OpenAI’s Dots lead on the future of ChatGPT

14:06Alex Heath:So why this interface though, where it's kind of one master single thread, because I think people are used to different chat threads and bouncing around. And I think, you know, there's muse, 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, the interface?

14:22Alexander Embiricos: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 you know 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 right so if we look at consumer and we look at enterprise and we look at like how we talk to people like obviously in both places we can talk in the room at the same time.

14:55Alexander Embiricos:We can also message and that's the center of gravity for how we talk to most people. Messaging products feel very different. Like iMessage is what you use in personal life. It's monothreaded per person. And then at work, you have Slack or Teams and that has like really heavy threading primitives. Like 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. Like WhatsApp is kind of like iMessage, right, is kind of like other texting tools. So my take is we can kind of learn from that.

15:27Alexander Embiricos:So we decided to make the interface for your dot really simple, like you're just texting with another person. I think over time, we're going to create more ways to at 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 like having different spaces to collaborate with your dot in is a great way to encapsulate different contexts.

15:48Alex Heath: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.

15:54Alexander Embiricos: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 three, maybe four. The three are, 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 for which. I mean, sometimes you might disagree with your teammate, like, oh, I wish you would have slacked me.

16:24Alexander Embiricos:You didn't need to send me a doc link. But, like, generally speaking, we kind of all know what we're doing. The fourth is maybe functional tooling, right? So, like, you're in Salesforce and 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, right? So if you compare talking to your dot to talking to ChatGPT, the dot's form factor has 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 an actual document.

17:02Alexander Embiricos: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. 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. 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 is an incredibly long-winded answer. But basically space is a collaboration. I'm like laughing at my head. I'm like, wow, you didn't answer this question at all.

17:34Alexander Embiricos:But yeah, so basically space, you can cut me off by the way. That's okay. 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 is, you know, I'm on the product team is often I'll write at a doc and, you know, I'm stubbing out content and often like teammates would go do something. So I have to to action that I have to go ping them. like I'm like in Google Docs and I'll like write something and then I'll command tab into Slack and like, hey, can you go get this Google Doc link and like fill this thing in?

18:09Alexander Embiricos:So now what I can do is I can just be tagging my dot continuously in there and be like, hey, flesh this out. Hey, pull that data. Hey, go ping this person and ask them if this is done. 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, right?

18:44Alexander Embiricos:Like if I could build you the awesomest document editor in the world, but if it's hard to type in it, you're not going to use it, right? 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 CSS, 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 OpenAI, 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.

19:19Alexander Embiricos: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.

19:40Alex Heath: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. It's AI-powered accounting that works with your Mercury banking and credit card transactions, plus external cards and payroll systems as well. You can choose between cash and accrual accounting and quickly generate reports for your cash flow, P &L, and balance sheet. Like everything Mercury does, the design of Mercury Books is super approachable and clean.

20:13Alex Heath:I finally don't feel overwhelmed when I'm trying to understand the big picture of my business. I love that AI does most of the heavy lifting, including for things like auto-categorizing transactions, and that Mercury's command AI agent can handle tasks end to end. You don't need to be paying for separate accounting software anymore. And you can even invite your accountant or CPA to work alongside your Mercury Books with you. This doesn't cost extra, and Mercury will even help you find a human accountant if you need one. Visit mercury.com slash books to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC-insured bank.

20:47Alex Heath:Banking services provided through Choice Financial Group and Column NA, members FDIC. AI is only as useful as the context it has. But when that context is scattered across tools, threads, and DMs, your team and your AI agents are flying blind. That's the problem Jira by Atlassian solves. What's the goal tied to your project? What got decided last week in Slack DMs? Atlassian's teamwork graph pulls all of the valuable pieces together, from Jira, Confluence, GitHub, Slack, and more, so nothing falls through the cracks. You get 44 % more accurate results with 48 % less token usage. With Jira, you can easily share your work context with the AI agents you already love, like Claude, Cursor, and GitHub Copilot.

21:28Alex Heath:Assign them work directly or connect your tools through MCP. All of this lets you spend less time digging through endless links and messages, chasing down what got decided and by who, and spend more time actually shipping. Learn more at jira.com. That's J-I-R-A dot com. I spend a lot of time context switching between meetings, often with no time to process one before the next starts. Thankfully, Granola 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 Granola to recall what was said in meetings and create helpful summaries.

