Why humans (not compute) are AI’s biggest bottleneck | Alexander Embiricos (OpenAI)

Do lead work on Codex. Codex is Open'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 Carpot that he tweeted the gnarliest bugs that he runs into that he just spends hours trying to figure out. Nothing else has solved. He gives it to Codex, lets it run for an hour and it solves it.

Starting to see glimpses of the future where we're actually starting to have Codeex be on call for its own training. Codex writes a lot of the code that helps like manage its training run, the key infrastructure. And so we have a codeex code review is like catching a lot of mistakes. It's actually caught some like pretty interesting configuration mistakes. One of the most mind-blowing examples of acceleration, the Sora Android app, like a fully new app. We built it in 18 days and then 10 days later, so 28 days total, we went to the public.

How do you think you win in this space?

One of our major goals with Codex is to get to proactivity. If we're going to 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 to build any agent, maybe you should be building a coding agent.

When you think about progress on codecs, I imagine you have a bunch of evals and there's all these public benchmarks.

A few of us are like constantly on Reddit. You know, there's a there's praise up there and there's a lot of complaints. What we can do 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 makes it more unclear what you should do as the human. Being at OpenAI, 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 Emiros, product lead for Codeex, OpenAI's incredibly popular and powerful coding agent. In the words of Nick Turley, head of Chachi BT 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 CPO, said, "Alex is simply the best." In our conversation, we chat about what it's truly like to build product at OpenAI. How Codeex allowed the Sora team to ship the Sora app, which became the number one app in the app store in under one month. Also, the 20x 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 Baze, Nick Turley, and Dennis Yang 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 of 19 incredible products, including a year free of Devon, Lovable, Replet, Bolt, Nadam, Linear, Superhum, Descript, Whisper Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, Champardd, Mob and Post Hog, and Stripe Atlas. Head on over to lenniesnewsletter.com and click product pass. With that, I bring you Alexander and Biricos after a short word from our sponsors.

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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. I want to start with your time at Open AI. So, you joined OpenAI about a year ago. Before that, you had your own startup for about 5 years. Before that, you were a product manager at Dropbox. 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 OpenAI are just like dramatically more than what I can imagine. 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 at OpenAI just kind of like made me reimagine what he 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." 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 codeex itself. I think it's a while since we bumped our external number, but like you know it's like the the 10xing of Codeex's scale 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 speaking for myself like having lived through that now I feel like anytime I'm going to 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. 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 and then pivot. But I think one thing I've realized at OpenAI 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 I have to be like way more ruthless with how I spend my time.

Before we get to codeex, is there a way that they've structured the org or I don't know the way that open operates that allows the team to move this quickly because everyone everyone wants to move super fast. I imagine there's a structural approach to allowing this to happen.

I mean, so one thing is just the technology that we're building with has like just transformed so many things, you know, from like both how we build but also like what kinds of things we can enable for users. And you know we spend most of our time talking about like the sort of improvements within the foundation models but I I believe that even if we had 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 is ripe if that makes sense.

But I think there's a lot of sort of counterintuitive things that surprised me when I arrived 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 kind of rally the ship and it was kind of like make sure you're pointed in the right direction and that you can like accelerate in that direction. 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 technically and then we also don't know what's going to land even if it works technically. 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 to be incredibly bottoms up. 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 they're bottoms up or at least a lot of people do, but OpenAI 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 it'll even make sense to work at a nonAI company in the future. I don't even know what that means. But if I were to imagine it or go back in time, I think I would like run things totally different.

What I'm hearing is kind of this uh ready, fire, aim uh is the approach more than ready, aim, fire. And this something and as you processed that because 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 shared 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. It's like to 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 kind of like roughly what do we think can happen? like someone um I've learned a ton from working here is a is a research lead and he likes to say that like in 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 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 far as like aiming, I think we want to know like, okay, 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 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 saying, "Okay, we're gonna be bottoms up. We're gonna try a bunch of stuff. We're not going to 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 this successful at Bottoms Upwork. it just super resonates basically.

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 everyone here has. So I think like the way that OpenAI runs like many you can't like read this or be on listen to a podcast and be like I am I'm just going to 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. So it might need to be like adjusted if you were going to implement this.

Okay. So let's talk codeex. You lead work on codeex. How's codeex going? What numbers can you share? Is there anything you can share there? Also just not everyone knows exactly what codeex is. Explain what codeex is.

