Why OpenAI Is Racing to Make Computer Use Superhuman

We've gotten computer use now to a place where it's faster than the average human. And I think we're getting really close where it is superhuman. What sorts of tasks do you think are ready to hand over to the agent versus the ones like, OK, it still requires some checking and correcting? Computer using agents now, since Astra, is now capable of doing anything that a person can do. We think a lot about, like, what new consumer experiences can we enable with a faster, better model. And then on the enterprise side, it's kind of naturally why the safety becomes more interesting. How do we give the ability to control the amount of human the loop there is and then the auditability? It's unlike any other thing that we've built where the developer has to do some work to adopt it. Someone has to build a connector or MCP server. You know, computer use works with everything. Now we have, like, capable models. They're getting, like, cheap enough to run at scale. What should kind of companies think of automating? I think of it as in two big categories. The first category is, like, AI agents can increasingly use the same software we do, clicking buttons, navigating websites, and completing work without dedicated APIs. What changes when they become faster at it than we are? A16Z's Seema Amble and Fabrizio Serafini joined OpenAI's Ari Weinstein and James Sun to explore the rapid progress in computer using agents and why computer use could become a universal interface for AI. They discussed how agents could automate tasks across legacy enterprise software, finance, procurement, and sales, alongside the technical advances driving progress, including why the harness surrounding a model can nearly double its speed. They also examine how enterprises can control and audit agents. And what comes next? AI that proactively works in the background, collaborates with us in real time, and eventually navigates software faster than expert humans. Welcome to the A16Z podcast. I'm Seema Amble. I'm joined by my colleague Fabrizio Serafini. In August, we wrote about computer using agents and how they're starting to do the real work inside companies. Today, I'm joined by Ari Weinstein and James Sun from OpenAI, who are going to help unpack what is going on with computer using agents from their perspective and share some thoughts on the future. Ari, James, thanks for joining us. Of course, happy to be here. Yeah, thanks for having us. Ari, kick it off and tell us a little bit about what in Astra has made computer using agents move forward and what's really unlocked. Yeah, so Astra's an amazing model. It's good at so many things. Our computer use models were already so good before Astra. You know, like Sol itself is amazing at computer use. So Astra really helps with a few things around the edges in my sort of anecdotal experience. One is it is a little bit faster on average because it uses fewer thinking tokens to do the same task. It's better at certain kinds of creative work. Like, for example, we had this demo where in our launch video where you were talking to it and it's drawing things on Microsoft Paint. It's way better at that sort of thing, which I think probably transfers to other kinds of creative tasks also. And then the last thing I find is it's just more reliable around the edges. It's kind of, I feel like I trust it a little more. It's sort of the kind of thing where there's a little difference that is hard to perceive, but really important between something being 99% reliable and 97% reliable. And I feel like it's sort of just a little higher in a way that feels pretty concrete. Yeah, and I think adding on to some of those things, Astra is also our most aligned model. And so talking about trust a little bit, if you look at the system card for it, it is much better at being resistant to prompt injection. It's much better at not taking any kind of unintended actions. And so when you think about it for enterprise work, especially for companies using it for sensitive things, whether it's procurement or talking to vendors or talking to customers, you're going to feel a lot safer kind of using it. And that's really, really exciting. And then some fun things on the creative side. I think internally we had Astra play video games against 5.6 and it wins every time. And so there are some cool tweets that people have made where Astra is able to beat Fallout in 30 hours or something like that. And so it's also just really amazing at playing video games. So I don't know, that probably translates to something, but yeah. Maybe just a double click on that point. Consumer versus enterprise use cases of computer agents, very different implications around kind of like all the scaffolding around, I would say, the computer using agents. How do you guys think about where things are more exciting between consumer and enterprise if you were to pick? I think our goal at OpenAI is really about building products that's useful for everyone. And so actually it's interesting because Ari is definitely more of a consumer guy here, and I spend a little bit more time on the enterprise side. I think the consumer is always the place where we try to push the frontier on what's possible. With Astra, like Ari mentioned, it's much faster compared to the previous models. And so we think a lot about what new consumer experiences can we enable with a faster, better model. And then on the enterprise side, it's kind of naturally why the safety becomes more interesting because I think the enterprise, whenever we talk to large companies, I think the use cases for computers is actually fairly obvious, right? Inside enterprises, there's lots of legacy software that doesn't have any MCP tools. They are software that's been used for 15, 20 years. And so a lot of times it's actually kind of hard to use for a normal person. Lots of UI that is from the last decade. And so you don't even know how to operate some of the key things. And so we've seen companies build skills that kind of teach the model to be able to use them better. And so