OpenAI’s Dots lead on the future of ChatGPT

Okay, Alex, I want to go deep on dots, but first, I want to just talk about this moment where it is an industry. It's something I've been thinking about a lot, and there's so many new products that are constantly popping up, which is this rise of personal agents. And it feels like it's a combination of a lot of things, the models, getting capable enough to really power these experiences, things like browser use, computer use, really taking off, people figuring out the harness. And now you guys have dots, which is the OpenAI bet on this form factor. I would be curious to hear you like bigger picture, maybe talk about why this moment is happening now. Totally. And yeah, I agree this is a massive moment. I joined OpenAI a little over two years ago, and I've basically been on a mission to ship dots since I joined, and so I'm like thrilled that it's happening. So, you know, recapping quickly, we had chat, right? That chat was like, it's very obvious today, but it was not obvious that that would be the right way for many people to get value from LLMs when we started. The way of after chat was coding agents, and then eventually those agents useful for knowledge work. And the big improvement from chat to coding agents was like, hey, then now these agents can do things, right? But they were still this like fundamental problem, which is I think the main problem we're solving with this next era, you know, the third chapter, active intelligence, we call it. And that's that even if you have an incredibly powerful coding agent, it's kind of like you have this incredible principal engineer who refuses to check Slack, refuses to check linear, and basically only does things when they're told by you. And if you're a power user, you can set up automations and kind of get them going more independently. But for a sort of normal user, that doesn't happen. And so for me, this is happening now because we're finally ready to provide intelligence in a way that's much more intuitive for people in a way that you don't have to put all the onus on yourself to get value from the product, but it can start proposing to you what it's going to do and how it's going to help. So that's the problem. And then, you know, we can get more to the reasons, but I think broadly it is the model capabilities are there. And I think that maybe, so I agree with you on what you said, maybe the thing you didn't mention that I think is underrated, is there's actually a lot of human change that needs to happen for these products to get adopted. You know, like if you look at like a company or a team adopting coding agents, day zero, when they don't know how to use them and they're not connected to any tools to like day 400, the agents are connected to a bunch of tools. They have access to context. People have best practices. I think that's the other major thing we needed, right? Because if you think of a proactive agent, if it doesn't have access to context, it can't actually be proactive in helping you. It's like a go on, but yeah, the connectors and all that stuff in a place where people are bringing their data into these experiences. Yeah, that makes sense. You said you joined and it's been your mission to ship dots ever since. Expand on that a little bit. I mean, were you all envisioning this form factor for this two years ago? I'll just speak for myself here, but I had this broad idea that chat was incredibly powerful and AI was going to transform the world. And it was really important, like the mission of opening up really resonates with me. That's why I joined, right? Deliver the benefits of AI to all humanity. And okay, I'm not necessarily going to be the person training the model, but I did feel with chat that they were so much low-hanging fruit on how to make that model more useful. And it was those two things I mentioned, let it do things and let me not have to figure out what to ask it to do. Because even today, I go on Twitter and I see someone talking about all the loops they're running and I feel a little bad about myself personally. I'm not an advanced practitioner. I'm not running enough loops. I'm pretty sure you are in advance. No, I am, but even I feel that way, and I don't know how do you feel. Oh my gosh, I'm overwhelmed every time I open it, I'm like, wow, 10 new things. Yeah, and it's like it's rough, right? What product categories exist that the better you get at it, the more aware you are of your own personal shortcomings? Yeah. Maybe like sports hobbies, maybe? I don't know. That's like, it's kind of rough, right? Yeah. So I broadly, I wanted to figure out how we could solve those things. And it was kind of interesting. We were working on chat. I remember the early days, I was working on the desktop app. We were trying to build that into a contextual assistant. And yeah, the models were not there in certain ways. Well, actually, fun story. I joined OpenAI through an acquisition of a small startup. And we had signed the deal the day before that OpenAI launched like their voice product. Actually, I was called just voice, you know, that draw dropping demo like a few years ago. And we, so we signed the deal the night before and then we got on a call with our team, watched that demo live. We didn't know that they were launching voice. It was a draw dropping moment and then we said to the team like, hey, we're joining that company. They just acquired us. That was wild. And so at the time, we thought that like multi-modal progress was going to be it. And that's how we were going to have the next wave of capabilities. And that was kind of the approach we took like the desktop app, chat. We did stuff. I would like screenshot your apps and try to do working it. But it was really slow. It didn't work super well. And it turns out like coding was actually that next frontier. And so we had a year of progress on coding. And I think, you know, with coding, we ended up building a lot of tech for helping agents do more. And we built out all these connectors in a more easy way than having the agent like click around on your computer and stuff. We also developed ways to like hoist the agent to the cloud. All these like base primitives that needed to be in place for us to ship these persistent agents. And now it's funny