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Combined for $21 trillion in revenue—and $2.1 trillion in profits. Introducing the 2026 Fortune 500, the definitive list of the biggest companies in America.

Fortune 500

This year’s Fortune 500 marks the 68th running of the list.

This year’s Fortune 500 marks the 67th running of the list.

This year's Fortune 500 marks the 66th running of the list.

This year's Fortune 500 marks the 65th running of the list.

This year's Fortune 500 marks the 64th running of the list.

This year's Fortune 500 marks the 63rd running of the list.

This year's Fortune 500 marks the 62nd running of the list.

This year's Fortune 500 marks the 61st running of the list. Wal-Mart claims the top spot again. See who made the list.

Wal-Mart stayed on top as its sales crept closer to half-a-trillion dollars. Apple moved into the top five. And UnitedHealth Group continued its steady climb. 

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HNI (HNI): Company Profile, Stock Price, News, Rankings

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HNI | Fortune

HNI (HNI): Company Profile, Stock Price, News, Rankings | Fortune

AI coding agents are transforming software development, but are human programmers still necessary? Fortune's AI editor Jeremy Kahn breaks down what "vibe coding" really means, why companies are racing to adopt coding agents, and the hidden risks of AI-generated code. From technical debt to slopsquatting, we’ll explore what the future looks like for software programming – and whether humans will play a role. 

Are Programmers Obsolete? | Fortune AI Playbook

Tech

the-enterprise-shift

You might have been hearing a lot about AI coding assistance and how this might mean that programmers' jobs are going away. So what's really going on here? I'm Jeremy Kahn, your AI expert at Fortune. I'm here to help cut through some of the hype around AI and to answer your questions. So what are coding agents? Coding agents are AI models that can write computer code. You just tell them in English what you want the computer program to do, and it handles all the coding. Vibe coding involves somebody who is not a programmer just telling an AI model they want to build an app or a website. Site and the AI model does all the programming for them. The adoption of coding agents is happening very fast. Companies like Google are saying that a lot of the code they put out is now written by these coding agents and even more traditional companies, banks like JPMorgan, a lot of their code is also being written by coding agents too. So why does this matter? Number 1, it's redefining the role of the developer. Software programmers no longer have to worry about writing individual lines of code. Instead, their job is more about setting high level features and functions and then supervising and checking what the AI does. Number 2, coding agents lower the technical barrier to entry. Now anybody can write code. You don't have to work for the IT department. Number 3, AI agents can accumulate A lot of technical debt. Technical debt means when a company's legacy computer systems or IT systems cause it problems in the future. With these AI coding agents, they often write code that on the surface looks great, but it can be riddled with cybersecurity vulnerabilities and actually be brittle. Number 4, don't trust AI to remember everything. AI models suffer from a phenomenon called hallucinations. That's when AI models make up. erroneous information in the context of coding assistants, that hallucination can take the form of made up libraries or unit tests. These coding assistants sometimes make up those libraries or they go to libraries that they shouldn't go to, and that's led to this phenomenon called slob squatting. Slob squatting is when you have a bad actor who goes out and registers a library that they think an AI agent might visit, but instead of putting good code in that. Library. They put malware there. The coding agent goes and grabs the malware and imports it back into the network. Back to the original question are software programmers obsolete? Well, autonomous software development is pretty much here today. Engineers at companies like OpenAI and Anthropic say they've gone months without handwriting any code. They just supervise what the AI coding agents do. So what does this mean for people in other professions? Well, there are some people I think that the same kind of automation is coming for every knowledge worker's job in the next 12 to 18 months, but I think that's going to take a lot longer than that. In most other jobs, there aren't something equivalent to unit tests in software where you can automatically tell if the code is good or not. In most other professions, telling whether the work product is good or not requires subjective judgment and a lot of experience, and AI models aren't yet good at that.