I think maybe the way to think about it is enterprise is where the capabilities have been there maybe for a little bit longer and the companies are catching up but trying to understand how to deal with it. And consumer is where we're trying to figure out the next types of use cases that's not maybe possible today with software. Maybe you can share a little more. Yeah, I mean the consumer use cases are so fun for me because it's kind of like how can you put your agent to work doing the things in your daily life that are so annoying and save you tons of time and just have more time to focus on the things that you're excited about. I think it's been really fun to see Dot where each Dot gets its own computer and you can ask it to do things for you in the cloud. It gets a full web browser, it can buy things for you, it can book haircut appointments and anything else you want it to do. So that's been really fun and I think there's a lot more on the horizon that's going to be really fun. Yeah, that was going to be my question on Dots. I think James, you probably use a Dot to walk through product feedback. It would be cool if you could share more about it. I'm just so curious to know on your end guys, how are you using Dots and how does computer use play into kind of what you can do with them? Yeah, I mean I think Dots is really cool for a few reasons. One is really proactive and so I've been very surprised a lot of times in both my personal life and work when Dots will just flag something that I just completely slipped through the cracks. Just the other day, so internally on our email system, whenever we get an external email for an invite, it doesn't actually show up on our calendars and so I end up missing a bunch of external meetings and so Dots now is catching these things for me. It's like, oh, there's this invite that's in your inbox. It's not on your calendar. Do you want me to like accept it in your Gmail so it shows up? So that's been a lifesaver. It's a small thing, but it matters a lot. But I also have it proactively kind of looking at, I think it's really good at proactively looking for tasks where it's the shape of it is not super, let's say, concrete every single time. So an example that I also have it set up to do is we use Slack internally and there's a ton of messages that's going on at all times and I used to have this Slack job using automations and codecs that's every hour or every 30 minutes go and try to pull things. And I've just ended up tuning out a lot of the notifications because it's just sometimes useful, sometimes it's not. With Dots, I can give kind of a high-level goal. It's like, hey, if anybody in my reporting chain says anything, flag it to me, right? If anybody who's giving some critical feedback, use your judgment about certain products, go for it. And then because it has a cloud computer, I've had this pretty cool workflow where when someone reports, they usually have a Sentry log. I could have it go use the cloud computer, actually drive my local browser while the Dots can control both your local computer as well as the cloud computer, log into Sentry, pull the logs, analyze it, give me feedback. And so it's saving that entire loop of figuring out which feedback is important, looking at it, then going to Sentry, downloading the logs, feeding into codecs, asking for thoughts, and all that can now just kind of be automated. And so, yeah, Dots is great for at least these proactive and kind of long-running tasks where they're not the same shape every single time. We talked a little bit about reliability, speed, token usage, and efficiency. How do you guys think about balancing all of these things as you keep making improvements on what you're releasing? I think we're really excited about computer use at OpenAI. We've invested in it a lot, and we've gotten to a place where I think we have the fastest, cheapest, and most reliable computer use product. And I think there's actually so much further we can take that. We spend a lot of time looking at speed. Speed is really interesting because, on one hand, the power of computer use is it can do tasks in the background. So in some cases, it doesn't matter how long it takes because you can be going and doing other things while it's accomplishing the task for you. But also, sometimes you want to watch it to make sure it does it correctly. You need to wait for it because it needs to complete this before something else can happen. And so the speed is so important because you don't want to get this feeling like, Oh, I could have just done that myself, and it would have been so much easier. So we've gotten computer use now to a place where it's faster than the average human at using a computer. And I think we're getting really close to a place where it is superhuman, meaning it is faster than an expert computer user like probably one of us at this table. And I think that's going to actually be a sea change when that happens. I think it's going to change the way we all interact with computers because all of a sudden, anything that you could do manually, it could do faster. So unless you really want to do it yourself for some reason, which I think actually there may be many reasons people would want to do that. But in a default case, you probably just want the agent to do it for you. And I think that's going to let us build all kinds of cool user experiences. So speed is something we think about a lot. And then obviously, we have a strategy of offering multiple different models at multiple different qualities and price points. And so for different use cases, you may want to use a different model. At the same time, our less expensive models are getting much better at computer use over time. And so I think there will actually be more variation over time in which models people want to use for which tasks. I think that's what I was saying. I think the other thought is, how do we give customers control over the safety, reliability, autonomy versus speed? When the model is slow, you can kind of watch it do stuff, but then you're limited to the tasks that