because we're kind of having a comeback of computer use now, right? Where now the models are so good at coding. And they're also good at using code using code to control the computer. Like if you get Astra to control the browser or computer, it might actually write code snippets to do it because that's faster than taking screenshots and clicking. Yeah. But yeah, now we're in back at this comeback of like, well, the models can do anything that you can do on a computer. This episode is brought to you by Mercury, AI native banking that's loved by more than 300,000 entrepreneurs, including me. Visit mercury.com to learn more. Mercury is a fintech, not a bank. Check the show notes for details. This episode is also brought to you by Jira by Atlassian, where teams and agents get the context, coordination, and control to move work forward. Try it free at Jira.com. That's J-I-R-A.com. Thanks also to granola, the AI notepad for people in back-to-back meetings. It works everywhere you do and lets you focus on what matters. Try to granola.ai/sources and use the code sources for three months off. So take me into the last few months building dots. We talked about the build up of how these things have progressed. When did you guys go, oh, we have to now, we're doing this. We're building this. This is the next big swing. If we think about this a little bit abstractly, the key things about dots are first is a model that is trained to be persistent and to go after tasks sort of proactively. So initially over a long horizon, maybe you could say something like, baby, sit this PR. The model, you didn't tell it exactly how to solve that problem, but it's like figuring it out. And then eventually even more open in a task, stay on top of the feedback channel. That's one key idea there, persistence. It's probably the most important idea. Some of the other ideas are it being available wherever you are so you can talk to it in any tool and it being able to do anything you can do because it has access to its own computer, not yours. So go back to that first idea of persistence. We've been working on this for actually a really long time and it's been not like an instantaneous step function improvement in capability, but it's been sort of like the models have been getting better and better at it and the product has also been slowly getting better at it. So for example, slash goal is a fan favorite feature in codex that enables the model to sort of go after a problem for a really long time. And so we were shipping things like slash goal, we were shipping internal prototypes of things that enabled you to have agents or threads that would just go for a really long time. And we just started seeing this like really explode across the company to the point where there was just like a channel of feedback around one of these internal prototypes that was just like constantly active, people sharing like all sorts of cool ways that they were like, meaningfully accelerating themselves. And so we were looking at that. And I remember we had a really fun debate, which was should we ship this prototype that works locally on your computer. So it was kind of like slash goal, like it didn't have its own computer. It would just like kind of create this agent and go after things. And so we asked ourselves, should we ship this locally on your computer or should we wait till we have a cloud computer, which is a harder thing to build. And should we ship this as just an enhancement to a codex thread? So it's just like you're just using codex, it's just like the thread can do more. Or should we embody this as an agent that has its own identity that you can reach in Slack, et cetera, et cetera. And so we kind of went through this narrative process, shipping with the company, seeing what we saw like exciting use cases, et cetera. And we ended up deciding, okay, for us internally, we can get a lot of internal acceleration from this local only non-embodied product. But the most intuitive form factor to help everyone benefit by asking this agent to do really hard tasks and long tasks would be, let's put it in the cloud and let's embody it, embodying mean, like let's give it a name, let's give it an identity, et cetera. And so we kind of then started a sprint to like get those two things, those two sort of tweaks to the form factor built around this base model capability with Aster. I'm curious about the decision to embody it and the identities that you can give it and the customization. Why give that level of control to the user? Totally. So it's a really good question. And to be completely honest to you, I think we made the right choice. But I also think that we together, like the industry are going to discover the answers to some of these questions, like should agents be embodied? How many should people have? Is it different between consumer and enterprise? Like we have our takes, like I can give you my takes, but we're going to figure that out. I think embodiment, though, to answer your question, it's a really powerful analogy that helps people understand how to use the product. Two main things. First, I think that like really smart, like power users of tools realize like advanced ways they can use tools. But a normal user is going to look at something that's not embodying kind of just ask an agent to do something like now one time. But the moment that you have this like entity that shows up in like, I don't know, Slack, it's just something clicks in people's brains and they start realizing like, oh, I can ask this for things that take days or that aren't continuous work. that maybe it's like stay on top of this product and let me know. And so people start giving it much harder tasks, and that was really important to us. We wanted to have really good tasks. The other thing that is really powerful with embodiment is it helps people understand that you are talking to one context, no matter where you're talking to it. So for example, the way that I work, when I'm commuting, I'm often on a call with my dot. I spend a lot of my day in Slack because I'm on the product team, so I'm slacking it across many different threads. Sometimes I'm forwarding DMs to it, sometimes I'm in threads, and then sometimes I'm in the app talking to it. But no matter where I'm talking to it, it's one