barn in Cambridge with an ambition to be the global standard for CPUs, and that mission they accomplished it. I, I, I also want to double. It's not just devices you talked about data centers and you recently announced that in 2025 50% of all computes shipped to top hyperscalers will be ARM based. Tell us more about that because that that's kind of not expected. It's a little counterintuitive and also back to the history of ARM, um, we started in in a in a barn in Cambridge, and it was a joint venture between a chip company called VLSI Technology and Apple. And they were looking for the first chip for a first PDA known as the Newton if people remember that that device, which was a PDA way ahead of its time relative to running off batteries, had a display. There were a million things wrong with it, but what it needed was a low power CPU, something that could run off a battery, and that's how ARM was conceived, was a chip to run off batteries which fast forward served us incredibly well because back to the. To the mobile phones because the battery power is so key there and you're running off batteries, Energy consumption is key, power efficiency. So that is really the application space where we're quite strong. Fast forward to these modern data centers that are running 100 megawatts, 500 megawatts, gigawatts. Energy efficiency is everything. So ARM had gotten into the cloud data centers a number of years ago, general purpose cloud with Amazon and Microsoft and Google, but when Nvidia made the choice with their Grace Hopper chip and then Grace Blackwell, they needed a CPU to pair up with the accelerator and they chose ARM. And because Grace Blackwell is literally every single AI identity center compute. Being done, we're there too. So hence, hence our big number 50%. Incredible. Um, you've made a bold statement that ARM is the only compute platform delivering AI everywhere from milliwatts at the edge to gigawatts in the world's largest data center. What makes ARM uniquely positioned to play in this space? So let's think about the data center application we just talked about, right? These giant AI data centers that are using GPU acceleration, um, they all need a CPU as well because the CPU does all of the, the management of the data, prepares the data that goes in the accelerator. There's just a huge codependency there. You can't have a GPU without the CPU. That's fine when you've got. 50 megawatts to deal with 100 megawatts, but when you go to a very small application where wearable glasses, where you have maybe 2 watts of power, you can't put a GPU there. Simply put, you have to find a way to run that AI accelerator on the CPU platform. So that's where we play. So we not only have a solution up in the cloud, but we have a solution in the smallest devices. We've been talking about this all day, but I still would love a raise of hands if you believe we are in an AI bubble. Because perhaps you've changed your mind since the morning when Ali asked the question. OK, um, who thinks we're not in an AI bubble? OK, I think. And who, whose answer is like it depends, complicated. Yeah, I would say like 3rd, 3rd, 3rd. What do you think? Are we in an AI bubble? I think it depends depends on how you define bubble. If, if, if the definitions of AI bubble is that the stocks are overvalued and the PEs are running way ahead of themselves historically, you know, you know, maybe, uh, that's not something frankly I spend a lot of attention on. If you ask the question of are we an AI bubble relative to the technology being relevant and powerful, absolutely not, 100%. I feel that in my bones. And, and you might ask, well, why are, why are you so convicted about that? You simply just have to look at the, the areas that AI tends to help us with already. The the previous speaker was talking about vibe coding where you, you go in and, and you ask a question relative to write me a piece of software. Well, imagine a world where you go to AI and say, write me a business plan, right, or write me a way that I could come up with a novel concept to build a company that could solve this level of problem. Take life sciences. Uh, I've always thought that the killer app for AI is around health. If you look at one of the bottlenecks in drug discovery today, it's actually around trials, human trials. If you could replace all of that with AI and have AI instantly tell you from the time you're starting R&D that you know exactly what the trials are going to look like and you've accelerated drug discovery by 1015 years, that, by the way, all of what I described is going to happen. None of that is an if statement. It's a when statement. So when I think about AI and and the changes it can make for humanity and the planet and where we are relative to its capability, we're so early and the benefits will be so profound. So I, I'm absolutely not believing we're in any kind of bubble. Yeah, um, I'm gonna take audience questions in a moment, so please tee up your questions. You know, a lot of attention goes into the training of these AI models, but as demand for AI increases, the prediction is we're going to need more and more AI inference, and this is You know, the shift is going to benefit ARM. So tell us more about how you think about kind of the training inference. Yeah, and again, another example of why I don't think we're in an AI bubble. So just backing up for a moment, what is training? What is inference? Training is teaching a model to do something, uh, whether that is teaching you to recognize images, answer questions about history, solving an enterprise problem, etc. etc. Months and months and