Investors are pouring billions into robotics amid the AI boom, but only a handful of startups will win in the famously capital-intensive and technically complex robotics space. Sequoia’s Stephanie Zhan and Skild AI’s Deepak Pathak unpack the race to build a general-purpose “robot brain,” separating what’s hype from what’s quietly transforming industry. Then, watch the future in motion as Skild AI’s robots learn, adapt, and perform live on stage.

Brains for Bots

Conferences

Stephanie, Deepak, thank you for being here. We're so excited to be here. Thank you for hosting us, Sally. I think we should start with the biggest central question right now. The AI boom has facilitated a lot of hype around robotics, but is robotics overhyped? That's a very good question and a very natural one. Like if you look at the whole history of AI right from 1954, from the days of Turing, robotics was the first application that AI was basically founded for, you can say. And if you look at all over the years, We all have seen impressive robotics videos for decades. Now, you may not have seen, but they are super impressive robotics videos even from the 1980s, 1970s, but still no robots around us, and it always feels like robotics is coming in the next 5 years, next 5 years, next 5 years. It has been 70 years. So is it overhyped? Is it not overhyped? It's a very genuine question. And one thing to look at here is the way robotics is done today, in the last, uh, I would say even 3 or 4 years. is very different the way it was done for the past 65 years before that. And that's a big change. Now, what is that big change? In one line, the change is Things in robotics used to be driven more by human intelligence. Like somebody super smart would look at this task X Y Z and have the robot preprogram the robot mathematically to do it. What has now changed is that these models or these robots can now learn from data. So that has shift from programming something to learning from experience. And that is what this new wave is about, which is so different about robotics. And this is, we know this approach generalizes from all the results we see around LLMs, VLMs, language models, and all that. And that is one big change from the all 5 year chunks before that and this 5 year chunk what we are seeing now, Stephanie. So to put that in perspective, Deepak has this wonderful saying that he likes to to repeat, which is that what looks hard is easy, but what looks easy is really hard. And I'm sure that all of you have seen the videos on X that go viral. It's a robot doing a backflip, a robot running in a straight line, a robot folding laundry or doing some kind of household chore, and all of that looks impressive in the abstract. You know, in a vacuum on X, it looks amazing. But it actually requires expert discernment. And I think that um this is what Deepak is getting at. What you don't actually see is whether this robot was trained for general intelligence. Whether it was able to do that task, actually interacting with that environment, or actually capable of executing that task if something in the environment were to change. A small tweak, for example. What Deepak and Skilled are capable of doing is that they're building this general intelligence that enables robots to do anything, anywhere on any robot hardware. And that's massively transformative. It's similar to what OpenAI and Anthropic have done. Um, in the world of digital intelligence, but instead they're doing it for the world of physical intelligence. And so, you know, the challenge I think for you as a consumer of all these videos is to really discern what is real and what's not. And even amongst the videos that go viral online, there is massive overhype, um, but there is. Real progress and real trajectory that we're working towards and that's what I'm so excited about with skilled. Well, this is the point both you and Deepak have been very, very clear with me about, which is that it's actually a lot easier to program a robot to do a backflip than it is to get them to climb stairs. You're smiling. Why? Please, I think we should maybe show that video. Let's do it. That, can we cue the parkour video? Yeah, no, that's, that's not yeah, here we go. The issue is I, I sent Ellie multiple videos to choose from with no clue of which one to choose, and she chose this one. Now this, now it will look super impressive. Wow, the robot is doing this. I cannot do this. Uh, and it, it is like. And it can keep on going. It's funny, right? But one thing you would notice here is There is very little interaction of the robot with the world. Apart from ground, the robot is not interacting with anything other than ground. Maybe there was one block in there to jump over that block by putting their hands over. Now if you think about this, so this going deeper into this, uh, OK, I'll let the video play. Nobody will listen to the voice. Well, I was gonna say the other thing about this too is, in part, a huge part of what you're doing is around a robot brain, right? What does that actually mean? Yeah, so one thing, uh, like, uh, if you think about this, this component, hard, easy, easy is hard, right? This was, this is not my quote. It's from Hans Moravec. He was also a CMU professor, uh. This was the culmination of 30 years