Totally. Yeah. So uh I have the very lucky job of of living in the future and leading products on codeex. Um and codeex is open coding agent. So super concretely that means it's an IDE extension 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 codeex to answer questions about code write code 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 life cycle which is all about uh you know writing code that you're going to get into production. Uh more broadly we think of codeex as like it's the what it currently is is just the beginning of a software engineering teammate. 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 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 Sentry unless you ask it to. And so like no matter how smart it is, like how much are you going to 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 want to get to the point where you know 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 going to be able to iterate their way there.

I thought the way uh I thought the point about not reading Slack in Dave Dog was it's just not distracted. It's just constantly focused and 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 human teammates, like you don't tell them what to do,

right? Like maybe when you first hire them, you have like a couple meetings and you're like, "Hey, like you kind of 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. You delegate a few tasks. But then eventually you just say like, "Hey, great. Okay, you're working with this set of people in this area of the codebase. You know, feel free to work with other people in other parts of the codebase too even." And 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 Codeex is to like get to proactivity. I think this is this is like critically important to like achieve the mission of OpenAI which is to deliver the benefits of AI to all humanity. You know, I like to joke today that like AI products 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. 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, it's thousands of times per day. And so a large a large part of our our goal with codeex is to figure out like what is the shape of an actual teammate agent that is sort of helpful by default.

When people think about cursor and uh even cloud code, it it's like a IDE that helps you code and kind of autocompletes code and maybe does some agentic work. What I'm hearing here is the vision is 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 IDE autocomplete and things like that. Is that is that a kind of a differentiator in the way you think about codecs?

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 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 Codex is doing?

Yeah, it's been Codex has been growing like absolutely explosively um since the launch of GPT5 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, the last the last stat we shared there was like we we were like well over 10x since August. In fact, it's been like 20x since then. Um, also the codex models are serving many 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 codeex team uh was to build a you know really tightly integrated product and research team that are iterating on the model and the harness together. And it turns out that lets you just do a lot more and try many more experiments as to how these things will work together. 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. And so we've reached a point where actually the codeex model is the most served coding model in the API as well.

You uh hinted at this uh what unlocked this growth? I am extremely interested in hearing that. It felt like before, I don't know, maybe this was before you joined the team. It just felt like cloud 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 Carpathy tweeted that he just like has never seen a model like this. He I think the tweet was the gnarliest bugs that he runs into that he just spends hours trying to figure out. Nothing else has solved. He gives it to Codeex, lets it run for an hour, and it solves it. What What did you guys do?

We have this strong sort of mission here at OpenAI to you know basically to build AGI. Um and so we we think a lot about what how can we shape the product so that it can scale right you know earlier I was mentioning like hey like if you're an engineer you should be getting help from from AI like thousands of times per day right and so we thought a lot about the primitives for that when we launched our first version of codeex uh which was Codex cloud and that was basically a product that had its own computer lives in the cloud you could delegate to it and you know the sort of the coolest part about that was you could run many many tasks in parallel But some of the challenges that we saw are that it's a little bit harder to set that up both in terms of like environment configuration like giving the model the tools it needs to validate changes and to learn how to prompt in that way. And sort of my 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 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. 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 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 codeex today is either they download an IDE extension 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 to help that be safe and secure 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 really strong feedback loop using the model and then over time like our team's job is to like help turn that feedback loop into you sort of as a byproduct of using the product configuring it so that you can then be delegating to it down the line and again analog you keep coming back to it but like if you hire a teammate and you ask them to do work but they you just give them like a fresh computer from the store it's going to 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. Here's the password for this 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 the initial version of Codeex was almost too far in the future. 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 come back a little bit. Let's integrate into the way engineers already integrate into IDs and locally and help them kind of on ramp to this new world.