months of time spent training a model, training chat GPT. And OpenAI takes 3 months, 6 months, 8 months to train this giant model. That's training. Once the model is done, it's released to the world, and on our phones we can ask a question and the question comes back with an answer. Sometimes it thinks it takes some amount of time to give you the answer. That's inference. OK, so the giant use case as you think about it, of course, is inference. The more. Sophisticated the training model is, the more inference we're going to use. First things today, still researching companies spend more time training than they do on inference. So all this compute, these 100 megawatt data centers, they actually spend more time training than doing inference. And you say, well, why is that? Because there's just so much still that the models can get smart at. Inference is the use case that happens everywhere. It happens in the cloud. It happens in your car. It happens in your earbuds. And again, back to, you know, what is good for arm to your question. Those will all run in areas that generally will have an arm processor, so we think as inference explodes, and it will as more and more of these models get sophisticated, you're going to run these AI workloads everywhere. It will be ubiquitous. We will not think about AI running in the cloud or AI running in an edge device. It's just software. That's how it works. Do you have any questions? The audience, I'm not. Yes, there's a question at the back. Please share your name and organization. Sure, Julie Michelle Morris, I'm always here to ask about cybersecurity and the security of this, um, so. The single source, the supply chain, of course, makes me a bit nervous. Um, does it keep you up at night to know that the weight of the world is on your shoulders? Uh, we have a very unique situation in the semiconductor industry, and we were chatting about this, uh, uh, in the, in the green room prior, where, where chips were not so interesting for, for a number of years and then they got really interesting and, and one of the Events that made it so interesting was COVID, and when you lost your key fob, it took 52 weeks to get a new one. It was a bit of what is going on here. Well, that was just a function of the semi supply chain has many single points of failure, not just ARM. There's TSMC which is in a very obviously. Interesting part of the world geopolitically where it sits. TSMC is by far and away the provider of all these AI compute chips. Everything Nvidia builds is from there and AMD, Broadcom. That's a single point of failure. There is a very sophisticated device that has to go into these fabs that comes from one company on the planet for EUV. It's a company called ASML, Dutch company. They're the only people in the world to build EUV machines. There are a set of mirrors that go into that EUV machine made from a company in Germany called Zeiss. They're the only company in the world that makes those mirrors. So yes, I don't know if it keeps me up at night, but it is something the entire semiconductor supply chain has been learning to live with. But I will say, and you've seen this partially with the Trump administration, this acceleration to move manufacturing back to the United States, I think is a step in the right direction. There's a lot of criticism in terms of. How long it will take, and there's no question that it will take decades to get to where it was because it took decades for it to kind of get to the point that it is, but we have to start and I'm glad to see that it's starting because it's necessary. Well, when we were, yeah, there's a question here. When we were prepping for our call, um, I mentioned that I'm an investor in an early stage chip company that's very focused on AI inference. So perhaps you can give us a sense of like what does the competitive landscape look like. Well right now the leader is Nvidia by far and away, and they do almost everything relative to training and inference. Now when you have such a large competitor in such a large market that of course begets lots of competition, and there will be lots of competition from a combination of sources. You'll have hyper scalers doing their own chips, whether that's Google TPUs and that's gone on for a number of generations. Microsoft Meta AWS and then you'll have specialized companies trying to do chips around inference not only for the cloud but also for the smaller edge devices. So I would never try to second guess or underestimate Jensen and Nvidia. I worked there for many years. He's an amazing leader in an amazing company, but the opportunity is so large and so the scale is so broad, you will see other players, no question about it. And Iman and Kumar, so you know I do agree on the Nvidia side, uh, so building off of that, how do you see the hardware landscape evolve, right? So you know, grasshoppers, for instance, the bandwidth between CPU and GPU is increasing. So as these models get bigger, you know, agents, long context, it's all memory and bandwidth centric. So how do you see the. Ecosystem evolve and what would the, you know, CPU of the future look like? Yeah, no, it's, it's a wonderful question because. Ultimately when you're handling these large AI models and the models are if you enter chat GPT or enter a question into it or Grok and it says thinking and you're wondering well why is it thinking it's not the computer that's thinking, it's the computer that's waiting for data to come into the computer to figure out how to make the answer. That's memory bandwidth. So I think what's going to happen over time is you're going to see some