context and it knows about all the conversations I've had. And there are a few ways you could try to teach users that. One is embodiment. The other is you could try to synchronize threads across surfaces, but that, to me, doesn't really make sense. If you and I have a conversation now, like live in this room, and then I text you later, it's not like there's going to be a transcript of our conversation in the text. But you still know it's me. Because I'm an entity. Well, and it makes sense, too, when you look at the product evolution of chat, GPT, you guys have been the merge. You guys have been trying to unify, work, chat, everything together. But there are still silos. And even until recently, voice mode didn't have the connectors access that the work and chat has it. So one of my routines is like, I take my dogs for walks in the morning in the evening, and I'll do work calls, and I'll talk to AI, and all that stuff. And I've been on the early beta for DOT the last few weeks, trying it on these walks. And it's amazing to have all that context and my connectors finally in one place together. And I do think embodying it makes sense in that context. But I'm also curious, just like, yeah, do you think the world we're going towards is everyone has their single agent that's like their personal agent, and that agent then delegates to a bunch of subagents, or people have an agent for different slivers of their life. How do you think that works out? So it's like the way I kind of think of that question is like, what is the interface that we're exposing to whom? And so to agents, well, parking with that, to agents, I think there's a different answer to that question. But to humans, there's only so many people that I want to remember the names of. And that's just because I have bad memory. And now, if each of those people have eight agents and they all have whimsical names, I don't know what's the sport you like-- I play Ultimate Frisbee. OK, so there are different kinds of Frisbee's. Imagine you had five agents, and they're all named different kinds of Frisbee brands. They're like, how am I going to remember that? And one of them is for GTM, look at the market, and one of them is for like, if I want to meet you for dinner. And it's just, it's hard for me. Maybe it's OK for you, but it's hard for me. And we see the future of these agents is clearly like people and agents working together to help empower us in advance our lives. But if that comes with a bunch of cognitive load of me knowing what all your agents are called and who they are, it's terrible, right? So our take on this right now is that we should support people having multiple agents, but two things. First, for a consumer, a normal person, like you probably just want one agent. And it's only like a power user might create multiple agents. We can talk about why. So most people will just have one. And that agent should feel like an extension of them. So for example, a decision we've made in product is if you put your agent in Slack or in Teams, it actually is prefix. The name of it is prefix with your name. So like my Slack handle is AE. And so my agent, if I give it a name, I just call my dot, because I like the name dot. So my agent is just AE-dot. If you call your agent Alice, your agent would be called AE-allus. And so this was not something that we thought of and were correct on. This is actually something we discovered as we were building, because we accidentally let everyone create many agents with random names. And we even encouraged people to give them fun names. Then no one had any idea what was going on in Slack. And it just became really difficult. And the open-ass Slack is already pretty active. Yes. And that's us working together, right? Like you and I aren't in the same Slack. Let's say we're texting. I was in there like, there's no way I'm going to know your agent's name. Right, right. So yeah, that's how I take that. So why this interface, though, where it's kind of one master single thread? Because I think people are used to different chat threads in bouncing around. I think there's Mews, there's Instinct, there's other products that have this kind of interface that everyone seems to be landing on, but why is this the interface? Something we believe in that Sam talks about is that the human world is shaped around humans. And so a lot of the tools we have were shaped around us. And so what I find very fun about this is that as soon as you start, say, designing software to be embodied or in some future building robots that are humanoid, you start to get to reuse a lot of really excellent design thinking that has already been done elsewhere in the world. So if we look at consumer, and we look at enterprise, and we look at how we talk to people, obviously in both places we can talk in the room at the same time, we could also message, and that's the center of gravity for how we talk to most people. Messaging products feel very different. I message is what you use in personal life. It's mono-threaded per person. And then at work, you have Slack or Teams, and that has really heavy threading primitives. It has channels for context separation. Those channels within them have threads with advanced controls. And I think there's a reason that there's been kind of this convergent evolution. What's up is kind of like iMessage, right? It's kind of like other texting tools. So my take is we can kind of learn from that. So we decided to make the interface for your doc really simple, like you're just texting with another person. I think over time, we're going to create more ways to add work, segment the context. Actually, space and dots, we call it dots and space, kind of fun name. Those work really well together. And so having different spaces to collaborate with your dot in is a great way to encapsulate different contexts. But since you mentioned it, can you explain space and kind of how that fits into the product roadmap? Because that's a big new thing as well. Totally. Yeah, it's massive, and I'm really excited about it. So yeah, maybe I'll go back to that point I made, right? Like, there's a lot of really excellent design work that is being done generally by other people. And one of those things is if you're trying to collaborate with someone, there are these