Why humans are AI’s biggest bottleneck (and what’s coming in 2026) | Alexander Embiricos (OpenAI Codex Product Lead)

0:00 Lead work on Codex. Codex is OpenEI's coding agent. We think of Codex as just the beginning of a software engineering teammate. It's a bit like this really smart intern that refuses to read Slack, doesn't check data dog unless you ask it to. I remember Carpothe tweeted the gnarliest bugs that he runs into, that he just spends hours trying to figure out nothing else, assault, he gives it to Codex. Let's it run for an hour and it solves it. Starting to see glimpses of the future where we're actually starting to have Codex be on call for its own training. Codex writes a lot of the code that helps manage its training run, the key infrastructure. And so we have a Codec Code Review is like catching a lot of mistakes. It's actually caused some like pretty interesting configuration mistakes. One of the most mind-blowing examples of acceleration is the Sora Android app, like a fully new. Uh we built it in eighteen days. And then 10 days later, so 28 days total, we went to the public. How do you think you win in the space? One of our major goals with Codex is to get to proactivity. If we're gonna build a super assistant has to be able to do things. One of the learnings over the past year is that for models to do stuff, they are much more effective when they can use a computer. It turns out the best way for models to use computers is simply to write code. And so we're kind of getting to this idea where if you want build any agent, maybe you should be building a coding agent. When you think about progress on codex, I imagine you have a bunch of emails and there's all these public benchmarks. A few of us are like constantly on Reddit. You know, there's a there's craze up there and there's a lot of complaints. What we can do basically as a product team just try to always think about how are we building a tool so that it feels like we're maximally accelerating people rather than building a tool that

1:22 makes it more unclear what you should do as the human being at open AI, I can't not ask about how far you think we are from AGI. The current underappreciated limiting factor is literally human typing speed or human multitasking speed. Today, my guest is Alexander Embirios, product lead for Codex, OpenAI's incredibly popular and powerful coding agent. In the words of Nick Turley, head of Chat GPT and former podcast guest, Alex is one of my all-time favorite humans I've ever worked with. and bringing him and his company into OpenAI ended up being one of the best decisions we've ever made. Similarly, Kevin Wheel, OpenAI's CPO, said Alex is simply the best. In our conversation, we chat about what it's truly like to build product at OpenAI.

2:03 How Codex allowed the Sora team to ship the Sora app, which became the number one app in the appstore in under one month. Also the twenty X growth Codex is seeing right now and what they did to make it so good at coding. Why his team is now focused on making it easier to review code, not just write code. His AGI timelines, his thoughts on when AI agents will actually be really useful, and so much more, a huge thank you to Ed Bays, Nick Turley, and Dennis Ying for suggesting topics for this conversation. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. And if you become an annual subscriber of my newsletter, you get a year free.

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5:10 Mm. Alexander, thank you. So much for being here and welcome to the podcast. Thank you so much. I've been following for ages and I'm excited to be here. I'm even more excited. I really appreciate that.

5:24 I wanna start with your time at Open AI. So you joined OpenAI about a year ago. Before that you had your own startup. For about five years. Before that you're a product manager at Drawbox.

5:36 I imagine OpenAI is very different from every other place you've worked. Let me just ask you this. What is most different about how OpenAI operates? And what's something that you've learned there that you think you're going to take with you wherever you go, assuming you ever leave? By far I would say the speed and ambition. of working at open AI are just like dramatically more than what I can imagine.

5:57 And You know, I guess it's kind of an embarrassing thing to say because you you know, everyone who's a startup founder thinks like, Oh yeah, my startup moves super fast and the talent bar is super high and we're super ambitious, but I have to say like working in open AI just kinda like made me reimagine what e what that even means. We hear this a lot about, you know. Feels like every AI company is just like, Oh my God, I can't believe how fast they're moving.

6:17 Is there an example of just like, wow, that wouldn't have happened this quickly anywhere else? The most obvious thing that comes to mind is just like the the explosive growth. Of codex itself. I think it's a while since we bumped our external number, but like, you know, it's like But The ten Xing of Codex's scale.

6:31 Was just like Super fast in a matter of months. And it's like well more since then. And you know, like Once you've lived through that, or at least in speaking for myself, like having lived through that now.

6:42 I feel like any time I'm gonna spend My time. On like You know, building tech product. There's that kind of that speed and scale that I now need to to to meet.

6:52 If I think of like what I was doing in my startup, it moved like way slower. And I You know, there's always this balance with startups of like how much do you commit to an idea that you have versus like find out that it's not working. Uh and then pivot.

7:06 But I think one thing I've realized that Opening I is like the the amount of impact that we can have and in fact need to have to do a good job is so high that it it's a I've been like way more ruthless with how I spend my time now. Before we get to codex. Is there a way that they've structured the org or I don't know the way that OpenAI operates that allows the team to move this quickly? Because everyone everyone wants to move super fast.

7:25 I imagine there's a structural uh approach to allowing this to happen. I mean so one thing is just the technology. that we're building with has like just tr transformed so many things. You know, from like

7:36 Both how we build, but also like what kinds of things we can enable. Uh for users. And You know, we spend most of our time talking about like the sort of improvements in the foundation models, but I s I believe that even if we had

7:48 No more progress today with models, which is absolutely not the case, but if even if we had no more progress. We are way behind on product. There's so much more product to build. So I think like Just like the moment.

7:59 Is right. If that makes sense. But I think there's a lot of sort of counterintuitive things that surprise me when I arrived at as far as like how things are structured. One example that comes to mind is like when I was working on my startup and and before that when I was a Dropbox, it was like very important. you know, especially as a PM to like always kinda rally the ship and always kinda like make sure you're pointed in the right direction and then you can like accelerate in that direction.

8:21 But Here, I think. Because we don't exactly know like what capabilities will even come up soon and we don't know what's going to work. Uh technically. And then we also don't know what's gonna land, even if it works technically.

8:33 It's much more important for us to be very like humble And learn a lot more empirically and just try things quickly. And Like The org is is set up in that way to be incredibly bottoms up.

8:45 You know, this is again one of those things that like as you were saying, everyone wants to move fast. I think everyone likes to say that their bottoms up, or at least a lot of people do. But open AI is like truly, truly bottoms up, and that's like been a learning experience for me. That now like It it'll be interesting if I ever work at like I don't think it'll ever th the it'll even make sense to work at a non AI company in the future. I don't even know what that means.

9:06 But if I were to imagine it or go back in time, I think I would like run things tot totally. What I'm hearing is kind of this uh ready Fire aim. Uh is the approach more than ready aim fire. And there's something uh and as you processed that

9:20 Uh,'cause that may not come across well, but I actually have heard this a lot at AI companies is Because you don't know and Nick Charlie showed I think the same sentiment. Because you don't know how people will use it. It doesn't make sense to spend a lot of time making it perfect. it's better to just get it out there in a primordial way, see how people use it, and then go big on that use case. Yeah.

9:39 It's like Okay, to use this analogy a little bit, I feel like there there is an aim component, but the aim component is much fuzzier. You know, it's kinda like roughly What do we think can happen? Like Someone um

9:50 I've learned a ton from working here as a as a research lead, and he likes to say that like An open AI. We can can have really good conversations about something that's like a year plus from now. And you know, there's a lot of ambiguity in what will happen, but but like that's a right sort of timeline.

10:05 And then we can have really good conversations about what's happening like in like low months. Or low or weeks. But there's kind of this like awkward middle ground, which was like as you start approaching a year, but you're not at a year where it's like Very difficult to reason about, right? And so as far as like aiming, I think we wanna know like okay

10:21 What are some of the futures that we're trying to build towards and like A lot of the problems we're dealing with in AI, like such as alignment, are problems you need to be thinking out like really far out into the future. So we're kind of aiming fuzzily there. But when it comes down to the more tactically like Oh yeah, like what product will we build and therefore how will people

10:37 Use that product. That's the place where we're much more like let's find out empirically. That's a good way of putting it. Something else that when people hear this, they People sometimes hear companies like yours

10:48 saying okay, we're gonna be bottoms up, we're gonna try a bunch of stuff, we're not gonna have exactly a plan of where it's going in the next few months. The key is you all hire the best people in the world. And so that feels like a really key ingredient in order to be the successful at bottoms up work. Just supervising basically.

11:04 Um I was just like Again. surprised or even shocked when I arrived at like the level of like individual like drive and like Autonomy that

11:13 Everyone here has. So I think like the way that OpenAI runs, like many you you can't like read this or be on the listen to a podcast and be like, I am I'm just gonna deploy this to my company. Um You know, maybe this is a harsh thing to say, but I think like, yeah, very few companies have the talent caliber to be able to do that.

11:30 So it might need to be like Adjusted. Okay, so let's talk codex. You lead work on codex. How's Codex going? What numbers can you share? Is there anything you can share there? Also just

11:42 Not everyone knows exactly what Codex is. Explain what Codex is. Totally, yeah. So uh I had the very lucky job of of living in the future and leaning promise on codex. Um and Codex is Open the eyes coding agent.

11:54 So Super concretely, that means it's an IDE extension, the VS Code extension. uh that you can install or a terminal tool that you can install and when you do so You can then Basically pair with codex to answer questions about code, write code.

12:08 uh you know run tests, execute code. And do A bunch of the work in sort of that like thick middle section of the software development lifecycle. Which is all about Uh, you know, writing code that you're gonna get into production.

12:20 Uh More broadly. We think of codex as like It's c what it currently is is just the beginning. Of a software engineering teammate.