innovations in memory technology. You're going to see innovations in packaging. You're gonna see innovations in interconnect. In other words, what's the material used to connect between the devices? Is it light? Is it a different kind of photonics? Is it a different area in terms of other interfaces? So I think back to your question on innovation, if you're, if you're investing in this space, it's a bit of the wild, wild west because people are looking for all kinds of different levels of innovation there because it all has to get solved. Selfishly, I think ARM is going to be in a wonderful place there because most of that traffic needs to run through or around our devices, and we're very involved in a number of activities in that space. Can we talk about physical AI, and that could be robotics, autonomous vehicles, drones, but also these small embedded AI first human-machine interfaces or wearables. And again, what does that mean for ARM? Well, again, because just about everything that you described is going to require something from a CPU standpoint to manage the activity, we will be involved. So then you get into physical AI, what's what's taking place there, and you see this a little bit with autonomous vehicles. In fact, I was reading an article that was in the New York Times this Sunday. It's, it was rather amazing. I was looking at Waymo and the number of accidents that Waymo had conventional accidents compared to human drivers. And it was about 1/10 to 1/20 just looking at and they were looking at different kinds of accidents, right, in terms of lane incursion or intersections. So there you have a data point that says, OK, autonomous physical AI is is starting to see benefits in in the cars and Waymo, wonderful company, they're all over San Francisco, is kind of autonomous 1.0. It's got all kinds of equipment on there. It's got LiDAR, it's got radar, it's looking all over the place relative to how to drive autonomously. The next generation using LLMs are they need less cameras, they need less devices, and why is that? Because they're actually using AI as opposed to looking at all the physical areas to make decisions on how to drive and how to how to move forward. What does that mean? It's going to really accelerate. Robotics, humanoids, things that can do factories, I think in the next 5 to 10 years, and again ARM will be involved, you're going to see large, large sections of factory work replaced with robots. And, and part of the reason for that is these. Physical AI robots can be reprogrammed to do different tasks. One of the issues you've had with factory robots in the past is if it was a pick and place machine for a factory, they're just optimized for one task for one task. The software was for one task, the hardware was for one task. Now if you can design a general purpose humanoid that the software is all AI and it learns by doing. It's going to completely replace a large set of factory workers. So I think in 5 to 10 years, and back to the geopolitical comment, I think it will level the playing field for countries because you will start to see physical AI be a great enabler for other economies. Quick question, because we're almost out of time. Renee, good, good comments. Um, AM is dominant. X86 is struggling. Risk 5, we'll see. Um, you talked about so many customers, right? Why then Is I'm going moving up the stack, um, going into building chips aren't you competing with customers, um, you know, what's the calculation there? Yeah, I don't think I said anything about that, um, 10, you know, it's a Silicon Valley rumor. Yeah, one of the things that we see is, is huge demand for our technology. And one of the other things we see is that the the speed required to develop products is just the demand on it is just getting faster and faster and faster. Part of that, and AI exacerbates it in the sense that. The models are moving far, far faster than the hardware can keep up. So when you sit where we do in the platform, in other words, the compute sort of starts with arm, it's not unnatural for customers to come to us and say, If the computer and models are all running on your platform anyway, instead of waiting for your IP, why don't you just build something for us that could be quicker. That is something we do listen to. So as I've said in our earnings calls very carefully, that is something we continue to explore largely because customers are pushing us for it. And why are they pushing us for it? They just want to get their end products out faster. Renee, last question, what are you most excited about for 2026? You know, I, I, I think that this continuance of, of AI and the acceleration of what we're doing, it to me, it's like, it's like being in Disneyland. It just opened, uh, we have hours and hours and hours to spend. Um, I was at a panel where. Someone said, give you a bold, give me a bold prediction for this time next year, and I said no one will be talking about an AI bubble a year from now. And then someone asked, because it popped? I said, I'm not gonna say big for that, but I, I just don't think we will. I think people have embraced a year from now that we're well on our way. I love that Disneyland just opened. Thank you for the conversation, Renee. Thank you.

Want generative AI that actually delivers results? It's harder than it looks. Two leading AI startups that specialize in corporate readiness explore the models and techniques tech leaders must consider to deploy effective AI systems. Open source or closed? Fine-tune your LLM, or retrieval-augmented generation (RAG)? And just how good must your organization's data be? We dig in.