Totally. And this was it was quite interesting because we we dog food product a ton at OpenAI. So you know dog food as in we use our own product and so Codex has been accelerating OpenAI over the course of the entire year and the cloud product 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 we train reasoning models all day and so we're very used to this kind of prompting thing and like you know think up front run things massively in parallel and uh 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 a ton of signal from dog footing internally but 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 open 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 helped Codeex 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 I guess you know you were mentioning models and the models have improved a ton. In fact um just last Wednesday we shipped GPD 5.11 CEX Max a very you know accurately named model. Uh that is that is awesome. It is awesome both because it is um for any given task that you were using GPD 5.1 codecs for it's like you know roughly uh 30% 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. 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 you know obviously there's a ton going on in the market right now, but like Codex Max is definitely like carrying that mantle of uh, you know, tackling the hardest bugs. Um, so that is that is super cool. But I will say it's like some of what how we're thinking about this is evolving a little bit from being like yeah we're just going to think about the model and like let's just like train the best model to really thinking about like what is an agent actually overall right and you know I'm not going to 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 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. And both of those things also have a really big role here. So for instance, one of the things uh that we're really proud of is you can have GP5.1 CX max 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. But now routinely we'll hear about people saying like yeah, it ran like overnight or it ran for 24 hours.

and so you know for a model to work continuously for that amount of time it's going to 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. So you need to have a model that has a concept of compaction and knows like okay as I start to approach this context window I might be asked to like prepare to be run in a new context window. And then 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 codecs actually been working across all three things. And I think that's like increasingly going to 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 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 bespoke tools or maybe you have like in our case a strong opinion that it should just use like 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 built codeex is that it just uses the shell. 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 Russian is just like we're building all three things in parallel and like kind of tuning each one 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 this space? Do you think it it'll event it'll always be this kind of like race with other models constantly kind of leaprogging 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 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. But even a teammate you know like if you think again an engineering teammate they can also like schedule a calendar invite right or move standup or do whatever right. And so in my mind, if we just imagine that every day or every week some like crazy new capability is just going to 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 you kind of just have like an AI teammate or super assistant that you just talk to and it just knows how to be helpful like on its own, right? And so you don't you don't have to be like reading the latest tips for how to use it. You 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 my head at least I have is that we build you know maybe a 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 a teams or in Slack with a teammate, chat is pretty good. I can ask for whatever I want, right? It's like it's kind of the the common denominator for everything. So you can chat with a super assistant about whatever topic you want, whether it be coding or not. And then if you are like a functional expert in a specific domain such as coding, there's like a guey 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 as open AI is basically this idea of like you have chat chatpt PT and that is 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. And so then you get to work and you just can naturally just yeah I'm just going to ask it for this and I don't need to know about all the connectors or like all the different features. I'm just going to 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, you know, that's how we we really build like the winning product.

This is so interesting because with the my chat with Nick Charlie, the head of chat JPT, I think he shared that the original name for Chat JPT was super assistant or something to that effect.

Yeah.

And it's interesting that there's like that approach to the super assistant and then there's this codeex approach. It's almost like the B TOC version and the B2B 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 in 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 ChatGpt in a sense. Or is there or is there something else there?

Yeah. So, you know, so we're getting to the like 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 in like the plausible way we get there, but as for how it happens, who knows? So basically, if we're going to build a super assistant, it has to be able to do things, right? So like we're going to have a model and it's going to be able to do stuff affecting your world.

And one of the learnings I think we've seen over the past year 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, 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 is you could point and click. That's a little slow, you know, and, uh, unpredictable sometimes. 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 nontechnical 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? Right? So I think that what we're doing with codeex is we're building a software engineering teammate. And as part of that, we're kind of building an agent that can use 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 codeex for like coding adjacent product purposes. And so 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 CHCH PT? In my mind, like just coding is a core competency of any agent, including Chach PT. And so like what really what we think we're building is like that competency. But 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 right because if if we you know one very reductive view we could have for an agent is it's just going to be given a computer and it's just going to like point and click and you know go around 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 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 or they want to know that the agent understands 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 sub team is probably has a different meta prompt for like how they want the crashes to be analyzed ized, 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 stuff that the agent does often, we probably just want to 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 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. 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.

Yeah. It feels like you're in the uh Karpathy 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

uh ton of value,

right? Yep.

And then I think like agents outside of coding, it's still like very early and you know, this is just my opinion, but I think they're going to get a whole lot better once they can use coding too and like in a composable way. This is it's kind of the fun part of like when you're building for software engineers. Like I at 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 and thinking about how to use the technology. 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 because a lot of people building for engineers get really annoyed because the engineers are so they're just always complaining about stuff. They're like, "Ah, that sucks. Why'd you build it this way?" I love that you enjoy it, but I think it's probably because you're building such an amazing tool for engineers that can actually solve problems 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? Uh clearly the way you're describing it is it's a teammate. It's going to work with you, make you more superhuman. It's not going to replace you with the way you just think about the impact on the field of engineering having this 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 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 pre AAI, right? But as we make code even more ubiquitous, it's actually just going to be used for many more purposes. And so there's just going to be a ton more need for people with this like humans with this competency. 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 kind of see how it pans out. But I think what we can do 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 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 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. is so then you end up reviewing AI code, right? And that's often a less fun part of the job for many software engineers, right? And so I actually think like we see that like this this comes out 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 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 Irw written code. Okay, cool. You know, another thing we can do is we can make it so that the agent's like better able to validate its work. And you know, it