12:29 And so, you know, when we when you when we use a big word like teammate, like some of the things we're imagining are that It's not only able to To write code. But actually it participates like early on in like the ideation and planning phases of writing software. And then further downstream in terms of like

12:43 Validation, deploying, and like maintaining code. To make that a little more fun, like one thing I like to imagine is like if you think of what codex is today. It's a bit like this like really smart intern that like refuses to read Slack. And like doesn't check data dog. Or like century, unless you ask it to.

12:59 And so like no matter how smart it is, like how much you're gonna trust it to write code without you also working with it, right? So that's how people use it mostly today is they pair with it. But we wanna get to the point where Yeah, it can work. Like just like a new intern that you hire, you don't only ask them to write code, but you ask them to participate across the cycle. And so you know that like even if they don't get something right the first try, they're eventually gonna be able to iterate their weight.

13:19 There. I thought the point about not reading Slack and Dave Dog was it's just not distracted, it's just constantly focused and this is Always in flow, but I get what you're saying there is it doesn't have all the context on everything that's going on. And like that's not only true when it's performing a task, but again, if you think of like the best team and teammates, like you don't tell them what to do. Right? Like maybe.

13:38 When you first hire them, you have like a couple of meetings and you're like, Hey, like You kinda learn like, Okay, this is this these prompts work for this teammate, these prompts don't, right? This is how to communicate with this person. Then eventually you give them some starter tasks to delegate a few tasks. But then eventually you just say, like, hey, great, okay. You're working with this set of people.

13:54 In this area of the code base. You know, feel free to work with other people in other parts of the code base too, even. And uh yeah, you tell me what you think makes sense to be done. Right. And so You know, we think of this as like proactivity and like one of our major goals with codecs is to like get to proactivity.

14:09 I think this is This is like Critically important. to like achieve the mission of opening AI, which is to deliver the benefits of AGI to all humanity. Yeah, I like to joke today that like AI products

14:19 And it's it's a half joke. They're actually like really hard to use. Because you have to like Be very thoughtful about When it could help you.

14:28 And if you're not prompting a model to help you It's probably not helping you at that time. And if you think of how many times like the average user is prompting AI today, it's probably like tens of times. But if you think of how many times people could actually get benefit from a really intelligent entity.

14:43 It's Thousands of times per day. And so a large a large part of our our goal with codec is to figure out like What is the shape? Of an actual teammate agent that is sort of helpful by default.

14:53 When people think about cursor and Uh even cloud coded. It's like uh I D E that helps you code and kind of auto completes code and maybe Does some agentic work? What I'm hearing here is the vision is

15:04 is different, which is it's a teammate. It's like a Remote teammate. A building code for you that you talk to and ask. To do things. And it also does I m I DE autocomplete and things like that.

15:15 Is that is that a kind of a differentiator in the way you think about codex? It's basically this idea that like We want the way like if you're a developer and you're trying to get something done, we want you to just feel like you have superpowers and you're able to move much, much faster. But we Don't think that's the thing.

15:30 In order for you to reap those benefits. you need to be sitting there constantly thinking about like how can I invoke AI at this point to do this thing. We want you to be able to sort of like plug it in. to the way that you work and have it just start to do stuff without you having to think about it. Okay. I have a lot of questions along those lines, but uh just how's it going? Is there any stats, any numbers you can share about how Codecs is doing?

15:49 Yeah, it's been codex has been growing like absolutely explosively. Um, since the launch of GPT five back in August. Um there's some definitely some interesting like product insights to talk about as to like how we unlock that growth if you're interested. But Yeah, and the last the last thought we shared there was like we we were like well over ten X.

16:06 since August. In fact it's been like twenty X since then. Um also the codex models are serving many tr many trillions of tokens a week. Now and it's basically like our most served coding model. Um One of the really cool things that we've seen is that The way that we decided to set up the codex team.

16:22 uh was to build a you know really tightly integrated product and research team. That are iterating on the model and the harness together. It turns out that lets you just Do a lot more and try many more experiments as to how these things will work together.

16:35 And so We were just training these models for use in our first party harness that we were very opinionated about. And then what we've started to see more recently actually is that other major sort of API coding customers are now starting to adopt these models as well.

16:50 And so we've reached a point where actually the codex model is the most served coding model. In the API as well. You uh hinted at this, uh w what unlocked this growth. I am extremely interested in hearing that. It felt like

17:02 before I don't know, maybe this was before he joined the team. It just felt like Claude Code was killing it. Just everyone was sitting on top of cloud code. It was by far the best way to code and then all of a sudden codex comes around. I remember Karpathi tweeted. That he just

17:16 Like has never seen a model like this. The gnarliest bugs that he runs into that he just spends hours trying to figure out nothing else to solve. He gives it to Codex, lets it run for an hour and it Solves it. What what'd you guys do?

17:30 We have this strong sort of mission here at OpenEI to you know, basically to build A VI. Um And so

17:37 We we think a lot about What how can we shape the product so that It can scale. Right. You know, I earlier I was mentioning like, hey, like if you're an engineer, you should be getting help from an From AI like thousands of times per day, right?

17:48 And so We thought a lot about the primitives for that. When we launched our first version of Codex. Uh, which was Codex Cloud. And that was basically a product that had its own computer, lived in the cloud, you can delegate to it.

18:00 And you know, the sort of the coolest part about that is you could run many, many tasks in parallel. But some of the challenges that We saw or that we're going to do that. It's A little bit harder.

18:10 to Set that up. Both in terms of like environment configuration, like giving the model The tools it needs to value its changes. And to learn how to prompt in that way.

18:19 And sort of my min my analogy for this is going back to this teammate analogy. It's like if you hired a teammate But You're never allowed to get on a call with them. And you can only go back and forth. you know, asynchronously.

18:30 Over time. Like that works for some teammates. And eventually that's actually how you want to spend most of your time. So that's still the future. But it's hard to initially adopt. And so we still have that vision of like that's what we're trying to get you to, a teammate that you delegate to and then is proactive. And we're seeing that growing, but

18:46 The key unlock is actually first you need to land with users in a way that's like much more intuitive and like trivial to get value from. So The way that most people discover, like the vast majority of users discover codec today is either they download an IDE extension.

19:01 Or they run it in their CLI. And the agent works there with you on your computer interactively. And uh it works within a sandbox, which is actually like a really cool piece of tech. To s to to help that be safe and secure.

19:13 But It has access to all those dependencies. So if the agent needs to do something, like it needs to run a command, it can do so within the sandbox. We don't have to set up any environment. And if it's a command that doesn't work in the sandbox, it can just ask you. And so you can get into this like

19:26 really strong feedback loop using the model. And then over time like turn that feedback loop into you Sort of

19:34 as a byproduct of using the product. configuring it so that you can then be delegating to it down the line. And again, now Gee. Yeah, keep going back to it, but like if you hire a teammate And you ask them to do work, but they you just give'em like a fresh computer from the store.

19:47 it's gonna be hard for them to do their job, right? But if as you work with them side by side You could be like, Oh, you don't have a password for this service we use. Like here's the password for the service. You know, yeah, don't worry, feel free to run this command. Then it's like much easier for them to then go off and do work for hours without you. So what I'm hearing is mm the initial version of codex was almost too far in the future.

20:05 It's like a remote in the cloud. uh agent that's coding for you asynchronously. And what you did is okay, let's actually is come back a little bit. Let's integrate into the way engineers already integrate into IDs and locally. And help them kinda on ramp to this new world. Totally. And

20:22 This was it was quite interesting because We we dog food product a ton. An open AI? So you know, dark food we as in we use our own product. And so Codex has been accelerating open AI.

20:33 over the course of the entire year and the cloud. was a massive accelerant to the company as well. Um It just turns out that This is one of those places where the signal we got from dog fooding is a little bit different from the signal you get from like the general market, because at OpenAI, you know.

20:50 We train reasoning models all day. And so we're very used to this kind of prompting and like Yeah. Think up front, run things massively in parallel. And uh

20:58 You know, it would take some time and then come back to it later asynchronously. And so You know now when we build we still get a t a ton of signal from dog fooding internally. But Uh

21:08 you know, we're also very cognizant of like the different ways that different audiences use the product. That's really funny. It's like Live in the future, but maybe not too far in the future. And I could see how everyone opening AI is living very far in the future and sometimes that won't that won't work for everyone. Yeah. What about just like uh intelligence training data? I don't know, is there something else that

21:28 Help. codex accelerate its ability to actually code? Is it like better, cleaner data? Is it more just models advancing? Is there anything else that really helped accelerate? Yeah, so there's like a few components here. Um

21:41 I guess. Yeah, you were mentioning models and the models have improved a ton. In fact, um Just last Wednesday we shipped GPT five one Codex Max. A very you know.

21:50 Accurately named model. Mm. Um That is that is awesome. It is awesome both because it is um for any given task that you were using GPD five point one codex for.

22:01 It's like no. Roughly uh thirty percent faster at accomplishing that task. But also it unlocks a ton of intelligence. So If you use it at our higher reasoning levels, it's just like even smarter.

22:11 Um and you know that that feedback that or that tweet you were saying, like Karpathi made about like hey, give us your gnarliest bugs, like Obviously there's a a ton going on in the market right now, but like Codex Max is definitely like carrying that mantle. of us, you know, tackling the hardest bugs. Um So that is

22:27 That is super cool. But I will say it's like Some of what we're gonna do. how we're thinking about this is evolving a little bit from being like, Yeah, we're just gonna think about the model and like let's just like train the best model. Like what is an agent?