of work from the founders of AI from 1950s to 1989, and this was their whole conclusion, heart disease is. Now what does that mean? It means that, for instance, uh, If somebody can do a backflip in humans or if somebody can throw a ball into a hoop from very far away, you would pay millions of dollars to watch those people on stage do that, right? But then if somebody goes and opens a door or climbs a stair, you would not be surprised. Why? Because every human can do that. Now if you think about both these tasks, what is different between the two? The difference is interaction with the world. Backflip is full control of your own body in a free space, right? Which is exactly what computers are good at. If you tell a computers everything about you, about everything controlled, they're good at doing that. One example is airplanes. We have had airplanes for almost 100 years at this point, right? They fly in the free space, so there's no interaction going on. But when you talk about picking up a bottle, a glass of water or going on stairs, what that involves is using your vision. To immediately to continuously correct where you keep your next feet on the stairs. Any one point here and there and you and you fall backward all the way. And that interaction is what is the root reason for human gender intelligence. It's called sensory motor common sense, which you don't appreciate because every human, almost every human has it. Yeah, I was gonna say when you put it that way, walking upstairs is an incredibly high. endeavor, um, I mean, Seth, when you're thinking about this, you know, Sequoia is famous for being interested in big markets. What do you see in terms of use cases, market opportunity? Well, I would highlight a couple of things, um, you know, if skill succeeds, we completely transform what robots are actually capable of today. The market's massive already, you know, you know of companies like ABB. Kanu, Kuka, Mitsubishi, Kawasaki, that alone, just on the industrial side, plus all the consumer folks, that's massive. But they're still constrained by having robots that are only built for specific things. And the real potential of what skill transforms is opening up the possibilities that are otherwise not possible for us to achieve. So if you think about Commercial settings, security and surveillance, robots always fail at stairs, elevators. But if we're capable of actually producing robots that can do all those things, we can tackle security and surveillance. Take industrial, all the different types of tasks that that would otherwise fall under edge cases that robots still fail at. If skill succeeds, they can tackle a whole host of industrial use cases that often pose dangers to humans because of the materials and cramped spaces that you now have to deal with. And then finally, in consumer settings, Um, you know, you see all these household robots, but they're only capable of doing one thing. It's your one vacuum cleaner, your one dishwasher, whatever it is. But if you're if we succeed at building the general intelligent robots, you finally have consumer robots that can actually tackle the whole host of household tasks that you now have. And so the opportunity here is, it's really all of the unlocked possibilities. The a parallel here is, you know, if you had thought about OpenAI and Anthropic back in 2016, it would have been. Hard to actually understand what the scope of market potential was because we were still living in this vertical AI world in software, but the real market for an open air anthropic is the market potential for all of digital knowledge work. And in the same way, that is what skills unlocks. It's the market of all of physical labor work, all of blue collar work today. And not only that, I mean that alone is transformative, but what's even more interesting is we can get all of this at accelerated pace and at a fraction of the cost. Because you have something that is generally intelligent, you no longer have to program these robots for specific things that take a long time to program. And they're only available at 6 or 7 figures today. But if they succeed, you now have this generally intelligent software that can run on any robot hardware for an order of magnitude smaller in cost. And so that's what's most transformative and excites us about skills. We're going to turn to the audience for questions soon, but one thing I wonder about is, um, especially with the influx of capital moving into this space, is it a winner take all market, a winner take most market? I'm curious for both of you. I think this is a very, very good question. Uh, it's always hard to predict about the future, but in, in my opinion, I would say that the answer is very technical. Why is it technical? Because in robotics, if you see, what's the main issue? The main issue is data. Like if you see the language model land, whenever these language models, whenever show some result, any company, let's say open, they release some result. You see 5 of the labs almost reproducing the same result in a matter of months or a few months. Why is that? Because data exists for language models on the internet, for vision models on the internet. Now, whether you can use it or not, it's a copyright issue, it's a separate issue altogether, but it exists. But