gets all the way down into like micro decisions. Like if you're going to have the an 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 pane 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? Right? And you know, I think if you're thinking about this from the perspective of like, how do I empower the human? How do I make them feel as accelerated as possible? Like you obviously see the image first, right? You shouldn't be reviewing the code unless first, you know, you've seen the image, unless maybe it's being like reviewed by an AI and now it's time for you to take a look.

When I had uh Michael Charel, the CEO of Cursor on the podcast, he he had this kind of vision of us moving to something beyond code. And I've seen this rise of something called spec-driven development where you kind of just write the spec and then the code, you know, the AI writes code for you. And so you kind of 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 going to be this higher level of abstraction that we focus on?

Yeah, I mean, I think 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 prompt to patch, right? We're starting to see people doing like spec-driven development or like plan-driven development. That's actually one of the ways when people ask like, "Hey, how do you run Codex 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 it 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. I think spec-driven development is like an interesting idea. It's not clear to me that it'll work out that way because a lot of people don't write like don't like writing specs either, but it seems plausible that some some people will work that way.

You know, like a 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, I'm coming up with this on the spot, so it's, you know, not a good name, but like chatter-driven development where it's 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 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 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. I don't want to have to write a spec for that, right?"

>> I have this sort of uh hypothetical future uh that I like to share sometimes with people as a provocation, which is like in a world where we have like truly amazing agents, like what does it look like to be a solopreneur? Um, and uh, you know, one terrible idea for how it could look is that it's actually there's a mobile app and um, every idea that the agent has to do is just like vertical video 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. So in this world like basically what your job is just to like plug in this app into like every single like signal system, 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 watching and right listening to you paying attention to the market, your users, and it's like, "Cool, 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 they're communicating with you in like the lowest like the gyms, like the modern way to communicate.

>> Yeah.

>> Swipe left or right and in vertical feed and then the Sora video. Okay. So I see how this all connects now. I see.

>> Yeah. To be clear, we're not building that, but like, you know, it's a fun idea. I mean, you see, you know, 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 really interesting thing is like if we think about like what is the most successful like AI product to date, um, I would argue, um, it's funny actually, not to confuse things at all, but like the first time we used the the brand Codex at OpenAI was actually the model powering GitHub Copilot. This is like way back in the day, years ago. And so we decided to reuse that that brand recently um because it's just so good, you know, Codex code execution. But I think actually like autocompletion and IDEs is like one of the most successful AI products to date. And part of what's so magical about it is that when 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, 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 OpenAI as we're building. So for instance, you know, 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 is we can then like contextually surface like ways that we can help you as you're going about your day, right? And so we break out of this like, you know, we're just looking at code or we're just in your terminal 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, you know, how can we help you with that?"

>> Man, there's so much there and I love this. Okay, so autocomplete on web with the browser. That's so interesting. 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. I I get it now. Okay. And then this chatter, what is a chatter-driven development? Uh, I had a No, this is a really good idea, but it reminds me I had John Gon on the podcast, CTO of Block, and they they have this product called Goose, which is their own internal agent thing. And he talked about an engineer at Block just uh has Goose watch him with like his screen and listens to every meeting and proactively does work that he should 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, 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 to do. Yeah.

>> Yeah. So, like we see this now, like we have a Slack integration for Codex. People love, you know, if there's like some thing that you need to do quickly. People just like at mention Codex, 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 using Codex a ton to just like answer questions like, "Why do you think this metric moved? What happened?" So questions, you you get the answer right back in Slack. It's amazing, super useful. But when it's as for when it's writing code, 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 world, I think we 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, an engineer for 10 years. 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 code and just have to go through and be on the hook if it is doing something dumb that's going to 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 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 100 hours to review. Who's going to go through all that?"

>> Right. Yeah.

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What has the impact of Codex been on the way you operate as a product person, as a PM? It's clear how engineering is impacted. Uh, code is written for you. What has it done to the way you operate, the way PMs operate at at OpenAI?