22:39 Actually overall. Right. And you know, I'm not gonna try to define Agent exactly, but at least the stack that we think of it as having is it's like you have this model. really smart reasoning model.

22:50 that knows how to do a specific kind of task really well. So we can talk about how we make that possible. But then actually We need to serve that model. Through an API. Into a harness.

23:00 And both of those things also have a really big role here. So for instance One of the things uh they're really proud is you can have GPT five point one codex masks work for really long periods of time. That's not like normal, but you can set it up to do that, or that might happen.

23:13 But now routinely we'll hear about people saying like yeah, it ran like overnight or it ran for twenty four hours. And so you know, for a model to work continuously for that amount of time, it's gonna exceed its context window. And so we have a solution for that, which we call compaction. Um But compaction is actually a feature that uses like all three layers of that stack.

23:32 So You need to have A model that has a concept of compaction and those like, okay, as I start to approach this context window, I might be asked to like prepare to be running a new context window. And then

23:43 At the API layer. You need an API that like understands this concept and like has an endpoint that you can hit to do this change. And at the harness layer, you need a harness that can like prepare the payload for this to be done. And so like shipping this compaction feature that now just like made this behavior possible to like anyone using codex.

23:58 actually been working across all three things and I think that's like increasingly gonna be true. Another Maybe like underappreciated version of this is is if you think about all the different coding products out there, they all have like very different tool harnesses.

24:11 with like very different opinions on how the model should work. And so if you want to train a model to be good at like all the different ways uh it could work, like You know, maybe you have a strong opinion that it should work using semantic search. Right. Maybe you have a strong opinion that it should like call the spoke tools. Or maybe you have like in our case a strong opinion that it should just use like

24:28 The shell. Work in the terminal. You know, you can be much you can move much faster if you're just optimizing for one of those worlds. Right. And so The way that we build codecs is that it just uses the shell.

24:39 But in order to make that like safer and secure We uh have a sandbox that the model is used to operating in. So I think one of the biggest accelerants to go all the way back to your to your answer question is just like We're building all three things in parallel. And like kinda tuning each one.

24:53 And um you know, constantly experimenting with how those things work. with like a tightly integrated product and research team. How do you think? You win in the space.

25:02 Do you think it it'll eventu it'll always be this kind of like race with other models constantly kind of leapfrogging each other? Do you think there's a world where someone just t runs away with it and no one else can ever catch up? Is there like a path to just we win? Again, comes back to this idea of like building a teammate. And Not just a teammate that

25:20 You know Uh participates in team planning and prioritization, not just a teammate that you know, really tests its code and like helps you maintain and deploy it. But even a teammate, you know, like if you think again, an engineering teammate, they can also like schedule a calendar invite.

25:34 Right, or move stand up. Or do whatever, right? And so In my mind If we just imagine

25:41 That's Every day or every week some like crazy new capability is just gonna be deployed. by a research lab. It's just impossible for us, like, you know, as humans to keep up and like use all this technology. And so I think We need to get to this world where

25:55 You kind of just have like Or super assistant that you just talk to. And it just knows how to be helpful, like on its own. And so you don't you don't have to be

26:05 like reading the latest tips for how to use it. You're just like you've plugged it in and it just provides help. And so That's kind of the shape of what I think we're building and I think that will be like a very sticky, like winning product if we can do so. So the shape that in in my head at least I have is that. We build

26:21 you know, maybe a c fun topic is like is chat the right interface for AI? I actually think chat is a very good interface. When you don't know what you're supposed to use it for? Uh in the same way that if I think of like I'm like on its teams or in Slack with a teammate. Chat is pretty good. I can ask for whatever I want.

26:35 Right, it's like Kind of the the common denominator for everything. So you can chat with the super assistant. about whatever topic you want, whether it be coding or not. And then if

26:45 You are like a functional expert in a specific domain, such as coding, there's like a GUI. That you can pull up. To go really deep. and like look at the code and like work with the code. So I think like what we need to build

26:57 As opening eye is basically This idea of like you have chat chat PT. And not as a tool that's like ubiquitously available to like everyone. You start using it even like outside of work. Right, to just help you. you become very comfortable with the idea of being accelerated with AI.

27:11 And so then you get to work and you just can naturally just yeah, I'm just gonna ask it. for this and I don't need to know about all the connectors or like all the different features. I'm just gonna ask it for help and it'll surface to me. the the best way that it can help at this point in time. And maybe even chime in when I didn't ask it for help. Um so in my mind if we can get to that, I think that's

27:29 You know, that's how we We really don't like the winning product. This is so interesting because Uh with the my chat with Nick Charlie, the head of Chat GPT. I think you shared that the original name for Chat GPT was Super Assistant or something like that. Yeah.

27:42 And it's interesting that There's like that approach to the super assistant and then there's this codex approach. It's almost like the B to C version and the B to B version. And what I'm hearing is the idea here is okay, you start with coding and building and then it's doing all this other stuff for you, scheduling meetings, I don't know, probably posting and Slack. Uh I don't know, shipping Designs. I don't know. Is that is the idea there this is like the the business version of Chat GPT in a sense, or is there or is there something else there?

28:08 Yeah, so you know so we're getting to the like uh The like one year time horizon conversation. A lot of this might happen sooner, but in terms of fuzziness, I think we're at the one year. So I'll give you like A contention and like a plausible way we get there, but as for how it happens, who knows? So basically If we're gonna build a super system, it has to be able to do things.

28:26 Right. So like we're gonna have a model and it's gonna be able to do stuff. Affecting your world. And one of the learnings I think we've seen Over the past year.

28:35 Or so is that For models to do stuff, they're much more effective when they can use a computer. Right. Okay. So now we're like, Okay, we need the super assistant that can use a computer.

28:45 Right, or many computers. And now the question is, okay, well how should it use the computer? Right. And there's lots of ways to use a computer. Uh, you know, you could try to hack the OS and like use accessibility APIs, maybe a bit easier as you could point and click. That's a little slow, you know, and uh unpredictable sometimes.

29:02 Um and another way, it turns out the best way for models to use computers is simply to write code. Right. And so we're kind of getting to this idea where like, well, if you want to build any agent, maybe you should be building a coding agent. And maybe To the user, a non-technical user, they won't even know they're using a coding agent, the same way that no one thinks about are they using the internet or not, which is they're more just like, is Wi Fi on?

29:22 Right. So I think that what we're doing with codex is we're building A software engineering teammate. And as part of that, we're kind of building an agent that can use

29:32 uh a computer by writing code. And so We're already seeing like some pull for this. It's like quite early. But we're starting to see people like who are using codex for like coding adjacent product purposes. And so

29:44 As that develops, I think we'll just naturally see that like, oh, it turns out like we should just always have the agent write code if there is a coding way to solve a problem instead of. You know, even if you're doing a financial analysis, right? Like maybe write some code for that. So basically, like, you know, you were like, Hey, is this like the two ends of of uh of this product for the super assistant, right, of Chat GPT. In my mind, like just coding is a core competency of any agent, including CatGPT. And so like what really what we think we're building is like. that competency. But

30:10 So here's here's like the really cool thing about agents writing code is that you can import. Code. Right. Code is like composable. Interoperable.

30:18 Right. If if we You know, one very reductive view we could have for an agent is it's just gonna be given a computer and it's just gonna like point and click and and you know. Go around.

30:28 But You know, that is the future and then how we get there. is difficult to sort of chart a path. Because a lot of the questions around building agents aren't like can the agent do it, but it's More about

30:40 Well How can we help the agent understand the context that it's working in and like the team that's using it? you know, probably has a way that they like to do things. They have guidelines. They probably want certain deterministic guarantees about what the agent can or cannot do. Well they want to know that the agent understands.

30:56 sort of this detail, like an example would be You know, if we're Looking at a crash reporting tool. Hitting a connector for it. Every subteam is probably has a different meta prompt for like how they want the crashes to be analyzed.

31:10 Right. And so we start to get to this thing where like, yeah, we have this agent sitting in front of a computer, but we need to Make that configurable for the team. Or for the user. Right. And let them like

31:19 Stuff that the agent does often we probably just wanna like build in as a competency that this agent has that it can do. So I think We end up with this generalizable thing that you were saying of like an agent that can just write its own scripts for whatever it wants to do. But I think that

31:34 Th the The really key part here is can we make it so that Everything that the agent has to do often or that it does well. We can just like remember and store. So that the agent doesn't have to write a script for that again, right? Or maybe like if I just joined a team and you are already on the same team as me.

31:50 I can just like use all those scripts that the agents had written already. Yeah. It's like If this is our teammate, uh we can They can share things that it's learned from working with other people at the company. Just makes sense as a metaphor.

32:01 Yeah. It feels like you're in the uh Karpathi camp of agents today are not that great and mostly slop and Maybe in the future they'll be awesome. Does that resonate? I think so, I think coding agents are pretty great. I think

32:14 Uh deals right. Yep. And then I think like agents out outside of coding, it's still like very early. And you know, this is just my opinion, but I think they're gonna get a whole lot better once they can use coding too in like in a composable way. Yeah. This is it's kinda the fun part of like when you're building for software engineers, like I

32:32 And my startup we were building for software engineers too for a lot of that journey. And they're just such a fun audience to build for. Because You know, they also like building for themselves. And are often like even more creative than we are in thinking about how to use the technology.