From Rag to Riches

Great guys, so we're going to talk a little bit in this session about you know how enterprises can really get AI to work at scale and deployments, and a lot of that has to do the title of the session is from Rag to Riches and rag here is retrieval augmented Generation, which some of you. Mayo is a technique for helping AI get the data it needs and the context it needs to give good answers, but we know, you may not be familiar with my 22 panelists. So Dawa contextual AI, just 30 seconds. What is it? What, what do you guys do as a company? Yeah, so you mentioned rag. Reg is really about making Gen AI work on your data, right? So I think one of the The interesting thing about where we are right now is we have these amazing language models and amazing G AI, but the question is how do you make that work on your data, and the problem to solve there is the context problem. How do you give it the right context? So that's what we're focused on enterprise startup, uh, solving hard problems with context engineering, essentially. Got it. And, and Arvind, similarly, Glen, for those who don't know Glen, what's Glean do? Yeah, well, I mean, think of GLEA like GPD but inside your company. It's a place where your employees go and ask any questions or give it work, and GLEN will use all of the world's knowledge as well as all of the internal company's context to, to actually answer those questions or complete those tasks. And so in many ways, you know, I think, you know, the underlying technology, the concept that we use is similar, which is understand your enterprise, understand your people, your data, your knowledge. And bring that as useful context, ah, combine that with the power of language models to actually solve, you know, real tasks and answer real questions. And you guys have seen like tremendous adoption, right? You've just hit 200 million in, in annual revenue run rate, is that right? Yeah, that's right. So we just a couple of months back, you know, we, you know, announced that milestone, I guess we did today, but the, uh, but that's exciting, you know, business is growing for us, uh, rapidly. Great, um. So Dawa, you sometimes talk about sort of RAG 2.0, that there's this kind of new way of doing retrieval augmented generation. What is that really and how is that? How are people doing rag today that might be different than how people thought about doing it, uh, maybe right after chat GPT came out in, in 2023? Yeah, or even before that, right? So RAg is older than, than chat GPT. So, um, so my claim to fame originates is, is I'm an AI researcher, right? So I was the project lead on the original RAg project at Facebook AI Research. And at the time, the way we did things, vector databases didn't exist. The model we gave the data to was not called a language model. It was just a sort of generative model, but it wasn't a language model. So if you look at where we are now compared to then, it's just a very different idea even. So, so the core framework is still the same. You're still doing retrieval to give context to the generative models so that it can deliver value. Uh, but the way you do that is very different. Um, and that's why when we started contextual, we were really obsessed with this context problem. I still think that's the most exciting, most interesting problem right now. So we have these amazing language models, even if you freeze all technology, and you just say, OK, this is, you know, whatever GPT 4 5.2 or Gemini 3, we just keep that. There's still a decade of innovation that we can do just from focusing on the context. Um, and so the, the way you really want to do that is not just a vector database, right? There's a lot more going on. We're, we're doing agentic rag these days. We're using retrieval as a tool, and there are many more tools that you can use. So that's why we, we were looking for a new. We called it RAG 2.0. I don't know if that was the right name really, but I think the core observation was that what you're building there is a system. So it's not a model, right? The language model is maybe 10, 20%, and so it's the same for something like Glean, right? That's an extremely complicated system. And the language model is just a very small part of that, and I think that's why we need to have something like a rag, right? But then if you want to do that the right way, then you have to start from the systems assumption, and that's what, that's what we've been doing. And if you do that the right way, then you get much, much higher accuracy. So that's really what we're seeing kind of across the board is. Enterprise AI adoption is all about high value use cases and if you really want to get there, then you need high accuracy and that's what we do much better than anybody else. Interesting. And, and Arvin, for getting this to work well, Enterprise search is key. How, how do you do that successfully? And when you, uh, you know, there's a lot of data out there that a lot of enterprise, uh, uses of, of AI are not really scaling. They're not getting full deployment. What do you think the pitfalls are there? Why, why, where are companies going wrong? Yeah, I mean, see, first of all, enterprise environments are very, very complex. Um, there is lots of information, lots of data across hundreds of thousands of different systems. Data is permission, it's governed, like not everybody in the company can access our data. So when you sort of start naively, and you say that like, look, I'm gonna actually make AI work in my enterprise. I'm gonna connect AI with all of my systems, that's when you start to sort of run into. all these challenges. Why AI is so hard. Like, first of all, just connecting all of the enterprise information, like bringing that context into AI is hard because that means you need to build integrations to all these different enterprise systems that you have, understand the data models, understand the APIs, you know, to connect with that data. Then the second challenge you run into is that, well, oh, you know, I, I launched AI and I connected it with my Um, with my documents, but you didn't actually take care of security. And you have now AI having access to really sensitive information