for robots, there is no data on the internet. So how do you get, how do you begin? Then uh training all these models to start with. Like the huge success of AI is driven by big models, big data equal to success. And we are missing big data in robotics, which is where the key part is. Right now we have to use alternate sources to somehow bootstrap robotics to get it out there in the field. But once it goes out there in Whichever company gets out there first across huge diversity of tasks, that creates sort of a data flywheel from robotics, right? And the interesting part is that's different from self-driving though. Because in self-driving, people still drive cars because cars help them go from point A to point B. You don't drive a car to help self-driving. That's not your goal. But in robotics, so robots have to be useful for something to begin with. But once, if somebody deploys these robots in large numbers across large numbers of tasks, Then you create the data flywheel, and that becomes your mode in some way to go forward. And when that becomes your mode, it almost becomes more like rich gets richer. Like, the more data you have, the better your models are, and the more data you get. So it becomes harder to catch on. And this is a classic trajectory for markets. Which are not heavily democratized like it's monopoly duopoly, triopoly, I don't know. So the winners will really be the winners. Yeah, exactly. What's your take, Steph? I agree with that. I think that the market traditionally has been very segmented based on different industries that you specialize in, but the reason is that you specialize in making robots for automotive, for industrial, for something else. If skills is successful, then they completely create this new category above all the hardware players. They become the generally intelligent software that powers everything else, and so it has the potential to be more winner take all. And I would say that I deeply believe Skilled is the leading frontier lab for building all of general physical world intelligence. They're leading in pushing the frontier. This is just a snippet of what you've seen. Uh, there's much, much more that you should check out on there. There are some, there is a video that involves a chainsaw and the robot brain the legs off a robot that where the robot actually exhibits emergent intelligence and generalized intelligence, so you should go check that out. They're the leading, they're leading in pushing the frontier research. They're also leading in terms of actually getting the. Robots out with real customers in in the real world and then I was the third thing I would say is they're leading in terms of becoming the talent magnet for all of the most elite talent in this field. And so it's been really exciting just 2.5 years since our founding, and they're on an incredible trajectory and, and much, much more to come. I think that it's moving at an accelerated pace, um, and it's, it's exciting to see. I would say you're a little bullish. I would add 11 thing to the argument about uh this monopoly versus like uh uh uh multiple companies. What I meant this argument was more for the software, right? But if this is of the hardware, it's very rare that people would want to, everybody want to buy the same hardware. So hardware is very, very difficult, unless you have, unless like Nvidia, like some chip or something. But if you're, if you the end hardware, People just want preferences. If you, if your neighbor has some hardware, and your other three neighbors have the same hardware, you would want something more unique for yourself. And so hardware, in the hardware side, you would see multiple companies, because that's how it is. Multiple form factors, different designs, different uh uh appearances. What, what I made the argument about, uh, this data, that's more for the brain part. Kind of like phones with Android kind of situation. Questions. We have time for one question. Oh my gosh, there are so many questions. How do I pick? Whose hand was up first? OK, I'm trusting you. I'm trusting you over there. Um, Deepik, one of the things you mentioned was that every year in robotics, it feels like by next year or the following year, we'll have something that's actually more mainstream. What do you think is gonna be for consumer applications or for people in this room, the time period over which we'll actually start to engage with robots on a daily or weekly basis? Yep. Instead of giving time frame uh for consumer like that you can buy or not, I would say one thing before you see robots around you in houses, which almost seems like the, the main narrative these days. You must be, you must have to see robots around other areas like when you go to grocery stores, when you go to hospitals, when you go to uh other more structured hotels. We are not seeing that right now. I think first that wave has to come, and then once you are already familiar with robots around the world, you're seeing it in real life, then you will say, OK, I have to buy those things now for my personal use case, the same way how computers began. First, computers were used for professionals. They were more for companies. The idea of personal PC came much later once people got familiar with them around it. The same timeline