32:46 Um and so like by building for software engineers, you get to just observe a ton of emergent behaviors and like things that you should do and build into the product. I love how you you say that'cause a lot of people building for engineers get really annoyed because the engineers have so They're just always complaining about stuff. They're like, Yeah, that sucks. So why'd you build it this way? Uh I love that you enjoy it, but I think it's probably because you're building such an amazing tool for engineers.

33:07 That can actually solve problems. Yeah and just You know, code for them. Um kind of along those lines, you know, there's always this talk of What will happen with jobs, engineers, coding, do you have to learn coding, all these things.

33:19 Uh clearly the way you're describing it is it's a teammate. It's gonna work with you, make you more superhuman. It's not gonna replace you. With the way you just think about The impact on

33:27 the field of engineering having uh s super intelligent engineering teammate. I think there's there's two sides to it, but The one we were just talking about is this idea that Maybe every agent

33:39 should actually use code. And be a coding agent. And in my mind, that's just like a small part of this like broader idea that like, hey, as we make code even more ubiquitous. I mean, you could probably claim it's ubiquitous today, even three AI, right? And as we make code even more ubiquitous, it's actually just going to be used for many more purposes. And so there's just gonna be a ton more need for people with this like humans with this confidency.

34:01 So That's my view. I think this is like Quite a complex topic. So You know, it's something we talk about a lot and we have to kinda see how it pans out. But I think what we can do

34:11 What we can do basically as a product team building in the space is just try to always think about How are we building a tool so that it feels like we're like maximally accelerating? uh people. You know, rather than building a tool. That's right.

34:23 makes it like more unclear what you should do as the human. Right. Like I think like To to you know give an example right now like Nowadays when you work with a coding agent.

34:33 Um, it writes a ton of code, but it turns out writing code is actually one of the most fun parts of software engineering for many software engineers. It's then you end up reviewing AI code. Right. And That's often a less

34:45 Fun part of the job for many software engineers. Right. And so I actually think like We see that like this This comes up plays out all the time in like a ton of micro decisions. And so we as a product team are always thinking about like, okay, how do we make this more fun? How do we make you feel more empowered? Whereas it's not working and I I would argue that like

35:00 Reviewing agent written code is like a place that Today is like less fun. And so you know, then I think okay, what can we do about that? Well we can ship a code review feature. That like helps you build confidence in the I written code. Okay, cool. You know, another thing we could do is we can make it so that the agents like better able to validate its work.

35:16 And you know it it gets all the way down into like micro decisions. Like if you're gonna have the and the agent capability to validate work. And let's say you have like I'm thinking of Codex Web right now, like you have a a pain that sort of reflects the work the agent did. What do you see first? Do you see the diff or do you see the image preview of the code it wrote?

35:33 Right. And you know I think if you're thinking about this from perspective like how do I empower the human, how do I make him feel like as as accelerated as possible, like You obviously see the image first. Right, you shouldn't be reviewing the code.

35:43 Unless first. you know, you've seen the image unless it's maybe it's been like reviewed by an AI and now it's time for you to take a look. When I had uh Michael Cherell, the CO of Cursor on the podcast, he had this kind of vision of us moving To something beyond code.

35:57 And I've seen this rise of something called spec driven development, where you kinda just write the spec And then the code. you know, the AI writes code for you and so you kinda w start working at this higher abstraction level. Is that something you see where we're going, just like engineers not having to actually write code or look at code, and there's gonna be this higher level of abstraction that we focus on? Yeah.

36:17 I think there's like constantly these levels of abstraction and they're actually already played out today. Right. Like Today like coding agents mostly it's like prompts to patch. Right. We're starting to see people doing like spec driven development or like planned and driven development. That's actually one of the ways

36:34 When people ask, like, hey, how do you run codecs on a really long task? Well it's like often Collaborate with it first to write like a plan.md, like a markdown file that's your plan. And once you're happy with that. then you ask a kid to g to go off and do work. And if that plan has verifiable steps, it'll like work for much longer. Um so we're totally seeing that.

36:50 I think spec driven development is like an interesting idea. It's not clear to me that it'll work out that way. 'Cause a lot of people don't write like Don't like writing specs either. But it it seems plausible that some some people will work that way.

37:03 Yeah, like uh A bit of a joke idea though is like if you think of like Um the way that many teams work today. They're they often like don't necessarily have specs. But the team is just really self driven and so stuff just gets done. And so almost that is like

37:16 I'm coming up with this on the spot, so it's you know, not a good name, but like chatter driven development. We're just like stuff is happening, you know, on social media and like in your team communications tools. And then as a result Like code gets written and deployed. Right. So

37:30 Yeah, I think I'm a little bit more oriented in that way of You know I don't even necessarily want to have to write a spec, like sometimes I want to, only if I like writing specs. Right. Uh other times I might just want to say like, hey, here's the like the customer, you know, service channel and like tell me what's interesting to know, but if it's a small bug, just fix it.

37:49 I don't have to write us back for that. Yeah. I had this sort of Uh Hypothetical future

37:56 uh that I like to share sometimes with people is a provocation, which is like In a world where we have like truly amazing agents, like what does it look like to be a solo entrepreneur? Um And uh Yeah.

38:06 one terrible idea for how it could look is that it's actually There's a mobile app. And um Every idea that it the agent has to do. Is just like vertical video.

38:16 On your phone. And then you can like swipe left if you think it's a bad idea and you can like swipe right if it's a good idea and like you can press and hold. And like speak to your phone if you want to get feedback on the idea before you swipe. You know, and in this world, like basically what your job is just to like plug in this app into like every single like signal system.

38:34 you know, system of record, and then you just sort of sit back and like swipe. I don't know. I love this. So this is like Tinder meets TikTok meets codex. It's pretty terrible. No, this is great. So the idea here is this thing is this agent is just watching and right listening to you.

38:49 paying attention to the market, your users, and it's like, Well, I here's something I should do. It's like a proactive engineer, just like here, we should build this feature, fix this thing. Exactly. I think communicating with you in like the lowest is like the the modern way way to communicate. Yeah.

39:05 Yeah, swipe left to right and uh in vertical feed. And then the store video, okay, so I see how this all connects now. I see. To be clear, we're not building that, but like, you know, it's a fun idea. I mean, like in this example though, like one of the things that it's doing is it's consuming external signals, right? I think the other

39:22 really interesting things like if we think about like what is the most successful like AI product to date. Um I would argue Um

39:31 Not to confuse things at all, but like the first time we used the n the the the brand codex at OpenAI was actually the model power and GitHub Copilot. This is like way back in the day, years ago. And so we decided to reuse that that brand recently, um,'cause it's just so good. You know, codex, code execution. But I think actually like Auto completion and I D E's is like one of the most successful AI products.

39:53 Today. And part of what's so magical about it is that when uh the it can surface like ideas for helping you Really rapidly. When it's right, you're accelerated. When it's wrong, it's not like that annoying. It can be annoying, but it's not that annoying.

40:09 Right. And so you can create this like mixed initiative system that's like contextually responding to like what you're attempting to do. And so In my mind this is like A really interesting thing for us as open AI as we're building.

40:22 So for instance Yeah, when I think about launching a browser, which we did with Atlas. Right, like in my mind one of the really interesting things we can then do

40:31 is we can then like contextually surface like ways that we can help you. As you're going about your day. Right. As we break out of this like you know, we're just looking at code or we're just in your terminal.

40:42 Um, into this idea that like hey, like a real teammate is dealing with a lot more than just code. Right. They're dealing with a lot of things that are web content. So like Yeah, how can we help you with that? Man, there's so much there. I love this. Okay. So autocomplete on the web with the browser. That's so interesting.

40:56 Just like here's all the things that We can help you with as you're browsing and Going about your day. I want to talk about Atlas. I'll come back to that. Uh codex, code execution. Did not know that. That's really clever.

41:07 I I get it now. Okay, and then this chatter, what is it, chatter driven development? Yeah. I had a no, this is a really good idea. But it reminds me I had John G Don G on the podcast, CT of Block.

41:18 And they They have this product called Goose, which is their own internal Agent. And he talked about an engineer.

41:25 I block. Just uh has Goose watch him. With like his screen. And listens to every meeting and proactively does work.

41:35 that he should p will probably want to do. So ships a PR, sends an email drafts a Slack message. So he's doing exactly what you're describing in in kind of a very early way. Yeah, that's super interesting. And you know, I bet you the so

41:48 If we go if we went and asked them what the bottleneck to that productivity is, did did they share? What it is. Uh probably looking at it, just making sure this is the right the right thing. Yeah. Yeah. Yeah. So like we see this now. Like we have a Slack integration for codex. People love, you know, if there's like something that you need to do quickly, people will just like add mentioned codecs, like, why do you think this bug is happening, right? Doesn't have to be an engineer, even like maybe you know, data scientists often here are are using codecs a ton to just like

42:13 Answer questions like why do you think this metric moved? What happened? So questions. Yeah, you get the answer right back in Slack. It's amazing, super useful. But when it's as for when it's w writing code.

42:23 Then you have to go back and look at the code. Right. And so The real like I think bottleneck right now is like Validating that the code worked and like writing code review. So in my mind, if we wanted to get to something like uh, you know, that uh a friend you were talking about's uh world, I think we

42:39 We really need to figure out how to get. People to configure. their coding agents to be much more autonomous on those later stages of the work. It makes sense. Like you said. Writing code. I used to be an engineer. I was an engineer for ten years.