you can do because you can't just sit here watching the thing doing it. It's actually less effective because it's slower than you. But once you can get to a point where you can no longer observe and audit everything it's doing, how do we give, especially enterprises and businesses, the ability to control the amount of human in the loop there is and then the auditability, right? I think a few months ago, I was part of Astra a few months ago. As part of Astra, we kind of launched a set of enterprise controls that let businesses choose, one, how much human in the loop is there? Do they want the agent to automatically approve everything? We have this really awesome system called Guardian, I think auto-approve in the app, that is a really smart model looking at every single action that the computer use agent is taking to decide if it's safe, if it's aligned to their original intent. A consumer might be like, I just want it to do everything, and I don't want to be asked for approval. Whereas an enterprise is able to decide which type of actions actually go through. I think the control part becomes more and more important as our computer use agents become also superhuman in doing things. Totally. I think as you try to roll out more enterprise use cases, I think employers are excited about the efficiency, but they're also wary of what's sort of being unleashed. Maybe even on the other side, have you guys seen the response from application software around, okay, now there's computer using agents accessing my legacy workflow software, and do you guys think at all about that? Are you more focused on just the agent that's being used by the consumer enterprise user? Yeah, I think this problem becomes really interesting on the open web, actually. I think when it's within software that is approved and used by the company, it's actually more straightforward because it's like, oh yeah, 100%, we want to have people automate and do work in here that they don't have to do tediously themselves. But when it comes to the internet, I think you kind of have a few different camps of folks. One is our business model is not in line with having agents use our product. You think about advertisers, publishers, whose business model is entirely on advertisement. If the agent's using the website for them, then where's that revenue coming from? And I think most places an agent's ad impression does not actually count towards real revenue. And so that's an interesting question that we're always working through and working with our partnerships team on. There's another question around for the companies who do really want to embrace having computer-use agents using their sites. A lot of them are adopting standards that let humans or people and agents to be able to use it together. So WebMCP is a standard that we're really excited about. We added support for it in the browser. And so you can imagine these scenarios where the agent is elevating the experience of using the site, but it's not entirely replacing it. So I think browsing the internet is still very fun in my opinion. I still like doing shopping. I still like looking at all the hotels and flights and places. And so you can imagine places where the agent does all the tedious work, but it kind of carves out an easier experience where you're doing the browsing. You're like, okay, now I want to finish the transaction and then put it in my credit card and do all this stuff, then you can hand it back to the agent. So WebMCP is kind of an interesting way where your interface with the website is still the site itself versus an API, but then the person is kind of using it together. And so those are some thoughts. Shifting gears, I want to double-click a little bit on what it means to do the job or get the end-to-end job done. We'd love to hear a little bit from you guys around what works well, how do you guys define the right narrow repeatable task, and what sorts of tasks do you think are ready to hand over to the agent versus the ones like, okay, it still requires some checking and correcting on the user side. Generally, I found the model to be pretty capable to do anything that you can do on a computer now. And one exercise that I try to force myself to do is that whenever I have a task, the first thing I think about is, can I just have the agent do it? Now there are obviously some sensitivities around if I'm sending a message to someone, I feel like it's coming from me. But in general, I think the way I think about it is the computer-using agents now, since Astra, is now capable of doing anything that a person can do. And there are questions around, I think, before of like, oh, there are some like squishiness around like specificity and requirements. I've generally found that the model is getting pretty good at whenever you're asking it to do something and there's some vagueness, it will actually, with Astra, I think we launched this like flexible input, right, and so it will actually ask you about, hey, like, I want some details. And if you don't answer in time, it'll just keep going and then pull the context as necessary. I think there are some things around, if you have repeatable workflows, it's nice to be able to kind of define, you know, the requirements or at least similar to like writing a product spec. But in general, I generally feel like unless there is something incredibly sensitive that you want to be in the loop for, I just reach for the computer-based model for everything I do now. I'm curious if the same for you. Yeah, it is, I'm trying to, you know, build that impulse to reach, right, because, you know, it's so good and sometimes it's so easy to get locked into the old way of doing things. I think the really cool thing about computer use as a technology is the universality of it. You know, it works with every application. It's unlike any other thing that we've built where, you know, the developer has to do some work to adopt it, someone has to build a connector, MCP server, you know, computer use works with everything. And it works by using the application the same way a human uses