you would see in the shorter term. What we will see next is More and more robots are deployed in the enterprise scenarios, which for the jobs, which are not where not everything is known or not pre-structured, where humans are involved. Let's say a human is doing some task on an assembly line, robot works alongside that. And that is not possible today. Today, robots that have to be only in cages and things like that. So you start from those areas, which is where, where skill is beginning as well. We are already deploying in a variety of areas, variety of, uh, Um, factory setups, but all those setups are the ones where there's randomness involved. And they act as bootstrapping, uh, uh, platform for us to go to the next stage, which is more unstructured, which is more like hotels, hospitals, things like that. And then they eventually stage is consumer. It's the same task everywhere, same skills everywhere. So it's, uh, the ordering has to be some somewhat like this. And I'm going to take it home. I'm sorry to all of you who had questions though, um. There is a lot of anxiety around robotics. As much as there are all the fun videos, there are also concerns about killer robots. There are concerns about robots taking, taking jobs. What can our robotics future look like? I'll start with you, and then we'll end with you. Well, I would say that you know there's a large labor shortage in a bunch of different areas. Um, there is incredible pain that people feel in just the churn of labor in different industries. So that's an area, both of those are areas where I think a lot of the services that robots will now be capable of offering will first start to tackle. It's the places where there are already pain, um, a high urgency, um, and desire to adopt fast. I would say there are 3 essays of robotics future. Three Ss, OK. The first S is safety. Uh, now, this is not robot safety. If you look at the human jobs today, right? We are right now sitting in this room, we are a bit privileged to be working here. That's our job. But if you look at, there are still so many jobs out there, where every day people go to work and they risk their life. Either for a short term basis, some risky jobs in a, in some scenario, or they receive their life from chronic issues from like continuously bending, you know, in a position and doing work. But people have to do this work to make the society function. So those are the jobs where you definitely need uh robots. It will only improve the quality of life of the workers who are working, doing these jobs. It's the first, safety of people are doing dangerous jobs. Then 3rd, 2nd, 2nd reason is If you think about jobs. While this is true, the narrative is that robots will take jobs, but if you look at the employment statistics right now, there are almost a million more job openings than people are available to fill them. At the same time, there are so many people who don't have jobs. What is the discrepancy? It's a discrepancy of It means that people want to do certain kind of jobs. So there's a huge, almost a million gap, and it's only increasing, uh, after COVID. It has really widened. So people want to do certain kinds of jobs but not other kinds of jobs, but they have to be done. Like today it's a huge labor shortage for blue collar, uh, workers for big factories, big, uh, big, big corporations, and it's totally the opposite narrative. a million jobs short, that's the highest, uh, we have seen, sorry, million labor short of the jobs available, right? Now the third aspect is, let's say we all, everything works well and robots do really well and they get deployed. Then the 3rd aspect is more of a task for all of us. It's a social aspect. It is true that when robots become really good, uh, in the future, there has to be some work around how do we handle the job aspect, the salary aspect, and what people do. And I believe there is a local minimum. It is a social issue, like, so that not only few people are benefiting from the same this evolution, everybody is benefiting because there's abundance of everything. But if we go beyond that, then there lies a scenario, a good scenario where everybody's doing things that they like, uh, work is more optional, and they are doing things that they enjoy instead of being forced to. And in that scenario, there are so many robots walking upstairs. Deepak Stephanie, thank you so much. Thank you, Ali.

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.

The electrification of everything–from transport and manufacturing to AI and data centers– is pushing power grids to their limit. Across Southeast Asia and beyond, the pace of new energy demand is outstripping the capacity of existing infrastructure, threatening to bottleneck growth just as investment surges into clean tech and digital economies. And it’s not just about building more wires: it’s about building smarter, faster, and often across national borders. For ASEAN, where regional integration is essential to competing as a next-generation energy and data hub, the challenge is as much political as it is technical. Can countries coordinate quickly enough to deliver the grids the future demands?  Or will ambition run straight into the hard limits of transmission?

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