42:50 Really fun to write code, really fun to just get in the flow, build architect test. Not so fun to look at everyone else's killed and just have to go through and be on the hook if it is doing something dumb that's gonna take down production. And Now that building has become easier. What I've always heard from companies that are really at the cutting edge of this is

43:06 The bottleneck is now like figuring out what to build. And then it's at the end of like okay, we have all this All a hundred PRs to review. Who's gonna go through all that? Right.

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44:00 Get Gira Product Discovery for free at atlassian dot com slash Lenny. That's at Lassian dot com slash Lenny. What is the impact Of codecs been on the way you operate as a product person, as a PM. It's clear how engineering is impacted.

44:16 Yeah. Code is written for you. What has it done to the way you operate and the way PMs operate at at OpenAI? Yeah, I mean I think mostly I just feel like much more empowered. Um

44:29 I've always been sort of more technical leaning PM. And especially when I'm working on products for engineers, I feel like it's necessary to like, you know, dog food the product. But even beyond that, I I I just feel like I can do much, much more. As a VM and uh you know, Scott Belski talks about this idea of like compressing the talent stack. I'm not sure if I'm phrased that right.

44:46 But it's basically this idea that like Maybe the boundaries between these roles are a little bit I Less needed than before. Because people can just do much more.

44:55 And every time you someone can do more, you can like skip one communication boundary and make the team like that much more efficient. Right. So I think I think we see it

45:06 You know In a bunch of functions now, but I guess since you asked about like product specifically. Uh, you know, now like answering questions much, much easier. You can just ask Codex for thoughts on that. Uh

45:17 A lot of like PM type work, understanding what's changing. Again, just ask Codex for help with that. Um Prototyping is often faster than writing specs. This is something that a lot of people have talked about. I think Something that

45:31 I don't think it's super surprising, but something that's slightly surprising is like we see Like we're mostly building codecs for to write code that's gonna be deployed to production. But actually we see a lot of throwaway code written with codex now. It's kinda going back to this idea of like, you know, ubiquitous code. So you'll see

45:46 Uh You know. Someone wants to do an analysis. Like if I want to understand something, it's like okay. Just give codex a bunch of data, but then ask it to build like an interactive like data viewer for this data. Right. You wouldn't that's just like too annoying to do in the past, but now it's just like Totally worth.

45:59 the time of just getting an agent to go do something. Um Similarly. I've seen like some pretty cool prototypes on our design team about like If you want to

46:08 Well, like a designer basically wanted to build an animation. And this is the coin animation codex. And it was like normally it'd be too annoying to program this animation. So they just vibe coded a animation editor. And then they use the animation editor to build the animation, which they then checked into the repo.

46:24 Actually our design is a there's a ton of acceleration there and like Speaking of compressing the Town Stack, I think our designers are very P M E. So you know, they they do pr ton of product work and like they actually have like an entire like vibe coded

46:37 sort of side prototype. of the codex app. And so a lot of how we talk about things is like we'll have like a really quick jam because there's like 10,000 things going on. And then the design will like go think about how this should work. But instead of like talking about it again, they'll just like vibe code a prototype of that in their like standalone prototype. We'll play with it. If we like it, they'll vibe code that prototype into

46:56 or vibe engineer that prototype into An actual PR to land. And then depending on their comfort with the code base, like codex TLIs and Rust is a little harder. maybe they'll like land it themselves or they'll like get close and then an engineer can help them like land the PR. Um

47:10 You know, we recently s shipped the Sora Android app. Um and Uh that was one of the most Mind blowing examples of acceleration, actually.

47:20 'Cause the uh usage of of codex internally to open it is obviously Really, really high. But it's been growing. Uh over the course of the year, both in terms of like now it's basically like all technical staff use it.

47:31 Uh, but even like the intensity and know how of how to make the most of coding agents has gone up by a ton. And so the Sora Android app, right? Like a fully new app. We built it. In eighteen days. It went from like zero to launch to employees.

47:44 And then ten days later, so twenty eight days total, we went to just like GA. To the public. And that was done just like with the help of codex. So Pretty insane velocity.

47:55 I would say it was like A little bit. Mm. I don't want to say easy mode. But There is one thing that Codex is really good at if you're a company that's like building software on multiple platforms.

48:05 So you've already figured out like some of the underlying like APIs or systems. Asking codecs to s like to port things over. is really effective because it has like something you can go look at. And so the engineers on that team Uh we're basically having codex. Go look at the iOS app.

48:20 produce plans of work that needed to be done and then go implement those. And it was kind of looking at iOS and Android at the same time. And so, you know, basically it was like two weeks to launch the employees, four weeks total. Insanely fast. What makes that even more insane is it was the it became the number one app in the app store. I don't know.

48:37 This just boggles the mind. Okay. So Yeah. So imagine what happened in the app store. With like a handful of engineers. Uh I think it was like Two or three, possibly?

48:49 Uh In a handful of weeks. Yeah. This is absurd. So

48:56 Yeah, so that's a really fun Um Example of a acceleration and then like Atlas is the other one that I think um Ben did a podcast the the the

49:06 And on Atlas. Uh sharing a little bit about how we built there. You know Many Atlas is is actually I mean it's a it's a browser, right? And building a browser is really hard. Um

49:17 And so We uh Had to Build a lot of difficult systems in order to do that. And

49:23 Basically we got to the point where that team has a ton of power users of codex. right now. And um you know, got to the point where they they were basically were s we we know we were talking to them about it. 'Cause a lot of those engineers are people I used to work with. I've before my start up. And so

49:38 It say, you know, before this would have taken us like two to three weeks. For two to three engineers. And now it's like one engineer One week. Um so massive acceleration there as well. And

49:49 What's quite cool is that uh Yeah, we we shipped Atlas on on Mac first, but now we're working on the Windows version. You know, that so the team now is like ramping up on Windows and they're helping us make codex better on Windows too. Which is adm admittedly earlier, like just the model we we shipped last week is the first model. that natively understands PowerShell.

50:07 So you know, PowerShell being. on the native like shell language. on Windows. So

50:13 Yeah, it's been It's been really awesome to see like the whole company getting accelerated by Codex, like from And You know, the most obviously also research and like improving how quickly we train models and how well we do it.

50:25 And then even like uh design as we talked about and and marketing. Like actually we're at this point now where Uh, my product marketer is often also making string changes just directly from Slack. Or like updating docs directly from Slack. These are amazing examples.

50:39 You guys are living at the bleeding edge of what is possible and this is how other companies are gonna work. Uh just shipping Again, what became the number one app in the app store and just blubbed all over the it just like took over the I don't know the world for at least a week. Uh built you said a twenty eight days and like I don't know, ten days, eighteen days, just to get like the core of it.

51:00 Working. Yeah, so like eighteen days we had a thing that employees were playing with. Yeah. And then ten days later we were out. And you said just a couple engineers. Yeah. Two or three. Okay. And then Atlas, you said was took a week to build. No no no. So Atlas not the whole week, but Atlas was like a really meaty project. Yeah. Um and so I was talking to one of the engineers on Atlas. Um About like you know, just how

51:22 What they use codex for. And it's basically like we use codecs for absolutely everything. And I was like, Okay. Well like You know, how would you how would you measure the acceleration? So basically the the answer I got back was Previously it would have taken two to three weeks for two to three engineers and now it's like one engineer one week. Do you think this eventually moves to non engineers doing this sort of thing? Like does it have to be an engineer building this thing could sort of build been built by a I don't know, a PM or designer.

51:45 I think we will very much get to the point where Well, basically where the boundaries are a little bit blurred. Right. Like I think we you're gonna want someone who's like understands the details of what they're building, but what details those are.

51:57 Will evolve. Kinda like how now, like You're writing Swift, you don't have to Speak assembly. You know, there's a handful of people in the world and it's really important that they exist and like speak assembly.

52:07 Uh maybe more than a handful. Right, but that's like a specialized function that like most Companies don't need to have. So I think we're just gonna naturally see like An increase in layers of abstraction.

52:19 And then the cool thing is now we're we're entering like the language layer of abstraction, like natural language. And the natural language itself is really flexible. Like you could have engineers talking about like a plan, and then you could have engineers talking about a spec, and then you could have engineers talking about just you know, a product or an idea. So I think we can also like start moving up those layers of of abstraction as well. But

52:40 You know, I d I do think this is gonna be gradual. I don't think it's gonna go up to like all of a sudden like nobody ever writes anything and like, you know, any code and it's just specs. I think it's gonna be much more like okay. We've set up our coding agent to be really good at like previewing the build or like at running tests. Maybe that's the first part, right? That most people have set up. And say, Okay, now we've set it up so that it can like execute the build and it can like

53:01 see the results of its own changes, but you know, we haven't yet built a good integration harness so that it can like In the case of Atlas, like by the way, I don't know if they've done any of this or not. I think they've done a lot of this, but You know. Maybe the next stage is like Enable it to like load.

53:13 a few sample pages to see how well those work, right? So then okay, now we're gonna like set up set up to do that. And I think For some time at least we're gonna have humans kind of curating like which of these connectors or systems or components that it the agent needs to be good at talking to. And then You know, in the future there will be an even greater unlock where Codex tells you how to set it up.

53:32 Or maybe sets itself up in a repo. What a wild time to be alive. Wow. I'm curious just the second order effects of this sort of thing, just how quickly it is to build Stuff, what is that?

53:41 do. Does that mean distribution becomes much, much more important? Does it mean uh ideas are just worth a lot more. It's interesting to think about. How quick how that changes. I'm curious what you think. I still don't think ideas are worth as much as Maybe some a lot of people think.