22:02Alex Heath: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. Granola isn't just a core part of my workflow. It's basically my second brain. Try Granola at granola.ai slash 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.

22:36Alex Heath: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 slash sources for 30 % off a Framer Pro annual plan. Framer.com slash sources. Rules and restrictions may apply. What do you think if you were to boil it down sets apart from Muse, GrokBot, Instinct, this new form factor that everyone's really betting on right now?

23:07Alexander Embiricos:Two things. First, most capable agent. Second, safest and most trustworthy agent. Those are the main two. We actually, it was really clarifying when we decided that those would be our two. It allowed 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 noticed where it was good, and noticed where we weren't focused on.

23:27Alex Heath:Yeah, it was very aggressive on permissioning. And I think that was, you guys are ironing out kinks, but I think it was very much, are you sure you want to make this purchase? like not being, I think there's a balance between how, how proactive and, and how much agency do you want it to actually have? And is that a trust that's earned over time? Is it something you give immediately? Do you do it on a sliding scale based on what the user says they want? I imagine that's pretty hard to build into the product. And I think you guys seem like early, you were going very heavy on permissioning and maybe you're finding more of that balance right now.

24:02Alexander Embiricos:Yeah. So the way that I think about it is, okay, we know 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 right we're talking like landing prs or evaluating hardware or planning a launch um and so uh well we could talk about why you know it's the most capable assistant but bringing this to 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.

24:34Alexander Embiricos:And so 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 sort of like have this most trustworthy agent. And that will slowly widen those over time. So for instance, you know, 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-6 Astra.

24:56Alex Heath:Which I want to talk about, but on the safety piece, talk more about that.

24:59Alexander Embiricos:So that's the model side, right? 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 in 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, right? That's like an obvious feature we want. We didn't ship for 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, like listen to other people too, right? This is because we think that we definitely want it to be multiplayer, but we want to be there, getting there very carefully, right?

25:32Alexander Embiricos:Like the moment your agent is answering unsolicited questions from other people, you need to be super thoughtful about what is the boundary of what it can share or not. You know, 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. Yeah. Very intentionally for us, we're starting with pro business and enterprise, business premium that is. And so that is our most AI literate audience. And, you know, 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, et cetera, and then go to a broader audience.

26:03Alex Heath:Pricing and accessibility I want to get to, but on the safety side, I think, especially when people are reading about hacking and rogue agents and all these things, and you and the Sam and others, and I just did the pod with him about that about a month ago, like 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?

26:34Alexander Embiricos:Yeah, so 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 were not running in the same level of sandboxing or the safeguards that are in the product. Right, shipped externally. Yeah. So yeah, so those models are very different. You know, like GPT-6 Astra is an incredibly well-aligned model.

27:03Alexander Embiricos:And then there's many, many layers of safety and defense and depth built into the product. So yeah, like just 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 your ear off about that. You then run that model in a harness. And we're running our model in the Codex harness, which is like 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 sort of this persistent agent behavior.

27:33Alexander Embiricos:You know, for example, that means things like running it for really long periods of time. It means things like it delegates to subagents and so forth. So we then actually re-evalu... We designed 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 X fill data from it? And so we ran those evals and we found, okay, we had a model try to hack it, prompt inject it and no successful prompt injections there.

28:05Alexander Embiricos:So that was great. But 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 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 defaults that you tested early on, we started with a very conservative policy, and then we whittled away at it with user feedback.

28:37Alexander Embiricos:So expect to get much easier.

28:39Alex Heath:It was like, accept the terms of service of the retailer before you buy the shoe. Like that kind of thing. I got it.

28:44Alexander Embiricos:It's buy the shoe. Thank you for being an early tester. We really appreciate it. Thanks to you testing some folks in the room don't have to deal with all of the same things. But basically every time we got feedback like the one you shared, we would go discuss that. And we'd be like, okay, how do we want the model to behave here? And so we really toned down. We call those over-confirmations when it really is like you didn't need to confirm that. You would have felt comfortable. So we have this default behavior. And the way the model is instructed to behave is that it does proactive research for you over the tools that you've connected it to.