media, right, for it. There's in part-- there's three, maybe four. The three main ones are live, messaging, and then documents, right? If you include slides as documents, right? And they have very different purposes, and all of us know intuitively when to reach free, which. I mean, sometimes you might disagree with your team. Like, oh, I wish you would have slapped me. You didn't need to send me a doc link. But generally speaking, we kind of know all know or doing. The fourth is maybe functional tooling, right? So like you're in Salesforce or you're in Figma or something, and so you're doing a specific workflow. If you're building something that is messaging, and you want it to really feel like messaging, you have to start to be disciplined about the form factor. So if you compare talking to your dot to talking to ChatGPT, the dots form factor has like actual message bubbles. And if you were to put a very long response in one of those message bubbles, it doesn't feel as good as it might feel in a long form generated ChatGPT response, which also may not feel as good as like an actual document. And so what we wanted to do is we kind of hard pivoted the design towards messaging. And then you're like, well, I need somewhere to list long form content, right? If my dot brainstorms like 12 ways it can help me, and it sends me 12 texts, I don't know if I'm thrilled, right? But if it generates a space for me, like a page in a space with a checklist, I'm actually pretty happy. So these things kind of fit well together and allow each to be more opinionated. And so sorry, this was an incredibly long-witted answer I got, but basically space is a collaboration. I'm like laughing at my own like, wow, you didn't answer this question at all. So basically space, you could come you off, finally. So space is a collaboration workspace that is built to be-- I wouldn't say agent first, but it's built for sort of equal weight agent and human collaboration. And so there's a lot of really cool things that you can do with that. For example, one thing that I do as, you know, I'm on the product team is I'll often write at a doc. And you know, I'm stubbing out content. And often like teammates would go do something. So I have to action that. I have to go ping them. Like I'm like in Google Docs, and I like write something, and then I'll command tab into Slack. I'm like, hey, can you go get this Google Doc link? I'm like, fill this thing in. So now what I can do is I can just be tagging my dot. Continuously in there. I'd be like, hey, flush this out. Hey, pull that data. Hey, go ping this person, and ask him if this is done. And if it's done, check this box. And you can get into really deep flow with agents. Another really cool thing you can do on a page is you can set agents instructions. It's kind of like an agents.md for a repo, except it's on a page. And then an agent can just like keep that doc up to date. And perhaps the third and last thing I'll tell you about it that I really like is like, if you look at documents as a format, historically, they have had a very hard constraint, which is that they needed to be optimized for human authoring. Like if I could build you the awesomeness document editor in the world, but if it's hard to type in it, you're not going to use it, because you have to type in it pre-AI. Interestingly, we now have this thing called HTML, which personally, I don't find, at least modern HTML, and CSI, I don't find to be very friendly for human authoring, but it's much more expressive than just like a plain document format. And agents happen to be very happy in HTML. And so space like naturally integrates like visualizations, HTML visualizations. So a lot of the pages that we have internally at OpenEI, like when we're discussing metrics, we'll probably have like a live chart that an agent is keeping up to date, that you can click into and ask the agent to customize. Or we'll have a prototype. Like the way we'll look at a design decision now is we might have clickable prototypes like literally in the page. So when you start to think like, hey, we're building agents, and we're going to talk to them in a messaging interface. So we need a sort of corresponding document interface. And that document interface can be agent first. There's a lot of magic kind of comes together. One of the biggest pains I've had running sources has been managing my books. I'm not an accountant. And all of the software out there is unintuitive, clunky, and detached from where my transactions actually live until now. Mercury recently launched Mercury Books. 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Learn more at Jira.com. That's J-I-R-A.com. I spend a lot of time context switching between meetings. Often with no time to process one before the next starts. Thankfully, Grinola runs in the background the whole time. It's an easy-to-use AI notepad for meetings that works everywhere, even on phone calls. I use Grinola to recall what was set in meetings and create helpful summaries. I use it every day to stay on top of what I need to get done with my team. It connects to my email and suggests follow-ups for me to quickly review and send, saving me valuable time. Grinola isn't just a core part of my workflow. It's basically my second brain. Try Grinola at Grinola.ai/Sources and use the promo code "Sources" for three months off. Framer is the AI website builder that powers the sources podcast website at podcast.sources.news. Framer brings AI agents into the same canvas where your website is designed, managed, and published, so you can move faster without giving up your taste or control. I use Framer to make the sources podcast website be the destination for everywhere you can find the show, plus you can also see recent issues of my newsletter. Start building with agents for free today at framer.com/sources for 30% off a framer pro annual plan. Framer.com/sources rules and restrictions may apply. What do you think if you were to boil it down sets dot apart from news, rock, bot, instincts, this new form factor that everyone's really battling on right now? Two things. First, most capable agent. Second, safest and most trustworthy agent. Those are the main two. We actually was really clarifying when we decided those would be our two. A lot of us to make a lot