53:58 They still think execution is really hard, right? Like you can build something fast, but you still need to execute well on it. still needs to make sense and be a coherent thing overall. Um Yeah, and distribution is massive. Yeah. Just feels like everything else is now more important. Everything that isn't the building piece, which is

54:13 Coming up with an idea, getting it to market. Profit. All that kind of stuff. I think we might have been in this. Weird temporary phase where

54:23 You know, for a while like you could you could just It was so hard to build product. That you mostly just had to be really good at building product and it maybe it didn't matter if you like had a intimate understanding of a specific customer. Um

54:37 But now I think we're getting to this point where actually like If I could only choose like one thing to understand, it would be like really meaningful understanding of like the problems that a certain customer has. Right, if I could only if I could only go in with one like core competency.

54:52 So I think that that's that's ultimately still what's gonna matter most, right? Like if you're starting a new company today. And You have like a really good understanding and like network of customers that are currently underserved by AI tools, I think you're like you're set.

55:06 Whereas if you're like good at building like You know. Websites. But you don't have any specific customer to build for, I think you're in a in for a much harder time. Bullish on vertical AI pr startups, is what I'm hearing.

55:18 Yeah, I completely agree. There's like, you know, there's like the general thing that can solve a lot of problems, and then there's like we're gonna solve presentations incredibly well and we're gonna understand the presentation problem. Uh better than anyone and we're gonna Uh plug into your workflows and then all these other things that matter for a very specific problem. Okay.

55:35 Incredible. When you think about progress on codex I imagine you have a bunch of Edolls and there's all these public benchmarks. What's something you look at to tell you, Okay, we're making really good progress. I imagine it's not gonna be the one thing, but what do you focus on? What's like something you're trying to push? What's like a KPI or two? One of the things that I'm constantly reminding myself of is that

55:55 A tool like Codex sort of naturally is a tool that you would, you know, become a power user of. Right. And so we can accidentally spend a lot of our time thinking about features that are like very deep in the user adoption journey. Um, and so We can kind of end up over solving for that.

56:10 And so I think it's like just critically important to like go look at like your like D seven retention. Right. Just go try the product. Like sign up from scratch again. Um, I have a few too many like Chat GPT pro accounts that I've just like

56:22 In order to maximally correctly dog food, like sign up for my Gmail and they charge me like two hundred bucks a month. I need to expense those. But uh Uh. You know, like I think just like the feeling of being a user and the early retention stats are still like super important.

56:37 For us. Because it you know, as much as this category is is taking off. I think we're still in the very early days of like people using them. Um Another thing.

56:45 That we do that That may might be I think we might be the most Like user feedback slash social media pills. Team out there in the space. is like a few of us are like constantly on Reddit.

56:58 And Twitter. And uh You know, there's a there's praise up there and there's a lot of complaints, but we take the complaints like very seriously and look at them. I think that Again, because you can use like coding aging for so many different things.

57:11 Um, it often is like kind of broken in many sort of ways for like specific behaviors. Um and so we we actually monitor a lot just like what the vibes are on social media pretty often, especially I think for for Twitter X, um It's a little bit more hypey.

57:28 And then Reddit is a little more Negative but real. Actually. Um so I've started increasingly paying attention to like how people are talking about using codecs on Reddit, actually.

57:39 This is uh important for people to know. Which of the subreddits do you check most? Is there like in our uh codex or I mean the algorithm's pretty good at surfacing stuff, but like R slash codex is Is there. Okay. I'll take Very interesting. And then uh if people tag you on Twitter, you still will see that, but maybe not as powerful as seeing it on Reddit.

57:56 Well the yeah, and the interesting well the thing with Twitter is it's a little bit more one to one, even if it's like in public, whereas like with Reddit does like really good upvoting mechanics. And like Maybe most people are still not bots, unclear. Um so you get you get like good signal on what matters and what other people think. So uh interestingly, uh Atlas, I wanna talk about that briefly.

58:13 Uh you guys launched Atlas. I tweeted actually that I tried Atlas and then I I don't love the AI Only uh search experience as just like I just want Google sometimes or whatever. Like just waiting for to give me an answer. I'm like, I don't wanna and there was no way to switch. I just tweeted, Hey, I'm I'm switching back. I don't it's not a great

58:31 I feel like I made some PMs at OpenAI sad and I saw someone tweet, Okay, we have this now. Which I imagine was always part of the plan. It's probably an example of we just ship We gotta ship stuff, see how people use it, and then we figure it out. Uh

58:43 So I guess one is that I don't know, is there anything there? And two, I'm just curious, why are you guys building a web browser? So I I worked on that list for a bit. Um I don't work on it now. Um But

58:53 You know, like the A bit of the narrative here for for me just to tell my story a bit was like I was working on this like screen sharing. Like pair programming startup. Right, and then we joined OpenAI. And so the idea was really to build a contextual desktop assistant.

59:06 And the reason I believe that's so important is because I think that It's really annoying to have to give all your context to an assistant and then to figure out how it can help you. Right. And so if it could just like understand what you were trying to do, then it could maximally accelerate you. Um and so

59:22 I I I would ar you know, I still think of codex actually as like a contextual assistant. Um from a little bit of a different angle, like starting with coding tasks. But Um The

59:33 Some of the some of the thinking, at least for me personally, I can't speak for the whole product, but was that A lot of work is done in the web. And If we can build a browser Then we can be contextual for you.

59:44 But in a much more first class way. We weren't hacking like Other desktop software which have like very varied support. For for like what content they're rendering to the accessibility tree. Uh we wouldn't be relying on screenshots, which are a little bit slower and unreliable.

59:58 Instead we we we could like be in the rendering engine, right? And like extract whatever we needed to. To help you um And also I like to think of like you know. Video games like

1:00:08 I don't know if you've played like I don't know. See. Hello. Right. Like You walk up to an object. I mean that's true for many games. You press

1:00:15 Man, it's been a long time. This is embarrassing. Prise. X. And it just does the right thing, right? And I was one of those guys who always read the instruction manual for every video game that I bought.

1:00:24 Now I remember the first time I read about a contextual action and I just thought it was like this really cool idea. And uh You know The The thing about a contextual action is we need to know what you are attempting to do.

1:00:34 We'd have a little bit of context and then we can And then we can help. Uh. And I think this is critically important because

1:00:42 You know, imagine this world that we reach, right? Where we're we have agents that are helping you thousands of times per day. Um Imagine if the only way we could tell you that we helped you was if we could like Push notify you. So you get a thousand push notifications a day.

1:00:57 of an AI saying like hey, I did this thing, do you like it? It'd be super annoying, right? Whereas imagine going back to software engineering. Like I was looking at a dashboard and I noticed some like key metric had like gone down. And

1:01:10 You know, at that point in time an AI could like maybe go take a look. And then surface the fact that it has an opinion on why this metric went down and maybe a fix. Right there, right when I'm looking at the dashboard. Right. That would be like that would much more keep me in flow. And enable the agent to take action on like many more things.

1:01:26 So in my mind, like Part of why I'm excited for us to have a browser is that I think we have Then like Much more context. Around.

1:01:35 Like what we should help with. Users have much more control. over what they want us to look at. It's like, hey, if you want to open s if you want us to like take action on something, you can open it in your AI browser. If you don't, then you can open it in your other browser. Right. So like really clear control and boundaries. And then

1:01:49 We have the ability to build UX that's like mixed initiatives so that We can surface contextual actions to you like at the time that they're helpful. As opposed to just like randomly notifying you. Hearing the vision for Codex being the super assistant. It's not just there to code for you. It's trying to do a lot for you as a teammate and as this kind of super teammate and that makes you awesome at work.

1:02:08 So I get this. Speaking of that, are there other non engineering Common use cases for codex. Just ways that non engineers. We talked about it, you know, designers prototyping and building stuff. Are there any

1:02:20 Fine or unexpected ways people are using codecs that aren't Engineers. I mean there's a load of a load of unexpected ways, but I think Like Most of what we're seeing like

1:02:30 real traction with people using things are still for now like very like I would say coding adjacent or like sort of tech oriented. Places where there's like a mature ecosystem. Um, or you know, maybe you're doing data anal data analysis or or something like that. I personally am expecting that we're

1:02:46 Gonna see a lot more of that over time. Um, but for now, like we're keeping the team like very focused on just coding for now'cause it's so much more work to do. For people that are thinking about trying out codex, is there like um Does it work for all kinds of code bases? Uh what what code does it support? If you're like I don't know it SAP, can you add codecs and start building things? What's kinda like the sweet spot where does it start to not be amazing yet?

1:03:11 This I I'm really glad you asked this question actually because The best way to try Codex is to give it your hardest tasks. Which is A little different than some of the other coding agents. Like

1:03:21 Yeah. Some tools you might think, okay, let me like start easy or just like You know, like vibe code something random and decide if I like the tool. Whereas like We're really building codecs to be

1:03:31 the like professional tool that you can give your like hardest problems to. Um and you know that writes like high quality code in your like enormous code base that is in fact not perfect right now. So yeah I think if you're gonna try codex, you wanna try it on like A real task that you have. And not necessarily like dumb that task down to something that's like trivial.

1:03:50 But actually like You know, like A good one would be like you have a hard bug and you don't know what what's causing that bug and you ask codex to like help figure that out. Well like To implement that.