29:15Alexander Embiricos: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 conflict. Do you want me to move the meeting? 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. Like, hey, next time if I'm on PTO, like on a holiday, you can tell people that I'm on holiday if they're also work colleagues or something.

29:46Alexander Embiricos: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 a layer called auto review, 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 like your rules, the custom instructions and all that, and just make sure everything goes well. And then, so that's like 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.

30:21Alex Heath:You've teased email as a thing that may be coming. When can this be in iMessage, WhatsApp? Is it going to be contained to chat?

30:31Alexander Embiricos:We definitely want it everywhere that you work. So yeah, we're intending, or everywhere that you communicate, right? 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. But I can't share more now. Other than that, it's coming soon.

30:48Alex Heath: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 Muse recently because people were using Muse 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. Like, 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.

31:23Alex Heath: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?

31:34Alexander Embiricos:I think we're going to discover together with the ecosystem how this should work, how 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 Dev Day were really all about the ecosystem. And so we're doing the same. So, for instance, WebBot Auth is something that we work really closely with Cloudflare on. So we actually, our agent declares explicitly that it's a bot. and so it's something we're actively thinking about we're in discussions with many partners for how to enable this but we don't want to take the approach of just saying we're just going to go out and figure out what happens we kind of want to be measured in our approach with customers and we think this works for us this is a little bit why we steered towards going for productive use cases for people at work because this is where we think there's a lot of demand for the power of AI there and there's a lot of really well aligned incentives for people trying to get things done together.

32:35Alexander Embiricos:So we're kind of hoping to land with this incredibly capable assistant for people trying to get ambitious tasks done and then broaden access and capability from there.

32:44Alex Heath: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 services 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'd be curious, even if you're just kind of brainstorming, like, do you have any idea for how this is going to net out?

33:14Alexander Embiricos: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, you know, like the example of like the hardware engineer, like looking at factory defects, that's where I feel like the, all that tool chain that that engineer is using, I think is like very well aligned to having an agent work in there.

33:29Alex Heath:But have you thought about the advertising piece, like companies that they monetize based on, you know, people, human beings looking at it and spending time with it? And now their agents are maybe going to go do that.

33:41Alexander Embiricos:Yeah, I'm not sure how it's going to play out. But I think like broadly, like at least my opinion here is that those companies are providing a service, right? And if previously they were monetizing with ads, we either need to make sure that they continue being rewarded for their 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.

34:02Alex Heath:Yeah. Let's talk about pricing and the fact that you all are putting Astra in this, which is great for everyone. You're not also, correct me if I'm wrong, you're not counting dot usage towards plan limits. Is that right?

34:14Alexander Embiricos:Yeah.

34:15Alex Heath:That can't be a permanent thing. Is this just a temporary to get people excited?

34:19Alexander Embiricos:No, that is permanent. Really? 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. Like 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 it things like hey you know 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 you know some task like that that's like you you're relying on it to have your back in the future and then you accidentally like code too much the night before and it doesn't do its job like that's we don't want that yeah 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 like pretty basic principles.

35:21Alexander Embiricos:So one of those principles is that your dot should be available 24 seven. And there should be no way for it to not be available 24 seven. I mean, barring abuse or something. Another principle should be now that it's available 24 seven, it should always respond to you quickly. So I started likening the way that we want to price to it to a bit of a, like more like a contractor, right? 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.

35:53Alexander Embiricos:And if they, let's say 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. That's just the question you can ask. So this is kind of how we want to price. Your dot is available 24-7. You pay a flat monthly amount to have access to your dot. Maybe you can pay more. another flat monthly amount if you want like a more powerful dot running on a more powerful computer or with more powerful models or whatever and 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 sorry more analogies as you can tell i'm still figuring out how to talk about this um and it does sort of a varied amount of work for you based on 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 i've done a lot of work for you.

36:48Alexander Embiricos:So I'm going to start using a more cost efficient model, or maybe is it okay for you if I do this work overnight? Because if I do it overnight, it's incredibly affordable. Like, is this urgent? And it'll kind of have a sense of whether or not that's urgent, right? On the other hand, maybe if you haven't used it at all that month, it might text you and say, Hey, like you're not utilizing me fully. Like, here's some things I could do for you. So anyways, those are, those are some of the thoughts on how we're going to set this up. The way, the way we're doing pricing is it's going to be long-term.