it, which I think is something worth understanding if you haven't spent much time with this technology. It, like, computer use uses computers the same way a human does. It makes the agent have all the capabilities you do, which I think is so powerful. Yeah, and I guess, like, adding on to what Ari's saying, like, part of it is, like, I think over time I've stopped thinking about it as, like, am I using them on, like, a computer use task or a non-computer use task? It's frequently they're, like, connected, right? Like, the way we think about computer use is that, like, computer use is not, like, a thing you pick up and just say, I want to do something with it. It's really, like, the universal kind of API that the model has to do whatever it takes. And so if you have an ambitious task and you want the agents to do it, likely the more complex it becomes, the more likely it will probably touch computer use at some point in that journey. And so, like, maybe the only time that I don't reach for computer use for some cases is if the task is so simple that then it's, like, easier for me to just go and, like, click two buttons and do it. But, you know, as computer use gets faster, even faster, then the question becomes, maybe I don't need to do that. I just, you know, drive voice and it just uses the computer with me. So those are some interesting thoughts there, too. Do you think there's anything you have to share with, like, an enterprise user to get them comfortable with that? Obviously, you guys are totally built on computer use, but, you know, I think there's obviously, like, hesitance sometimes on the enterprise user of, like, okay, well, yes, the human would click through it and probably do it worse. But there is that feeling of, like, there's accountability and, you know, that person's trained and, you know, all the classic kind of rebuttals. I'm curious how you have to convince people around. For sure. Lots of conversation in enterprises kind of over the last little bit. And I think the main thing is we think about it as two layers, right? The first layer is, like, how do we create deterministic controls? I think that's really what gives, you know, enterprises a lot of feeling of safety is when they can, even though our model is the most aligned ever, and it's, I think, better, you know, better than most people at, you know, protecting against prompt injections and unintended things because it's, like, just, you know, the intelligence is so smart. But I think there is just a feeling of safety when the enterprise admins can basically say for certain tasks or for certain data sources, just too sensitive to be used. And so we see kind of enterprises, so what we launched was two things, right? One is the ability to be able to deterministically create a sandbox around your agent. So being able to say it can use certain apps, it can use other apps, it can use certain websites and not certain other websites. And then two is, you know, this kind of part of a human in the loop. Like, what type of actions does it require a human to be intervened? And so we might say, okay, for any time there's, like, a download and upload, a person has to kind of approve it. Anytime it goes to a new website, a person has to approve it. Now, again, these are all, like, dials an enterprise can kind of go through. And so it's really, like, that's one. And then two is really just showing the enterprise admins just how, you know, all the evals that we've run internally around how aligned these models are, even compared to, like, you know, both internal and kind of third party evals. I think there's also some baseline stuff that we do across the board to just try to help people build trust with these kinds of products. You know, we know it's so new, it's such a magical and sometimes alien experience for people. And so, you know, we put a lot of work into the design of the product so you can always see the cursor if you want to watch it perform a task and it sort of animates in a fun way so it's not boring to watch. And then, like James was saying, this is very customizable for enterprises, but also for a regular user. You know, we ask you for permission before using a new application or website, depending on which sort of modes you're in. And I think that helps people sort of not feel like it's going to go do some runaway stuff on their computer. You kind of want to have this trust building moment where you figure out what it feels like and what you want it to do for you. And then you sort of feel more comfortable over time. The Codex cursor is so cute. I love it. It's like such a, I know it's a small detail, but like watching it use the computer gives you like confidence and it makes the experience nice. So I think there's a kind of like a user experience point that's worth, that's definitely worth investing on. And I guess I'm curious, like in a broader sense, like, okay, now we have like capable models. They're getting like cheap enough to run at scale. In your view, what should kind of companies think of automating? Like what would you, where do you see the most promising kind of use cases within enterprises? Are there some workflows, maybe something around the ERP, maybe something around kind of how like, again, humans and agents can work together to get tasks done. But like, if you have to like pick where are you most excited to see your models perform in the world? Yeah, I guess like from my perspective, I'd love to hear ours. It's like two, I think of it as in two big categories. The first category is like automating, you know, tedious workflows. That's like the more boring one. And I'll give us some boring examples. Like- Boring is opportunity. Boring is opportunity, yeah, exactly. Like ERPs is a really big one. And so all the acronyms you can think of, ERPs, CRMs. All the systems of record. Yeah, every single system of record, right? And so we know we've seen folks, you know, procurement software. Like, you know, you're going to vendors and every year you're ordering a bunch of lab equipment. Instead of having this giant spreadsheet with