Yeah, I mean, I think mostly I just feel like much more empowered. Um, 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, dogfood the product. But even beyond that, I I I just feel like I can do much, much more as a PM. And, uh, you know, Scott Belsky talks about this idea of like compressing the talent stack. I'm not sure if I've phrased that right, but it's basically this idea that like maybe the boundaries between these roles are a little bit like less needed than before because people can just do much more. 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, 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 know just ask Codex for thoughts on that. Uh, a lot of like PM-type work, understanding what's changing, again, just ask Codex for help with that. Uh, prototyping is often faster than writing specs. This is something that a lot of people have talked about. I think something that I don't think it's super surprising, but something that's slightly surprising is like we see like we're mostly building Codex to write code that's going to be deployed to production, but actually we see a lot of throwaway code written with Codex now. It's kind of going back to this idea of like, you know, ubiquitous code. So you'll see, 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 would that's just like too annoying to do in the past, but now it's just like totally worth 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, well, like a designer basically wanted to build an animation, and this is the coin animation in 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 use the animation editor to build the animation, which they then checked into the repo. Actually, our designers are there's a ton of acceleration there. And like speaking of compressing the talent stack, I think our designers are very PM. So, you know, they they do a ton of product work. And like they actually have like an entire like vibe-coded 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 a designer 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 or vibe-engineer that prototype into an actual PR to land. And then depending on their comfort with the codebase, like Codex CLI in 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, you know, we recently shipped the Sora Android app. Um, and uh, that was one of the most sort of mind-blowing examples of acceleration actually because usage of of Codex internally at OpenAI 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, 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 18 days. It went from like zero to launch to employees and then 10 days later, so 28 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, I would say. It was like a little bit, 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, so you've already figured out like some of the underlying like APIs or systems, asking Codex to 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 were basically having Codex go look at the iOS app, 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 to 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, this just boggles the mind. Okay. So

>> Yeah. So imagine releasing the number one app on the app store with like a handful of engineers.

>> Uh, I think it was like

>> Two or three, possibly.

>> Uh, in a handful of weeks. Yeah, this is absurd. So

>> Yeah, so that's a really fun um example of uh acceleration. And then like Atlas was the other one that I think um Ben did a podcast, the the engineer on Atlas uh sharing a little bit of how we built there. You know, many Atlas is is actually, I mean, it's a browser, right? And building a browser is really hard. Um, and so we uh had to build a lot of difficult systems in order to do that. And basically we got to the point where that team has a ton of power users of Codex right now. And um, you know, it got to the point where they they basically were, you know, we were talking to them about it because a lot of those engineers are people I used to work with before at my startup. And so they'd 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." So massive acceleration there as well. And what's quite cool is that uh, you know, 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 admittedly earlier, like just the model we we shipped last week is the first model that natively understands PowerShell. So, you know, PowerShell being uh the native like shell language on Windows. So yeah, it's been it's been really awesome to see like the whole company getting accelerated by Codex, like from and, you know, most obviously also research and like improving how quickly we train models and how well we do it, and then even like uh design as we talked about and and marketing, like actually we're at this point now where my product marketer is often also making string changes just directly from Slack or like updating docs directly from Slack.

>> These are amazing examples. You guys are living at the bleeding edge of what is possible and this is how other companies are going to work. Uh, just shipping again, what became the number one app in the app store and just beloved all over the, it just like took over the, I don't know, the world for at least a week. Built, you said in 28 days and like, I don't know, 10 days, 18 days just to get like the core of it working.

>> Yeah. So like 18 days we had a thing that employees were playing with and then 10 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 what they use Codex for and it's basically like, "We use Codex for absolutely everything." I was like, "Okay, well like, you know, how would you how would you measure the acceleration?" And 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 have built been built by, I don't know, a PM or designer?

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 you're going to want someone who's like understands the details of what they're building, but what details those are will evolve. Kind of like how now, like if 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. 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 going to naturally see like an increase in layers of abstraction. And then the cool thing is now we're entering like the language layer of abstraction, like natural language. And then natural language itself is really flexible, right? 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, you know, I I do think this is going to be gradual. I don't think it's going to go 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 going to 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 it's like, "Okay, now we've set it up so that it can like execute the build and it can like 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 a few sample pages to see how well those work, right? So then, "Okay, now we're going to like set up set up do that." And I think for some time at least, we're going to have humans kind of curating like which of these connectors or systems or components that 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 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 does that 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. I think 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

>> Coming up with an idea, getting to market, profit,

>> All that kind of stuff. I I think we might have been in this weird temporary phase where 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 didn't matter if you like had an intimate understanding of a specific customer. Um, 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. So I think that that's that's ultimately still what's going to 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, right? Whereas if you're like good [clears throat] at building like, you know, websites, but you don't have any specific customer to build for, I think you're in for a much harder time.