37:14Alexander Embiricos:It's going to be a separate sort of bucket from your main codex limit 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 like how much work it can do for you, but is always available for you.

37:32Alex Heath:Why not just do like a take rate business if you believe that people will use this to buy things and make transactions at scale? Just take a small vig on all activity that flows through.

37:44Alexander Embiricos:I mean, I think that might make sense for some other business models where you're betting on like transactions being like one of the main things that you're doing. But like I said, we're we're not really betting on that. Like 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.

38:01Alex Heath:OK, well, on that note, you know, 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 ChatGPT now, and you've got the work tab, and chat obviously still is massive. How do you see these things coexisting? Is Dot the future interface of all of it?

38:27Alexander Embiricos: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 uh and now we're just launching dot right and so the way that i look at is like chat is the is the consumer assistant for everyone um right now we have this different product called work which really we should just merge with chat so that 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 gonna 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.

39:14Alexander Embiricos:So we're starting with Dots as a sort of like premium standalone product. It's starting with just Pro. We need to go get it to Plus after we've figured out, learned 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 ChatGPT into just like an incredibly powerful consumer.

39:40Alex Heath: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.

39:46Alexander Embiricos: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 change what they're doing or how they're using things. We just want it to just be upgraded.

39:58Alex Heath:So you see it as more of a gradual thing over time.

40:00Alexander Embiricos:Yeah.

40:01Alex Heath:When you think about the roadmap over the next year for Dot, if we're talking a year from now, what do you think is the most meaningful change?

40:10Alexander Embiricos:Can I tell you a funny story? and we can get back to the question. Okay, so when I first joined OpenAI, it was through this acquisition, and we had a meeting, and some of my team were in the room, and Greg was there, and so everyone 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 no one has any questions. We're all a little nervous, and I was the CEO of this company, so everyone looks at me. It's like, well, you have to ask the obligatory question. So I'm like, oh, what am I going to ask?

40:42Alexander Embiricos:I'm nervous. And so I'm like, okay, in two years, what does success look like? And then Greg, I always remind him of this. He 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. And I was like, oh God, I'm already fired. Okay.

41:01Alex Heath:Well, don't fire me.

41:02Alexander Embiricos:No, no, no. It's all good. Sorry.

41:05Alex Heath:But I get the point. It moves so fast. like six months ago, you probably couldn't imagine all of this. So it is probably an unfair question.

41:14Alexander Embiricos: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 to more users. Let's get Beyond Pro. Also, for example, let's enable your dots to talk to other people and maybe even other dots. 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 shipped space all the interplay between those things right now I think can be really sanded down and made super smooth 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.

42:07Alexander Embiricos: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 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 dots 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 and which are accelerating an entire team or an entire organization.

42:47Alexander Embiricos: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 personal assistant dots, and then we have assistant dots much more broadly deployed within enterprise. But that is another place where we're starting very slow, very careful, working with a handful of customers.

43:06Alex Heath:Got it. Okay, last question. What's the craziest thing your dot has done for you that you're like, wow, it happened and it blew your mind?

43:14Alexander Embiricos:The craziest story that I've heard, it was like 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 props, like awards, for having fixed a bug before it made its way to production.

43:32Alex Heath:And it wasn't.

43:33Alexander Embiricos:And the engineer was like, what happened? By the way, I tell this 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 we're building a lot of safety controls around that. This is why we do small scale. This is early on when it was just our team testing it. And yeah, his dot, he'd asked his dot to stay on top of the feedback channel and the dot saw the feedback and then identified an issue and then spun up a PR and 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.

44:08Alexander Embiricos:And then, you know, the Dots had figured that out, right? It was like, oh, you always create your PRs directly and you always set them to auto-merge. And then someone else had approved the PR and it did it. So to me, that was a really cool moment for sure.

44:20Alex Heath:Wow. Well, knock on wood, we all get to have moments like that. Thank you, Alex.

44:24Alexander Embiricos:Yeah, sure. Thanks for having me.

44:25Alex Heath:Thank you. Try it free at Jira.com. That's J-I-R-A dot com. Framer is the AI native website builder that lets you build faster without giving up control. Visit framer.com slash sources for 30 % off. Rules and restrictions may apply.

45:26Thank you.