like a thousand rows, and then the ops team, so it's like, you know, work-to-learn companies, like normally what they have done is they have this giant spreadsheet that gets filled out by the teams themselves. Then an ops team takes that giant spreadsheet, puts it into an internal, you know, procurement software. And then that gets like, you know, finalized as a bunch of lines. And then a sales team then goes, procurement team then takes that giant software output and then goes out and reaches out to vendors. Computers can automate all of that, right? So like procurement is a big one we see. Finance is another really interesting category we've seen. And so, you know, there's a lot of different ledgers within a company, right? And so you have like ledgers for expenses, ledgers for all these things. And so at the end of the month, there's a lot of reconciliation. And a lot of these financial software don't have APIs to be able to quickly pull things. And so the finance team being able to have computer use, look through all these ledgers, reconcile, pull out anything that's like interesting to flag either to a person or then do some investigation. Also a very boring but useful workflow. For their sales team, a lot of sales teams uses a lot of different software for like transcripts, for getting data, all these kinds of things. And so having computer use go and do prospecting and then going and putting that into a CRM or some kind of like, you know, outreach software and then all that. So all the system records is the boring part. And then like market research, another one that we see a lot. And so, you know, in my free time, I've been building this like video annotation software for badminton, a lot of badminton. And like, I like to film myself playing the game and then having the agent kind of go and look at stuff. And I was like, okay, how do I make this thing really useful? There's some software out there for like pickleball, for squash, for tennis. And so I just had computer use, go log in, create an account on these softwares, try it, take screenshots, give me feedback and then try to build it back into the app. And so these are all kind of like tedious workflows that could be done. And then I think some of the more like interesting, creative work of like enabling more ambitious work is like, you know, we talked about the model now being really good with like visual design. So circuit design, you know, being able to like architecture work, hardware engineering, game design, these are also areas that I think we're really excited about where the Astra can take. Less, you know, less common for enterprises to think about those right now because they're thinking about the boring opportunities. But I think as folks understand the capabilities of the model more, that's where I think we'll kind of see more usage next. But curious if you have any thoughts, Ari. Yeah, I think kind of similarly on the consumer side, there's a lot of things where it's sort of automating drudgery or just doing things you don't want to do. It's filling out DMV forms for you. It's helping you unsubscribe from a bunch of emails in your inbox. And then I think there's also things that are more like, I'm hearing from a lot of people's stories of people doing things that they just wouldn't have time, they wouldn't have had time or wouldn't have thought to do otherwise. Like, I don't, for me personally, I'm maintaining spreadsheets about my finance and spreadsheets about meal planning. And I have a friend who's a personal trainer who's making customized things for his clients. And some of these things would just be so time prohibitive to do in your personal life. But this makes it so easy. I use Apple Numbers, which is like a obscure spreadsheet program, but the model still knocks it out of the park. I don't think I've ever heard someone use Apple Numbers. It's a surprisingly great spreadsheet software. And then I think there's a third category of thing, which I'm kind of the most excited about, which is just starting. And I think, you know, on the horizon, you know, which is really just having a different way to use your entire computer or interface with software. I think you saw in our Astra ad, really cool examples of people just talk to their computer with their voice. It's such a natural way to use your computer. And I think over time, we're gonna see the form factor of software itself change due to this technology. And I'm really excited about that. On that point, okay. Form factor of software changing. Imagine if you're an enterprise user, you're used to using whatever clunky ERP from, you know, 30 years ago. There's a world in which you now, you know, unleash a set of agents that just operate that software and you don't really care. There's another world in which folks are a new, like AI native player is using a computer using agent. And I'm curious if, do you think the world is gonna go more in the direction of like keep the legacy folks and just have this layer on top? Or do you think, you know, computer using agents can absolutely be part of new software players? And this is something I think a lot about. So I'm curious to get your guys' thoughts. It is such an interesting moment where simultaneously, the cost and complexity of creating new software is so much lower. So you can't imagine software changing so much, but also there exists so much legacy software in the world that computer use is so well suited to interact with. So I expect both of those things to continue for a long time. You know, if you look at the software that's in production today, a lot of it was written 10, 20, 30, 40 years ago. And I don't think that that stuff's gonna go away overnight. I do think that in some way, the existence of computer use may put more pressure on companies to make software work better with agents because all the software is going to work with agents by default. And I think WebMCP is a great example of this. And I think we'll see more stuff like that. So yeah, I sort of expect both to be super important, but I do think it'll like kind of shift