of strong decisions, in my opinion, and you've been in the alpha, and I'm sure you've tested it for things that you were using other products for, and notice where it was good, and notice where you weren't. It was very aggressive on permissioning, and I think that that was you guys are ironing out kinks, but I think it was very. Are you sure you want to make this purchase? I think there's a balance between how proactive and how much agency do you want it to actually have, and is that a trust that's earned over time? Is this something you give immediately? Do you do an sliding scale based on what the user says they want? I imagine it's pretty hard to build into the products, and I think you guys seemed like early you were going very heavy on permissioning, and maybe you're finding more of that balance right now. Yeah, so the way that I think about it is, okay, we're building the most capable and trustworthy agent. We want people to give it really hard tasks, and really hard tasks probably come with a lot of responsibility, depending on the nature of the task. We're talking like landing PRs, or evaluating hardware, or planning a launch. We could talk about why it's the most capable assistant, but bringing this to your point about safety, we then decided, okay, we have lots of dreams and ambitions for how this thing will work in the future, but to start, we're going to be conservative. We made trade-offs in terms of capabilities, we made trade-offs in terms of product, and we made trade-offs in terms of rollout, to have this most trustworthy agent, and then we'll slowly widen those over time. So, for instance, for various reasons, you could debate which model you might want to launch with, but we decided to launch with our most capable, but also our most aligned model, which is GPT-Sex Astra. Which I want to talk about, but the safety piece, talking more about that. So that's the model. Next, there are many features that we want, and many of our users want. So, for example, once you give your agent its own identity, you can say Slack, or we were testing email, for example, you immediately want it to email everyone or Slack everyone, and to be emailable by everyone. That's an obvious feature we want. We didn't ship with that feature. When you give your agent its own identity in Slack, it only responds when you tag it, unless you explicitly tell it, "Hey, listen to other people, too." This is because we think that we definitely want it to be multiplayer, but we want to be there getting there very carefully. The moment your agent is answering unsolicited questions from other people, you need to be super thoughtful about what is the boundary of what a can share or not. That's a little bit on product. We could talk a lot more about the controls in product. Maybe the last thing I'll say is rollout-wise. Very intentionally for us. We're starting with pro, business and enterprise, business premium, that is. So that is our most AI-literate audience. You could talk about going much broader, much faster, but for us, we want to do this really carefully. Go to that audience, see how things go, tune, etc, and then go to a broader audience. I say an accessibility I want to get to, but on the safety side, especially when people are reading about hacking and rogue agents and all these things, the SAM and others, and I just did the pod with them about that about a month ago. The alignment stuff you guys are doing on the frontier, but people are rightfully concerned about are these agents hacking? Are they going to, and maybe being reticent to hand over their personal data? How do you guys approach credit cards, personal information, that kind of stuff? Is it fully sandboxed? How does the architecture relate to the rest of chat? Personally, I'm very happy that there's this conversation just in the market generally about the level of responsibility that people are giving agents. I think it plays well because we are taking that so seriously in this product. One thing I do want to call out, those incidents that we disclosed were done with models that were not intended to be shipped internally, and we're not running in the same level of sandboxing or the safeguards that are in the product. So those models are very different, like GP6, Astra is an incredibly well-aligned model, and then there's many, many layers of safety and defense and depth built into the product. To talk through the architecture and how that works, the way that I would think about it is you start with the model, very well aligned, you could talk to your girl about that, you then run that model in a harness, and we are running our model in the Codex harness, which is a very well-tested harness, which we have many evaluations for, including evaluations from a safety perspective on how that model performs in the harness. We then modified that harness to enable the product to have this persistent agent behavior, for example, that means things like running it for really long periods of time, it means things like it delegates to sub-agents and so forth. So we then actually redesign that harness to make sure it's safe and re-evaluated things or tests in that harness. So for example, when you have this harness, you have this persistent agent, and like I said, we were experimenting with email, and so we evaluated, hey, if you email it, can you ex-fill data from it? And so we ran those evals, and we found that we had a model try to hack it, you know, prompt inject it, and no successful prompt injections there. So that was great. But we so we basically do all that all the way at the harness level. Then in the product itself, there's kind of two sides to it, or maybe three. The first is the default behavior and rules that you can customize. The second is how we enforce those rules, and then the third is how we give you visibility on what happened, you know, with all those systems running. So to talk about the model behavior, we have some very conservative defaults that you noticed. By the way, those are those defaults that you tested early on. We started with a very conservative policy, and then we whittled away at it with user feedback. So, you know, expect to accept the terms of service of the retailer before you buy the shoe, like that kind of-- Yeah, I got it. It's not a shoe. Thank