Bullish on vertical AI startups is what I'm hearing.

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 going to solve presentations incredibly well, and we're going to understand the presentation problem uh better than anyone, and we're going to uh plug into your workflows and all these other things that matter for a very specific problem."

Okay. Incredible. When you think about progress on Codex, I imagine you have a bunch of evals 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 going to be the one thing, but what do you focus on? What's like a KPI or two?

One of the things that I'm constantly reminding myself of is that 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 oversolving for that. And so I think it's like just critically important to like go look at like your like D7 retention, right? Just go try the product. Like sign up from scratch again. Um, I have a few too many like Catchup Pro accounts that I've just like in order to maximally correctly dogfood like signed up for on my Gmail and they charge me like $200 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 for us because, 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 that we do that that might might be, I think we might be the most like user feedback slash social media pill team out there in this space, is like a few of us are like constantly on Reddit and Twitter and uh, you know, there's there's praise up there and there's a lot of complaints, but we take the complaints like very seriously and look at them. And I think that again, because you can use like coding agents for so many different things, um, it often is like kind of broken in any 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 hypy 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 Codex on Reddit. Actually.

>> This is uh important for people to know. Which the subreddits do you check most? Is there like an R/Codex or

>> I mean, the algorithm is pretty good at surfacing stuff, but like r/Codex is is there.

>> Okay, I'll take. Very interesting. And then uh if people tag you on Twitter, you still see that, but maybe not as powerful as seeing it on Reddit.

>> Well, yeah, 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, there's 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 want to talk about that briefly. Uh, you guys launched Atlas. I tweeted actually that I tried Atlas and then I I don't love the AI-only search experience. I was just like, "I just want Google sometimes or whatever, like just waiting for AI to give me an answer. I'm like, I don't want to." And there was no way to switch. I just tweeted, "Hey, I'm I'm switching back. I don't, it's not great." And 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 got to ship stuff, see how people use it, and then we figure it out." Uh, 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 Atlas for a bit. Um, I don't work on it now. Um, but, 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. 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 are trying to do, then it could maximally accelerate you, um, and so I I would, 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 some of the some of the thinking at least for me personally, I can't speak for the whole project, but was that a lot of work is done in the web. And if we could build a browser, then we could be contextual for you, but in a much more first-class way. We weren't hacking like other desktop software, which have like very varied report 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. Instead, 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 I don't know if you've played like, I don't know, say Halo, right? Like you walk up to an object. I mean, this is true for many games, you press, man, it's been a long time, this is embarrassing. Press 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. And 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. We need to have a little bit of context, and then we can and then we can help. And I think this is critically important because, 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 is if we could like push notify you. So, you get a thousand push notifications a day 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 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. 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 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, 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 we have the ability to build UX that's like mixed initiative, so that we can surface contextual actions to you like at the times 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, as this kind of super teammate that makes you awesome at work. 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, I don't know, fun or unexpected ways people are using Codex that aren't engineers?

I mean, there's a load of a load of unexpected ways, but I think like most of where we're seeing like 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 an data analysis or something like that. I personally am expecting that we're going to 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 because there'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? What what code does it support? If you're like, I don't know, SAP, can you add Codex and start building things? What's kind of like the sweet spot or does it start to not be amazing yet?

This 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, you know, 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 Codex to be 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 codebase that is in fact not perfect right now. So yeah, I think if you're going to try Codex, you want to try it on like a real task that you have and not necessarily like dumb that task down to something that's like trivial, 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 or like to implement that, you know, the fix."

>> I love that answer. Just give it your hardest problem. I will say like, you know, if you if you're like, "Hey, okay, well the hardest problem I have is that I need to build like a new unicorn business." Like obviously that, you know, it's not going to work, not yet. So I think it's like, give it like the hardest problem, but something that is still like 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, 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 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 into someone just setting up Codex for the first time to help them have a really good time, what's something you would whisper?