over time. What do you think, James? Yeah, I agree with you. I think it's like the way, there's like a lot of arts and craft and you understand this because Ari built a beautiful product with his team. The cursor was all him. And so I think there's like a lot of art and craft into building like great products that people can hold in their hand. And I don't know, like for me, if we go, if the internet becomes just this like faceless, you know, thing that is just agents talking to each other and people are not using anything anymore, I would be very sad. And I don't think it will be like that because like there is like something about craftsmanship and artisan software that's like just feels nice to use because the person who designed it really, really understands like the experience that the customer wants to have. So like my wife loves, is recently got into playing this like, you know, board game. I forgot the name of it. It's where you connect like five pebbles to like win. I think it's like Omak is the name of it. And so anyway, so she's trying to get good at it because she's playing online with friends and it's getting her ass kicked. And so I was like, all right, I'm gonna build you like a tactical trainer for, you know, going through different gambits so you can like get better at it. Like that's, for example, something that like, I, you know, created the first version. I was like, oh, I know like she likes to do it in a certain way. She likes these type of ways to learn more than others. And so I was able to like, you know, craft the software to be something that feels really good for her. And like these kind of personalized software, I know it's like, I think maybe today still feels like only really tech people will do. But I think if you like think about this a few years out, it's like if anybody can build anything and they can build these really craft software in a very short amount of time for like niche use cases that are like so well tailored for people, I think that's a beautiful world. And again, you know, at the end of her training, the lessons and mistakes she made, that could be an agent that summarizes it and gives her in a text format. But when she's actually playing with the AI and trying to get better, that's something that the person has to do. So I do think it's, I'm more, I'm bullish that I don't think UI is going away. And I think kind of a little tangential to your original question, but I do think that the pressure of cheap software will make it so that software has to be better. But I don't think it will just be like, yeah, everything will have an MCP and an API and, you know, computers will never be useful. I love this point so much. I think that like humans are so. hardwired for direct manipulation, you really want to go to, you know, move and do things with your own hands. Yeah. And I think that it's really exciting to think about how software in the past was so one-size-fits-all. It had to be, you know, often a lot of the software we interact with every day is designed for millions, tens of millions, hundreds of millions, billions of people to use. And I think we will enter an era where any piece of software could be designed for a hundred people or one person and just how much more personal and unique that kind of software will be. Yeah. I, my mental model is I think there's probably two buckets. One is on the consumer side, you've got either something you just get, you get joy out of, you enjoy using, or you learn from, there is this, it's not the utility of it. It's the joy of interacting or learning or whatever it is, and you want to spend time. On the B2B side, you may want your agent to complete a bunch of the tedious work, but you also, there's this concept of like the audit log, like, and you, that could be personalized in how you want to interact. But the product really is me as the human user setting the goal and then being able to like understand the logic, but not necessarily need to run through like the entire chain of command of how it happened. And so like any B2B user still needs to figure out, okay, like, why did that payment get made and go back in and get a summary of what happened and be able to say, okay, pay this person at this time, yeah, at 30 days, this much discount should be applied, et cetera. They want the agent to take care of like the execution of it, but they want to be able to at some level, like some level of control still needs to be put in there. And I think that's like something that does require a UI of some sort. And it has to be, I think if our, if we're moving more towards believing that AI will do more of this, that part has to be enjoyable to use too, right? Why not? Like, why should we settle for shitty software? I do think it'll be really interesting to see if we, and I'm optimistic that we will see new kinds of software where it has traditional user interfaces, but is also using agents under the hood. And then maybe sometimes those agents are themselves using computer use to automate legacy software by giving you some much nicer, more purpose-built experience. I'm like really excited for that classification. It's kind of like the one where we like to use internally, it's like the shared intelligence layer, right? You have your agent using legacy software somewhere else and doing things at other places. You like, if you're using an application, the application wants to get access to that context so it's able to deliver a better experience. Like I think if we had, every website has its own agent that doesn't talk to your agent. It's a very fragmented world. And so figuring out like how that plays together, where the agent can plug in and use your other software. I think it's going to be like a really interesting thing to play out for the next few months. I love that you guys talk about this because I think it's so underappreciated, the importance of like human-AI interaction, especially if you want like a product to be successful. So like being thoughtful of like, how can I kind of, like humans are used to collaborate. So you want any agent