you for being an early test. We really appreciate it. Thanks to you, Testiga, some folks in the room don't have to deal with all of the same things. But you know, basically every time we got feedback, like the one you shared, we would like go discuss that, and we're like, okay, how do we want the model to behave here? And so we really toned down, we call those over-confirmations, when like it really is like you didn't need to confirm that, you could do it if felt comfortable. So we have this default behavior, and the way the model is instructed to behave is that it can-- it does proactive research for you over the tools that you've connected it to, and it's thinking about how to help you. But it doesn't do anything to help you without you confirming first. So for example, if it noticed that there was a conflict on your calendar, it's not just going to move the meeting. It's going to say, hey, I noticed this is a conflict, do you want me to move the meeting, right? Or if someone's asking me a question, and it knows the answer already, because it has all my context, it's not going to send the answer, it's going to ask me if I want to send it. So you can then prompt it to give it more instructions, and like, hey, next time, if I'm on PTO, like on a holiday, like you can tell people that I'm on holiday, if they're also work colleagues or something. And then we also have a layer of custom rules that you can set, that you can go look at in the UI. So all of this is like the model trained to follow your instructions, but then we add a layer on top of that, which is, well, what if the model doesn't actually follow instructions. Well, we have have a layer called AutoReview, which is another model that's observing and kind of like policing the base model. And so this is a layer that is aware of your rules, the custom instructions, and all that, and just make sure everything goes well. And then-- so that's the controls. And then finally, we have this auditability, which is anything you're doing in the product or the model is doing the product, you can go see the subagents that it's running and exactly what the subagents are up to. You've teased email as a thing that may be coming. When can this be in iMessage? What's up, is it going to be contained to chat? We definitely want it everywhere that you work. So yeah, we're intending-- or everywhere that you communicate, going beyond work to consumer as well. So we have texting coming soon. It's a story for a fun time about the journey to enable that. OK. But I can't share more now other than that it's coming soon. OK. The web part of this is something that is maybe under-discussed as the technology is moving so fast. But Amazon got a lot of headlines for blocking news recently because people were using news to buy things. Then there's companies like Shopify, which seem to be leaning in. They're partnering with a bunch of companies on this space. And I think everyone's kind of waiting to see what happens. How do the sites and the companies that people use to buy things need to adapt or not to the rise of agents like this? I'd be curious to hear you talk about that. And then how you guys are approaching this from a partnership's perspective? Are you just kind of going in and saying like, we're going to let people do this? And then we'll figure out a partnership. Are you saying no? Like, you can't crawl this until we do a partnership? How are you thinking about that? I think we're going to discover together with the ecosystem how this should work, how all the shopping and payments should work. And there are different approaches you could take. You could just go and be a bit of a pirate. Yeah. There's a way to do things. We are taking a very partnership and ecosystem first approach. You know, a lot of the announcements today at DevDay were really all about the ecosystem. And so we're doing the same. For instance, WebBotOff is something that we work really closely with CloudFlare on. So we actually are agent declares explicitly that it's a bot. And so it's something we're actually thinking about where in discussions with many partners for how to enable this. But we don't want to take the approach of just saying like, OK, we're just going to go out and like fear what happens. We kind of want to be measured in our approach with customers. And we think this works for us. Also, you know, this is a little bit why we steered towards going for productive use cases for people at work, because this is where we think like, there's a lot of demand for the power of AI there. And you know, there's a lot of really well-aligned incentives for people trying to get things done together. So we're kind of hoping to land with this like incredibly capable assistant for people trying to get ambitious tasks done and then broaden access and capability from there. The only place I could see incentives maybe not being aligned is our companies that are engagement based or have huge advertising businesses that rely on humans seeing those ads. And now agents are all of a sudden crawling their surfaces and doing things on behalf of humans who never see the ads. And that feels, I mean, a lot of the web is supported by advertising. I don't think anyone in the industry, not just you guys, has the answer yet for how. And I'm be curious to-- even if you're just kind of brainstorming, like, do you have any idea for how this is going to net out? Well, this is why I was saying actually I was saying on the other side, like if we're using-- if we're trying to get things done together, the example of the hardware engineer looking at factory defects, that's where I feel like all that tool chain that that engineer is using, I think, is very well aligned to having an age work in there. But have you thought about the advertising piece like companies that they monetize based on people, human beings looking at it and spending time with it, and now their agency are maybe going to go do that? Yeah, I'm not sure how it's going to play out. But I think broadly, at least my opinion here, is that those companies are providing a service. And if previously they were monetizing with ads, we either need to make sure that they continue being rewarded for the service with ads, or we need to provide some alternate means of compensation for