1:03:59 You know the facts. I love that answer. Just give it to your hardest problem. I will say like you know, if you're if you're like, Hey, okay, m well, the hardest problem I have is that I need to build like a new unicorn business, like obviously that Yeah. It's not gonna work.

1:04:11 Uh not yet. So I think It's like Give it like the hardest problem, but something that is still like

1:04:18 One Like question. Right or one task, um to start. That's if you're testing. And then over time you can learn how to use it for like bigger things. Yeah, what languages does does it support? Basically w the way we've trained codex is like there's a distribution of languages that we support and it's like fairly aligned with like

1:04:33 the frequency of these languages in the world. So unless you're writing some like very esoteric language or like some private language, it should do fine in your language. If someone was just getting started. Is there a tip you could share to help them be successful? Like if you could just whisper A little tip.

1:04:48 into someone just setting up codecs for the first time to help them have a really good time. What's something you'd whisper. I might say try a few things in parallel. Right, so you could try giving it a hard task. Um

1:05:00 Maybe ask it to understand the code base. Uh Formate a plan with it. Around an idea that you have. And kinda build your way up from there. And like

1:05:08 sort of the meta idea here is it it's again it's like You're building trust within you teammate. Right. And so like you wouldn't go to a new teammate and just give them like hey, do this thing, uh here's zero context. You would start by like first making sure they understand. The code base and then you would like maybe align on a platinum approach and then you would have them go off and do bit by bit. Right. And I think if you use codecs in that way, you'll just sort of naturally start to understand like the different ways of prompting it. Because it is

1:05:32 It's a super powerful like agent. And model? But it is it is a little bit different to Prov Codec than other models. Just a couple more questions. One

1:05:40 We touched on this a little bit. As AI does more and more coding, there's always this question of should I learn to code and why why should I spend time doing this sort of thing? For people that are Trying to figure out. what to do with their career, especially if they're in to software engineering computer science.

1:05:56 Do you think there's specific elements of computer science that are mo more and more important to lean into? Maybe things they don't need to worry about. Like what do you think people should be leaning into skill wise? in as this becomes more and more of a thing. In our workplace. I think there's like a couple

1:06:13 Angles you could go at this from. Um I think the Well, The easiest one to think of, at least, is just like

1:06:21 Be a doer of things. Um I think that you know with Coding agents. um getting better and better over time. It's just What you can do as

1:06:31 Even like someone in college or a new grad is just like so much more than what that was before. And so I think you just want to be taking advantage of that. Definitely when I'm looking at like hiring folks who are earlier career, it's like definitely something that I think about is how How productive are they using the latest tools?

1:06:48 They should be like super productive. And If you think of it in that way, they actually have like less of a handicap than before. versus an a a more senior career person because You know, the divide is actually getting smaller because they've got these amazing coding agents now.

1:07:01 Um so that's one thing, which is like I guess the thing the advice is just like Learn about whatever you want, but just make sure you spend time doing things, not just like fulfilling homework assignments. I guess. I think the other side of it though is that It's still

1:07:14 Deeply worth understanding. Like what makes a good like overall software system. So I still think that like skills like really strong systems engineering skills. Or

1:07:25 Even like really effective like communication and collaboration with your team. Skills like that, I think, are Are important, they're I mean continue to matter. for for quite some time. Like I don't think it's gonna be like

1:07:36 All of a sudden Uh The AI coding agents are just able to build like perfect systems without your help. I think it's gonna look much more gradual where it's like Okay, we have these AI coding agents.

1:07:48 They're able to validate their work. It's still important and like that for example, like I'm thinking of an engineer who was working on Atlas since we were talking about it. He set up codecs so that it can like verify its own work, which is a little bit non trivial because of the the nature of the Atlas project. So the way that he did that. was he actually prompted Codex like hey why can't you verify your work?

1:08:05 Fix it. And like did that on a loop. Right. And so You still Like at various phases are gonna want a human in the loop to like help configure

1:08:14 The coding agent to be effective. And so I think like You still want to be able to reason about that. So maybe it's like less important that you can like type really fast and like you understand exactly how to write.

1:08:25 Not that anyone writes a f you know, four each loop or something, right? But It is Or you know, you don't need to know how to implement like a specific algorithm. But I think you need to be able to reason about the different systems and like what makes like effective. A software engineering team effective.

1:08:38 So I think that's But the other really important thing and then like maybe the last angle that you could take is I think If you're on the frontier of knowledge for a given thing. I still think that's like deeply interesting to go down, partially because

1:08:51 That knowledge is still gonna be like Uh You know, agents aren't gonna be as good at that. But also partially because I think that like by trying to advance the frontier of a specific thing, you'll actually like End up like

1:09:03 being forced to take advantage of coding agents. And like Using them to accelerate your own workflow as you go. What's an example that when you when you talk about being at the frontier some Codex writes a lot of the code that helps like manage its training runs.

1:09:15 the infrastructure Uh Yeah, we move pretty fast and so We have a codex code review is like catch a lot of mistakes. It's actually caught some like pretty interesting configuration mistakes. And uh

1:09:26 you know, we're starting to see glimpses of the future where We're actually starting to have codex. Even like be on call for its own training. Which is pretty interesting. Um so there's lots there.

1:09:37 Uh wait, what does that mean to be on call for its own training? So it's running, it's training and it's like oh something broke. Someone needs and it It does it like alert people or it's like here, I'm gonna fix the problem and re restart. This is an early idea that we're like figuring out. But the basic idea is that, you know, during a training run, there's like a bunch of graphs that like today like humans are looking at, and it's like really important to like look at those. Um we call this babysitting. Because it's very expensive to train, I imagine, and very important to move fast and Exactly. And there's a lot of there's a lot of systems underlying uh the training run. And so like a system could go down or there could be an error somewhere that gets introduced.

1:10:10 And so we might need to like fix it or pause things or I don't know, there's lots of actions we might need to take. And so Basically having codex like run on a loop to like evaluate. How those charts are moving over time.

1:10:21 um the sort of this idea that we have to like how to enable us to like train like way more efficiently. I love that. This is very much along the lines of this is the future of agents. It's codex isn't just for building code and right, it's It's a lot more than that. Yeah.

1:10:36 Okay, last question. Uh Being at OpenAI, uh, I can't not ask about your AGI timeline and how far you think we are from AGI. I know this isn't what you work on. But there's a lot of opinions, a lot of I don't know.

1:10:49 Timelines. How far do you think that we are from a Alright. humanly human version of AI, whatever that means to you.

1:10:56 For me, I think that It's a little bit about like when do we see the acceleration curves kinda go like this, or I don't know which way I'm mirrored here. Right. When do we see the hockey stick? And I think that the current

1:11:08 limiting factor. I mean there's many, but I think a current underappreciated limiting factor is like literally human typing speed. Or human multitasking speed. Unlike writing prompts. Right. And like you know, you were talking about it's like you can have an agent like watch all the work you're doing, but if you don't have the agent

1:11:24 Uh also validating its work. then you're still bottlenecked on like, can you go review all that code, right? So my view is that We need to um Unblock.

1:11:34 those productivity loops from like humans having to prompt and humans having to like manually validate all the work. And so if we can like Rebuild systems to let the agent like be default useful. We'll start unlocking hockey sticks. Unfortunately, I don't think that's gonna be binary. I think it's gonna be very dependent on what you're building, right? So like

1:11:52 I would imagine that like next year, if you're a startup. And you're building a new new piece of like you know, some new app or something. It'll be possible for you to set it up on a stack where agents are like much more self Sufficient. Than not, right?

1:12:05 But now let's say I I don't know, you message SAP, right? Let's say you work in S A P Like they have many like complex systems and they're not gonna be able to just like get the agent to be self sufficient overnight. In those systems. So they're gonna have to slowly like maybe replace systems or update systems. to allow the agent to like handle more of the work end to end. And so

1:12:23 Basically my sort of long answer to your question, maybe boring answer. is that I think starting next year we're gonna see like early adopters like starting to like hockey stick their productivity. Um and then over the years that follow, we're gonna see larger and larger companies like hockey stick that productivity. And then somewhere In that fuzzy middle.

1:12:40 is like when that hockey sticking will be like flowing back into the AI labs, and that's when we'll we'll basically be at the AGI. Yeah. I love this answer. It's very practical and it's something that comes up a lot on this podcast, just like The time to review all all the things AI is doing is really annoying and a big bottleneck. I love that you're working on this.

1:12:59 Because it's one thing to just make coding. much more efficient and do that for people to another to take care of that final step of okay. Is this actually great? And that's so interesting that your sense is that's the limiting factor. It comes back to your earlier point of Even if AI did not advance anymore.

1:13:15 We have so much more potential to unlock if we Yeah. as we learn to use it more effectively. Uh so that is a really unique answer. I haven't heard that perspective on what is the big unlock. Human typing speed to review basically what AI is doing for us. Mm-hmm. So good.

1:13:31 Okay. Uh Alexander, we covered a lot of ground. Is there anything that we haven't covered? Is there anything you wanted to share, maybe double down on before we get to our very exciting lightning round? I think uh one thing is that the codex team is growing.

1:13:47 And uh as I was just saying We're still somewhat limited by human thinking speed and human typing speed. We're working on it. So um If you're an engineer.

1:13:56 Um, or a salesperson or I'm hiring for product uh product person. Uh please hit us up. I'm not sure the best way to give contact info, but I guess you can go to our jobs page or do they have contact for you? Actually, do you do listeners have contact for you? Like, hey, I wanna apply it to Codex? No. Uh I do have a contact forum at Lenny Rachitsi.com. I'm afraid of all the amazing people that are ping me, but there we go. We could try that. Let's see how that works. Yeah, or another maybe an easier version. We can edit all that out. Up to you.