to be able to like kind of fit into the team. I'm just curious, like if you apply that to a new startup, let's say you're like, I don't know, starting a company tomorrow and you want to use computer use to like solve a workflow within the enterprise, how would you think about it? And would you be kind of, what would be the things that jump to mind in terms of like, oh, I want to know when it fails well, I want it to be able to communicate with people or I want it to be like, I don't know, a black box. Just like maybe if you had to talk to founders right now, what advice would you give them as they try to use your models and capabilities to just solve problems for enterprises? In my mind, it depends on, it depends so much on the modality of, you know, are you trying to, are you a small team and you want sort of individual on the team to be able to use some piece of software that your team is using, but in a more efficient way? Or are you maybe building something that itself wants to interact with existing software, in which case maybe you have some, you know, machine in the cloud somewhere that's doing computer use in the background. I think that will really affect the modality of how you want the human agent collaboration to be, right? And yeah, you know, in the former case, I think a lot of our products are serving this use case, you know, where you sit down at ChachiBT and, you know, there's a lot of great, you know, collaboration you can have there. And in the other case, you know, if you're building something with the API, you're doing your own computer use implementation, then yeah, then it really is interesting to figure out when do you want the human to be in the loop? Is the agent telling you when something's wrong? Is it telling you when something went right? You know, is something summarizing that so that you don't get bombarded with notifications saying, hey, this happened, this happened, this happened, which actually you're going to ignore. I don't know. What do you think, James? Yeah, I mean, you know, both Ari and I were startup founders before we joined OpenAI. And so, you know, I'm still very bullish on startups. You know, I think the question, right, kind of comes down to like, what, is there still a mo? Right. Is there something that startups can still tackle while labs are doing things? I mean, in general, I just think there's like two things. One is like, I think there's so many things to build, right? And like, even though we're quite big now, thousands of people, we are still feeling very stretched in internally. And like, you know, it's like the teams are not very big for all the problems. And so if you have a problem that you really deeply understand, you have industry expertise, you understand how regulatory works, the go to market motion, how to service those customers. It is still, I think, a very green field out there. And the tools that you have through our APIs, like Ari mentioned with computer use, I think I think will still let you accelerate. And speed, really, I think at the end of the day, speed and pain is kind of the moat that exists today. I think the other thing I think about a lot is like there are, in my opinion, still a lot of like vertical industries where the diffusion of AI is like much slower. And so, you know, I was talking to a company the other day who uses computer use to help restaurant groups to, you know, take pictures of like their back storage, you know, and then using that, the vision looks at kind of like what is going to be missing based on a projection for tomorrow's traffic. And then it takes that to then use computer use to go to like a Shopee or some kind of like procurement software to then order your groceries for the next day. And then based on that, it will go into your ERP to adjust like the timetable for your like employees who are, who you might need more people to help. Because in the last year, at this time of the day, there was a lot more traffic coming in from like a holiday or something like that. And so like, you know, those restaurant groups are probably not like tech companies that are super, you know, familiar with all the tools that are available. And so I think like, you know, living in San Francisco, we see a lot of thinking, wow, like the labs can do everything, the codecs, and like, you know, why don't they just install codecs and just have it do stuff. But like for the average American and the average small, medium business owner, the average enterprise, even understanding what the tools are capable is one, connecting tools in a way that makes sense is two, deploying it is three, servicing is four. And so there's still a lot, in my opinion, where if there are complex workflows that require a lot of disparate software, computer use is a great way to join them, complete the entire intent or flow, and then building it in a format and UI that is feels good to hold for those customers feels still like a really, really big opportunity that, you know, we I think is still open. Tell us about what's next. What are you excited about that's on the frontier that you can share around around computer using agents? Well, I've sort of gestured towards it already, but we're really excited about speed. We're really excited about making computer use super human. And we're really excited about things that blend together different kinds of interfaces that bring together sort of multimodal inputs and outputs so you can have more kind of fluid experiences with your computer. I think right now a lot of our computer use product is very background focused. It's very much like, let me give the agent a task so it can do that while I do something else, which is actually a tremendous strength, right, of the product. But I think that in the future we'll also see more of this like real time collaboration between you and the agent. So I'm really excited about this area and and other otherwise just sort of better, cheaper, more, more available, easier to use, more. Yeah, exactly. Being able to actually access everything. Yeah, I think I think there is, you know, with