them. I don't think there's any scenario where they don't have that. Yeah. Let's talk about pricing and the fact that you all are putting Astra in this, which is-- it's great for everyone. You're not also curving from wrong. You're not counting.usage towards plan limits, is that right? That can't be a permanent thing. Is this just a temporary to get people excited? No, I'm excited. That is permanent. Really? But so this is really fun to talk about. So we're going to try pricing for dots in a new way where you don't run out of rate limits. Here's what I mean. So we have to unpack this a little bit, because it's a bit of a new idea. Today, if you're using codex and you consume all your rate limits, and then you try to ask codex to do more stuff, it'll just stop, because you're out of rate limits. But just when we think about this from a product perspective, I mean, first of all, it just sucks to not be able to use more. But secondly, when this is a persistent agent that you're counting on to have your back, you're telling you things like, hey, I don't know. Stay on top of my flight and order me an Uber to the airport when it's time, based on the traffic or something. Or like, some tasks like that, it's like, you're relying on it to have your back in the future. And then you accidentally code too much the night before, and it doesn't do its job. We don't want that, right? And so we kind of had to go a little bit to the drawing board and thinking about how pricing should work and come up with some pretty basic principles. So one of those principles is that your dodge will be available 24/7. And there should be no way for it to not be available 24/7. I mean, barring abuse or something. Another principle should be now that it's available 24/7, it should always respond to you quickly. So I started liking the way that we want to price do it to a bit of a more like a contractor. Or like, if you have a contractor who's very responsive and very polite, they will always answer your messages. Now, if you ask them to do work, there's sort of a varied amount of work that they're going to do for you based on how you're compensating them, but they're always available to you, right? And if they, let's say they, I don't know, they set up this beautiful installation behind us. And then you have a question about it. You can ask them for that. And they don't have to charge you by the hour to answer how they did this, right? That's just the question you can ask. So this is kind of how we want to price. We want your dodge is available 24/7. You play a flat monthly amount to have access to your dodge. Maybe you can pay more, another flat monthly amount if you want like a more powerful dog running on a more powerful computer, or with more powerful models, or whatever, or running faster, or able to do more parallel work. But at the end of the day, you pay this flat amount. It's kind of like buying internet, so I have more analogies. As you can tell, I'm still figuring out how to talk about this. And it does sort of a varied amount of work for you based on how much you're paying. But it's never not available. And as maybe, even as you approach your limits, it can start telling you like, hey, I've done a lot of work for you. So I'm going to start using a more cost-efficient model. Or maybe is it OK for you if I do this work overnight? Because if I do it overnight, it's incredibly affordable. Is this urgent? And it'll kind of have a sense of whether or not that's urgent. On the other hand, maybe if you haven't used it at all that month, it might text you and say, hey, you're not utilizing me fully. Here are some things I could do for you. Some of the thoughts on how we're going to set this up, the way we're doing pricing is it's going to be long-term. It's going to be a separate bucket from your main code exclamation because we're doing this very new thing with it. For the next month, we just have very generous limits. And this is going to be as we build out and sort of measure and tune this new setup where you have a model that is aware of how much work it can do for you, but it's always available for you. Why not just do a take rate business. If you believe that people will use this to buy things and make transactions at scale, it just take a small big on all activity that flows through. I mean, I think that might make sense for some other business models where you're betting on transactions, being one of the main things that you're doing. But like I said, we're not really betting on that. We're betting on people doing really ambitious work with this tool. And so they're not necessarily buying anything when they're doing that ambitious work. OK, well, on that note, the bet that you're making, that brings me to probably my biggest question I've been thinking about since I started testing this. And certainly, since the keynote this morning, is what does this mean for chat? You guys have 1.2 billion weekly users of chat, GPT now. And you've got the work tab, and chat, obviously, still is massive. How do you see these things co-existing as dot the future interface of all of it? Chat is definitely here to stay. So just for anyone who's not following, chat, you know, fastest growing consumer assistant, you know, over 1.2 billion users. And then we have our agent products, Codex and Work, which have 35 million users, and also growing. And now we're just launching dot, right? And so the way that I look at it is like, chat is the consumer assistant for everyone. Right now, we have this different product called Work, which really we should just merge with chat. So that will happen. Codex is a product for developers, so great. And then we have dot, which is us in a way, like the way I think about it is like we're a little bit repeating the Codex playbook. If we're going to take this frontier capability, and we need to ship it in sort of a careful, conservative, and very powerful way, and then figure out how to bring that capability to everyone. So we're starting with dots as a sort of premium standalone product. It's starting with just pro. We need to go get it to plus, you know, after we've like figured out, you know, learn to how everyone's using it, the safety story, the scaling story, all that. And then we eventually need this capability to get all the