inside your company, and everybody in the company can then go and use it. So, so like when you, like, you know, like this, this, you know, we're all in the very early stages of making AI work. There was like this tremendous pressure from the executives, you know, uh, team, you know, within the company. Um, we had to do something, make AI work, and we didn't really, they properly like sort of prepare. For it and how to, how to make it happen correctly for us like at Glean like you know we've been in business for now almost 7 years, so we're just like, you know, like you know you're one of one of the original sort of enterprise AI focused companies. The, the first application that we deliver to our customers is, is search, which is, you know, we go in and connect Glean with all of our enterprise systems and we sort of understand more robustly, first of all, you know, what is all this. Information that you have inside the company, what information is up to date, uh, is high quality, what information has become obsolete at this point, because, you know, when you think about context again, you have to make sure that you're using the right context and making AI work on it as opposed to making AI work on scale. How much of that sort of data cleaning can be automated and how much of that is still kind of a manual process? Someone has to look at a human has to look at it, say, yeah, this is out of date. We're not actually going to put that in the database that we allow the AI system to access. Yeah, I mean, I think like when all these projects failed, like, you know, like if somebody needs to take the blame, so who took the blame, you know, the, uh, the startups, you know, and the product companies will tell you that, hey, your data sucks, you know, it is bad and it's your fault, right? So go and fix it. But the reality is that humans cannot fix, you know, the data. You cannot suddenly change our, our habits. You certainly can't like get people to. You know, actively maintain, manage information, keep it up to date, remove the steal information out. That's not how we work. Like, you know, it's, you know, we're going to work the same way as we work today. And so the, the right solution is for AI itself to figure it out. AI has human capabilities, and if you have the right AI, it will actually you know what information is good and what is not, and it will actually make careful use of, like, you know, you know, the information the right way and again, like, you know, the right security. Do, do you agree with that? Is that you can do that. Yeah, and, I, I think one of the most sort of misunderstood sayings I often hear from people is garbage in, garbage out, which is total BS. I think like what, what you want is garbage in information out, and that's why you want to use AI in the first place, right? So I think that is a very common misconception. So maybe we're not quite there yet, but In the long run, what you want to do is just apply AI to your data and all data in an enterprise almost by necessity is going to be garbage. So the question is how do you get the useful information out of that? Absolutely. I'm going to try to get one question from the audience in a second. So think of a question and I'll hopefully get. At least 11 in the audience, um, there's a lot of talk out Arvin, uh, uh, about sort of context engineering as like the next frontier of how to get Enterprise A out of work. What's your view on that and, and how are you, how is Glean working with things like MCP, you know, and scripts and all of these new ideas about giving more context to models? Yeah, I mean, I, I think that that's exactly like the new frontier of innovation is like for any given task, like how do you. build the right context and context is dynamic. Like it's not that like, hey, I have this task, and you suddenly just like you go into all these systems and pull some right pieces of information and you're done. It's actually iterative process where, you know, models and agents are going to be solving complex tasks and Every step of that execution, you have to sort of like figure out like what's the best way to bring the best information to these agents so that they can do their work properly. Um, so, so that's, that's, that's absolutely the key. Like, you know, what we do at GLEAN is, we are, I mean, we think of ourselves as we're trying to build, you know, your enterprise brain. Um, the idea behind GLEAN is you connect Glean with all of your different systems inside the company. So now we understand. You know who your different people are, um, what are the different business processes, projects, teams, uh, product lines that you offer, so sort of building a deeper understanding of your enterprise and also work like, you know, what are the different business processes and how do individuals go and actually execute those business processes today without AI, like, you know, what are humans doing? So sort of if you observe, if you actually connect with all the systems and you observe all the human activity, you're able to sort of then bring, you know, that collective human intelligence. of enterprise in one place and you do that and this is basically the repository of context and now from here, like when you actually ask AI to do some, you know, some piece of work you've got to sort of now, you know, from that repository pick, you know, the right context, you know, and that right context really is like, you know, well how did humans in the past did similar things, you know, in this company so that AI can sort of use that as the as the baseline to start and then do do its work and that's how it sort of really improve accuracy and. And and build these agents that are uh that are capable of actually extending, you know, that human intelligence and, and, and, and automate work. Alright, I wanna get, I wanna get one question for the audience. Who's got a question? And if no one does, I've got, oh, right over here is a guy, uh, with a question. Please state your name and where you're from. Rick Chavie with VX, uh, enterprise scaling platform. One question we, we are getting with our customers is around. If they have a lot of M&A and they've got multiple