1:14:24 But uh yeah, or I would just say you can drop us a DM uh for example, I'm M B Rico on Twitter and Yeah. If you're interested in joining the team. What a dream job for so many people. What's a sign they

1:14:37 I don't know. What's like a way to filter people a little bit so they're not fletting your inbox. So specifically if you want to join the codex team, then you need to be a technical person who uses These tools and I think I would just ask yourself the question. Uh hey, let's say you know I work to join open AI and work on codex.

1:14:55 Over the next six months. You know, and crush it. what does the life of a software engineer look like then? And I think if you have an opinion on that, you should apply and if you don't have an opinion on that and have to think about it first. you know, depending on how long you think about it, I guess that'll be the filter, right? Like I think

1:15:11 There's a lot of people thinking about the space, and so we're We're very interested in folks who sort of of already being thinking about like what the future should look like with agents and like we don't have to agree on where where we're going. I think we want people who like are very passionate about the topic, I guess.

1:15:28 It's very rare to be working on a product that has this much impact and is at such a bleeding edge of where it's possible. It's uh what a cool role for the right person. So uh um it's awesome that you have an opening and This audience is Uh a really good fit potentially.

1:15:44 for for that role. So I hope we find someone. That would be incredible. With that we've reached our very exciting lightning round. I've got five questions for you, Alexander. Are you right? I don't know what these are, but I'm excited. Let's do it. Uh they're uh The same questions ask everyone except for the last one.

1:16:01 So uh probably not a surprise. I should probably make them more more often a surprise. Okay, first question, what are a couple of books that you recommend most to other people? Two or three books that come to mind. I have been reading. A lot of science fiction recently.

1:16:16 And I'm sure this has been recommended before, but the culture. I think it's Ian Banks is the name of the author. Part of why I love it is because it's like Basically Relatively recent writing.

1:16:28 About a future With AI. But it's an optimistic feature with AI. Um, and I think you know, a lot of sci fi is like fairly dystopian. Um but this is like

1:16:38 People uh the sort of the joke, at least on the s culture subreddit. Is that let me let me see if I can get this right. It is a Like space communist utopia. Or or like I think it's a gay space communist utopia. Um and uh I just think it's like really fun to think about.

1:16:55 Um Like to use the culture as a way to think about like what kind of world can we usher in and like what decisions can we make today to help usher in that world. Well I've not I don't think anyone's recommended that. I know you're reading you mentioned before we started recording Lord of the Rings right now. Uh if you want another AI ish Sci fi book. Uh have you read Fire Upon the Deep?

1:17:15 No, I haven't. Okay. It's uh Incredibly good. It's like uh a sci fi space opera sort of epic tale. with uh superintelligence. So

1:17:25 Yeah. Someone Mostly not optimistic, but somewhat optimistic. Okay, next question. Is there uh favorite recent movie or TV show that you've really enjoyed. Yeah, there's an anime called Jujutsu Kaisen.

1:17:38 Which I really like. Um Again, it's got a kind of a slightly dark topic of like demons. Um But what I love about it is that the hero is really nice and I think there's this new wave of like

1:17:50 Anime and cartoons were The protagonists are really friendly and like people who care about the world rather than being like Sort of. Like if you look at like some older anime like that started the genre, like you know, there's this like

1:18:05 Evangelian. uh or Akeeda and like those characters, the protagonists are like Deeply flawed, like quite unhappy. Um Yeah, they didn't start the genre, but it was like a trend for a while to sort of

1:18:18 poke fun at the idea that in these in these cartoons the protagonist was very young, but being given a ridiculous amount of responsibility to like save the world. And so there was kind of a a wave of like Uh content that was like critiquing this by making the character like basically go through like serious like mental issues. In the middle of the show.

1:18:36 Um, and I'm not saying this is better, but at least it's quite fun to have like these like really positive protagonists. Or just trying to help everyone around them. I love how much we're learning about your uh personality hearing these recommendations. Uh nice protagonists, optimistic futures. Uh I think you know you if you don't believe it, you can't rule it into existence. So

1:18:57 This is your training data. Is there a product you recently discovered that you really love? To the an app. Could it be some clothing, could be some kitchen gadget. Tech gadget.

1:19:08 Uh-huh. Yeah, so I have been like quite into Uh you know, combustion engines. Um

1:19:16 And cars. Акції the reason I came to America. Initially was because I wanted to work on like US aircraft. Um But you know, now I work in software.

1:19:24 Um, and so for the longest time I've basically only had like quite old sports cars. Uh all just'cause they were more affordable. Um And then uh recently Um

1:19:36 We got a Tesla instead. And I have to say that I find the Tesla software like quite inspiring. Um in particular it has the self driving feature and You know, I I've mentioned a few times like Today like

1:19:49 I think it's really interesting to think about how to build like mixed initiative software that makes you feel maximally empowered as a human. Mm. maximally in control, but yet you're getting a lot of help. And I think they did a really good job. with enabling

1:20:02 But all these different ways that you can adjust what it's doing without turning off the self driving. So like you can accelerate, and you know it'll like listen to that. You can turn a knob, change its speed. You can steer slightly Um I think it's a it's actually a master class in like building an agent.

1:20:19 That Still leaves the human in control. This reminds me of Nick Curley's whole uh Mantra was Are we maximally accelerated? Yeah. Which makes sense. The tracks.

1:20:31 Uh two more questions. Do you have a life motto? That you often think about and come back to in work or in life that's been helpful. I don't know if I have a life model, but maybe I can tell you about the number one value

1:20:44 company value from my startup. Love it. Which is still something that sticks with me, which is to be kind and candid. That tracks. Kind and candid. Wow.

1:20:54 Yeah, and we had to put them together because We as founders realize that we Often would be nice. And it

1:21:05 wasn't actually the right thing to do. We would like delay the difficult conversations and we were not candid. And so every time we would like remind ourselves of this motto and then we would become more candid. And then six months later we would realize that we were in fact not candid six months ago. And we need to be even more candid. So then the question is like okay, like

1:21:22 H how should we be candidal? It's like okay, well Let's let's think of being can it as an act of kindness, but also think of that both in terms of doing it and willing ourselves to do it, but also in terms of how we frame it as people. It is a beautiful. uh way of summarizing how to how to lead well. The book about dare uh

1:21:39 Challenge directly, but care deeply. Uh radical candor. Yeah, yeah, right. Yeah. So it's like another way of thinking about radical candor. Okay, last question. I was looking up your last name, just like hey. What's the what's the story here? So your last name is Embiricos. And I was talking to Jet GPT.

1:21:55 And it told me the most famous individuals with the surname are the influential Greek poet and psychoanalyst Andreas Embiricos. And his relative, the wealthy shipping magnate and art collector George M. Dyerkos. So the question is which of these two do you most

1:22:12 identify with. The Greek poet and psychoanalyst Or the wealthy shipping magnate and art collector. I think it's it's gonna have to be the poet because Uh

1:22:22 He uh He loved the island that our family's from. Wait, you know the city people. Okay. This is not news to you. Okay. Well, I mean it's an enormous family, but it's like Greek, so you know, these big families, everyone like everyone's your uncle. You know what I mean? Like my mother's Malaysian and also like everyone is my uncle and or aunt in Malaysia too, if that makes sense.

1:22:42 Yeah. But Yeah, he he loved this island that the family sort of like initiated from I believe

1:22:49 I don't actually know where that shipping my nate lived. I think it was New York or something, but Anyway, we all came from this island called Andros. Um, which is a really beautiful place. And it's like there's more like livestock there than than humans. Uh, not too many tourists go there. Uh, but I think he like part of what I think is really cool is like he published a lot and a lot of his writing is about like

1:23:09 The beauty of that island. Which I think is super cool. Wow. That was an amazing answer. Two more questions. Where can folks find you if they want to follow you online and, you know, maybe reach out and then how can listeners be useful to you? I I'm one of those people who has social media only for the purposes of having work.

1:23:24 You know, my phone my phone turns black and white at like nine PM at night. Uh but yeah, so it's Twitter or X uh at N Rico. Um And uh yeah, if you post an R slash codex, I'll probably see it.

1:23:36 Uh So you know you can go there. Um How can listeners be useful? Um, I would say please try codex.

1:23:44 Please share feedback? Let us know what to improve. We pay a ton of ten Ton of attention to feedback. I think it's like Honestly like The growth has been amazing, but it's still very early times.

1:23:53 Um, so we still pay a lot of attention and hope to do so forever. Um, and also um I would say if you're interested in in working on the future of coding agents and then agents generally then. Please. Uh.

1:24:06 apply to our job site um and or message me in those social media places. Alexander, this was awesome. I always love meeting. People working on AI because It always feels like this very I don't know. sterile, scary, mysterious thing. And then you meet the people building these tools and they're always just so awesome.

1:24:24 And you especially, just so nice and Uh As you like the examples you shared, uh Optimism and kindness. You know, this is what We want to be

1:24:33 This are these are the kinds of people we want to be building these tools that are gonna drive the future. So Um I'm I'm really thankful that you did this. Um Grateful to have met you and uh thank you so much for being here.

1:24:45 Yeah, thanks so much for having me. This is fun. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show. at Lenny's podcast dot com.

1:25:08 See you in the next episode.

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