way to free. And I think the way that we get all the way to free is actually we're baking this all into chat and making chat or chat to BT into just like an incredibly powerful consistency. Well, it's so much more capable. I mean, to me, this should be the interface for everyone, because it can do so much more-- Yeah, so you can expect a lot of the learnings from this to make their way into chat. But I don't think we want to ask 1.2 billion people to like change what they're doing or how they're using things. We just want it to just be upgraded. So you see it as more of a gradual thing? Yeah. time. Yeah. When you think about the road map over the next year for dot, like if we're talking a year from now, what do you think is the most meaningful change? Can I tell you a funny story? Yeah. And we can get back to the question. Yeah. Okay. So when I first joined OpenEI, it's through this acquisition. And we had a meeting and some of my team were in the room. And Greg was there. And so everyone, you know, it's just talked about how excited they were that we were all together. And then Greg asked, does anyone have any questions from the multi team, which is our startup? And everyone, no one has any questions. We're all a little nervous. And I was CEO of this company. So everyone looks at me. It's like, well, you have to ask the mandatory, you know, obligatory question. So I'm like, oh, what am I going to ask? Have nervous. And so I'm like, okay, in two years, what a success look like. And then Greg, I always remind you of the sea looks at me and he says, in two years, that's an incredibly long period of time. That's not the right duration to ask about. You should be asking about success in two months. Yeah. That's like, Oh, God, I'm already fired. Okay. Well, don't fire me. Okay. No, no, no, no, it's not good. But I'm so sorry. So, no, but I get the point like this, it moves so fast. Like six months ago, you probably couldn't imagine all of this. Yeah. So it is probably an unfair question. So what I can say, though, is if I think about what's next, we are approaching, shipping these capabilities incredibly carefully. And next, what I would like to do is to start looking at some of the things that we didn't want to do immediately and landing them. So for example, let's get some more users. Let's not let's get beyond pro. Also, for example, let's enable your dots to talk to other people and maybe even other dots. Right. We need to be super thoughtful and careful about how we approach that. We just shipped basically in my mind a reboot of codex cloud. I'm really excited about it because I worked on the first codex cloud. It's super powerful. Your dot also super powerfully can control your local codex. We just ship this space, all the interplay between those things right now, I think can be really sanded down and made super smooth. Yeah. So I want to do that as well. The other really interesting thing that we haven't talked about, I think yet, is specialist dots. Yes. Right. And so we have this idea of like there's basically like two things going on. We have assistant dots. That's everyone has an extension of themselves. And then separately, you know, if you're an organization and you have some function or workflow that you would like to have more like more automated, you may not want that being done by individuals who have their own personal thoughts taking on that work. You may want to actually like control that centrally. Right. And so you can create specialist dots, which specialize in doing a particular thing and which are centrally managed by IT, which have their own identity completely separate from any sort of individual user in which are accelerating an entire team or an entire organization. And so I think the other thing that I'm really excited about over the upcoming months is bringing specialist dots to life and entering this world where it's like we have us. We have our our personal assistant dots. And then we have assistant dots much more broadly deployed within enterprise. But that is another place where we're starting like very slow, very careful working with a handful of customers. Got it. Okay. Last question. What's the craziest thing your dot has done for you that you're like, wow, I used to happen in it blew your mind. The craziest story that I've heard, it was like on someone I was working with as we went up to this was this engineer woke up and realized that in Slack, he had received a bunch of kudos, which is like, you know, props, like awards for having like fixed a bug before it made its way to production. And the engineer is like, what happened? By the way, I tell the story with a little bit of reticence because that's actually not something that you would want to happen without the person knowing. And so you know, we're building a lot of things. It fills around that. This is why we do small scale. This is like early on when it was just our team testing it. And yeah, his dot, he'd asked his dots to stay on top of the feedback channel and the dot like saw the feedback and then identified an issue and then spun up a PR and you know, didn't set that PR in draft mode. So like, you know, dots should set PRs in draft mode and then set it to auto merge because that was that engineer's personal preference. And then, and you know, the dotted figure that out, right? I was like, oh, you always create your PRs directly. And you always set them to auto merge. And then someone else had approved a PR and it did it. So to me, that was, that was a really cool, cool moment for sure. Wow. Well, knock on wood. We all get to have moments like that. Thank you, Alex. Yeah. Thank you. Thanks for having me. Banking should feel like modern software. Get everything you need in one place. Visit mercury.com to learn more and apply online in minutes. Grinola is the best AI notepad I've tried. It works everywhere on a video or phone call in person or an Apple watch. Try it now. Grinola.ai/sources and use the promo code sources at checkout for three months off. Jira by lasting is where your team and your agents work from the same context. That's j-i-r-a.com. Framer is the AI native website builder that lets you build faster without giving up control. Visit framer.com/sources for 30% off. Rules and restrictions may apply. [BLANK_AUDIO]