ERPs, e-commerce, CRMs, whatever, is coming up with that consistent framework unified to be able to apply the agents or any other technology for that matter to give that sort of single view of, of revenue execution or or whatever application you're looking at. So I'm, I'm wondering how. With your models, you know, how you sort through all that confusion, or in some cases systems may not even be really automated. You have to, yeah, and it's, do you need a single source of truth, or can you work across multiple systems? You should work across multiple systems again, so I, I think in a, in the complexity of an enterprise you are not going to have everything in one place. Right, so that's why we have things like MCP, some of the information you might want to index so that you can do faster retrieval over it, but, but in the end I think you just have to accept that you are dealing with a sort of messy reality of an enterprise and now you need to have an AI system that can deal with that. So part of that is understanding what you need to look for and so you know, doing the right tool calls, hooking up into the right ERP systems. A lot of the complexity that that that. that you solve with something like a glean or also with our platform is really about hooking up all this data. Now you have this, so you have this kind of data layer then you have The intelligence layer and then the missing part is the context layer. Like how do you get all of the right little bits of context from that in an iterative way so that intelligence can be useful. Arvin, I, I want to play something you said. You talked about having the AI learn the existing context and the existing workflows to sort of replicate those, which is probably a good place to start. But there's this tension there because a lot of people want what they really want to do with AI is reinvent workflows and change workflows. Can you do both at the same time? How, how does that work? Yeah, in fact, actually what I was saying was, see, there is that human intelligence, the human way of doing things today in your enterprise, and that's the, that's the starting point. So when you, when you, when you ask AI to actually solve a new problem, like either, you know, you let it loose, you know, with no sort of, uh, baggage, you know, to deal with, so to speak, like, you know, like let AI actually go and figure out. From, from scratch, like how to solve that task, or you say that, well, look, you know, like, you know, there's, there's some value in you knowing, like, you know, how we've been doing this work in the past. And you actually bring that context in and then, but you don't like, you know, you don't ask AI to sort of replicate that exactly what the, you know, what, how you were doing that business process before that actually just serves as the inspiration, the starting point, and AI can actually make its own decisions, you know, on top of that. So, that's how it works. So like, you know. Most of the use cases like as people are building these agents today, what we're seeing is that AI converges faster and is able to add value and you know, sort of like, you know, conforming to like you know how the business really wants to work if you bring in that, you know, context. Interesting, um, I wanna do a couple quick rapid fire rounds because we're, we're running a little low on time. Um, if what do you think is the most overhyped sort of, uh, AI technology at the at the moment? uh, what, what do you have to say? Agents, agents, Arvin. I, I, I, I, I would say that any technology, you know, any cool demo, like, you know, it seems to make a complex business process somehow like, you know, magical and automated right away because none of them is gonna work. Interesting, but I think on agents, so I think agents. Is most overhyped but also probably most underhyped. It depends on the time scale you're looking at. Interesting. I was, well, that's my next question is, you know, in, in the next year, what do you think's gonna come along? What, what, what, what's on the roadmap that you think's gonna make the biggest difference, to kind of enterprise implementation, Ivan, what do you think? Well, I mean, I, I think the models like are delivering amazing capabilities every year. Ah, this year was a big one for, for us in terms of the, you know, the, the Like the level of agents, the complexity of tasks we can handle through the thinking models. So I actually don't know what's coming next year, but again, like, you know, to, to what you said before, I think we're very happy with the underlying model technology. And, and now it's time for us to sort of really build robust integrations into enterprise systems and building a deeper understanding of. Um, your enterprise processes and automate them, but for me, like what, what, what's most exciting is that next year AI is going to become proactive. Today, like most of the AI use inside the enterprise is reactive in the sense that I, as an individual, have to go to AI, make it, ask it to do something for me, and it does it, and very few people do that. People don't have time to actually make it happen. So next year I think we're gonna see the, see this flip and AI. OK. Do, what do you, what do you see coming? Yeah, I, I think test time compute to your point is, is really, I think a lot of people are underestimating what this means. So this is really going to change everything. We are just getting started, um, and so when you hook up all your data and then you have these smart reasoning models, uh, that can do so much more, we are going to unlock all of these very complex use cases that really seemed like science. Fiction and that's already happening everywhere now. I mean, like even with things like coding, right, but how good we are at that now with AI. I, I don't think I would have expected that even a year ago. So, uh, it's going to come very quickly. I, I think next year is really going to be the year of, uh, of ROIs and through complex. Well, we're, we're out of time. We'll have to invite you guys back next year to see if your predictions are right. Thank you very much for, uh, for joining us. Thank you. Thank you.

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