Dots, right, we're giving folks these like computer in the cloud, chat GP work also has a browser in the cloud. I think, honestly, like for for me, like one thing I'm excited about is just making the experience feel a little bit more coherent. Like, you know, today you can install the Chrome extension and that uses your like password manager and everything in Chrome. And then we have an in-app browser in the desktop app, which has a different set of cookies and different passwords. Your computer in the cloud also has different things. And for the average consumer, it's confusing. It's like, why do I have these four different browsers that all don't talk to each other? If I open up a tab in one, does it like show up in the other? And so, like, I think a lot of what I'm excited about is how we make computer use go from this product that is used by like enterprises and like really hardcore tip of the spear kind of users to something that's actually understandable for people. It's kind of like an oxymoron, because I think computers is actually the most easy to understand AI products because it's an AI using a computer. But then because of the like complexities of like the different like states you have to manage and the different places you're using it, the implementation details sometimes seep through and then it becomes kind of hard to grok. And so being able to kind of make that consumer grade and easier to understand, more coherent is something I'm really excited about for next quarter. And I think it comes back to the point that there is still a human that's setting off the agent. Right. And they have to the better the UI and controls and the easier and more enjoyable it is to use, the more they're going to get out of it. And that's absolutely on that point. One other thing I want to gesture towards is I think another challenge of the product today is you you have to put some thought into how you're going to use it and you have to remember to use it and you have to convince yourself to do things that are different from how you're used to doing them. And I do think dots like James was talking about earlier have this really powerful proactivity where they can sort of come to you and identify situations and and help you solve problems. I think we'll also see a lot more proactivity. I'm really excited about a world where the agent can sort of adapt to you instead of you adapting to it. And I think that we're going to see a lot of that. And I think it will make it a lot easier for people to actually like relate to and and understand and incorporate this technology while also still being the one sort of setting and steering the goals, if that makes sense. One of the things we haven't talked about yet is the momentum behind computer use and how much has changed. Fabrizio and I chatted a little bit about this in our piece. We'd love for you to just kind of talk about where it's, you know, how it's the trajectories so far and where things are at it. Yeah. I mean, James and I have been working on computer use for some time now at OpenAI and it's been an incredibly exciting past like six to nine months. Because we started working on these computer use products and very few people were using them. The models, all of a sudden it felt to me, became great at these kinds of tasks earlier this year because of amazing work the research team did. And then we've been working super hard with a bunch of teams internally on how to create actually good product experiences around this. And then just in the past couple of months, all of a sudden, you know, the product's growing like crazy and people are really seeing the value in it. But I think it's still so early, you know, it's still the case that only some portion of our user base is using computer use. And I think we're going to see it grow quite a bit. Yeah, totally. I mean, you know, I joined OpenAI in January and I joined working on the Atlas product and love Atlas, but I think, you know, one of the things that we realized was in the Atlas product, the agent experience, I think for a few reasons, wasn't actually like a big selling point. Not many people used it, partly because I think most people treat it like a normal browser. And over the last few months, because the model capabilities have gotten better, we've now incorporated browser and browser use into kind of all of our existing products where there is already agentic usage. We've just seen, you know, it's like YC level of growth week over week. And then I'm always surprised by how fast it can go. And I think this next like big inflection point is like cloud, right? Like how do we like desktop is still very much a power user thing. I know we probably use our desktop all the time, but like most consumers are on their phones. And so how do you give every single Chachapiti user, how to give everybody who's just using their phone for their day to day life, a powerful computer that they don't even need to understand how to use, that it just kind of makes things work for them. So I'm really excited about how that kind of brings it to, you know, building users. It feels like computer using agents have had a ton of momentum behind them. And now it's the moment of like, OK, how do we get it into more people's hands? They trust it. They feel like they can really use it. And to your experience, your point earlier about coherent experiences, like that's that's a real, I think once as that gets a lot better, I think consumers will feel more comfortable using it and it'll feel more natural. I think that's right. I think I think we're at the scaling moment of computer use, both for consumer and enterprise. Yeah. At the same time. And I'm really excited to see where it goes and what people build with it. Nice. Us too. All right. Well, thank you so much for joining us. This was awesome. We love talking about this topic and it was really fun to hear it from you guys directly. Thank you. A lot of fun as well. Thank you. Thank you, guys. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. 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