I might say try a few things in parallel, right? Right? So you could try giving it a hard task. Um, maybe ask it to understand the codebase. Uh, formulate a plan with it around an idea that you have and kind of build your way up from there. And like sort of the meta idea here is it's again, it's like you're building trust with the new teammate, right? And so like you wouldn't go to a new teammate and just give them like, "Hey, do this thing, here's zero context." You would start by like first making sure they understand the codebase, and then you would like maybe align on an approach, and then you would have them go off and do bit by bit, right? And I think if you use Codex in that way, you'll just sort of naturally start to understand like the different ways of prompting it because it is, it's a super powerful like agent and model, but it is it is a little bit different to prompt Codex and other models.

Just a couple more questions. One, 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? Why should they 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 into software engineering, computer science, 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 angles you could go at this from. Um, I think the, well, the easiest one to think of at least is just like 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 even like someone in college or a new grad is just so much more than what that was before. And so, I think you just want to be taking advantage of that. You know, 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, right? 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 a more senior career person because, you know, the divide is actually getting smaller because they've got these amazing coding agents now. 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 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 even like really effective like communication and collaboration with your team, skills like that I think are are important are going to continue to matter for for quite some time. Like I don't think it's going to be like all of a sudden uh the AI coding agents are just able to build like perfect systems without your help. I think it's going to look much more gradual where it's like, "Okay, we have these AI coding agents, they're able to validate their work." It's still important and like, for example, like I'm thinking of an engineer who was working on Atlas since we were talking about it, he set up Codex so it can like verify its own work, which is a little bit non-trivial because of 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? Fix it." And like did that on a loop, right? And so you still like at various phases are going to want a human in the loop to like help configure 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, not that anyone writes a, you know, for 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. So I think that's 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 that knowledge is still going to be like, uh, you know, agents aren't going to 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 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?

So

Codex writes a lot of the code that helps like manage its training runs, the key infrastructure. Uh, you know, we move pretty fast and so we have a Codex code review is like catching a lot of mistakes. It's actually caught some like pretty interesting configuration mistakes. And, 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.

>> 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 does it like, alert people or it's like, "Here, I'm going to fix the problem and 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. 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. 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, um, is 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, right? It's it's a lot more than that.

>> Yeah.

>> 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, timelines. How far do you think we are from a humanly human version of AI? Whatever that means to you.

For me, I think that it's a little bit about like when do we see the acceleration curves kind of 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 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 on like 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 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 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 going to be binary. I think it's going to be very dependent on what you're building, right?

So like 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 but now let's say I don't know you message SAP right let's say you work in SAP like they have many like complex systems and they're not going to be able to just like get the agent to be self-sufficient overnight in those systems so they're going to 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 basically my sort of long answer to your question, maybe boring answer is that I think starting next year we're going to see like early adopters like starting to like hockey stick their productivity. Um and then over the years that follow, we're going to see larger and larger companies like hockey stick that productivity. And then somewhere in that fuzzy middle 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 tier.

>> 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 the things AI is doing is really annoying and a big bottleneck. I love that you're working on this because it's one thing to just make coding much more efficient and do that for people. It's 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. We have so much more potential to unlock if we uh 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.

>> Mhm. So good. 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 codeex team is growing 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 um or a salesperson or I am hiring for product, a 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 listeners have contact for you?

>> before they send me like, "Hey, I want to apply to Codex."

>> Uh, I do have a contact form at lennyrichchi.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 goes.

>> Okay. Or Yeah. Or another maybe an easier. We can edit all that out or up to you. But uh yeah, or I would just say you can drop us a DM. Uh, for example, I'm Emir Rico on Twitter and hit me up if you're interested in joining the team.

>> What a dream job for so many people. What's a sign they I don't know what's like a way to filter people a little bit so they're not flooding your inbox.

>> So, specifically, if you want to join the codeex 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 were to join OpenAI and work on Codeex 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 have to think about it, I guess that would be the filter, right? Like I think there's a lot of people thinking about the space and so we're we're very interested in folks who sort of have already been 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 but I think we want people who like are very passionate about the topic. I guess

>> 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 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 ready?

>> 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. So, uh, probably not a surprise. I should probably make them more more often a surprise. Okay, first question. What are a couple 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. 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 about a future with AI, but it's an optimistic future with AI. Um, and I think, you know, a lot of sci-fi is like fairly dystopian. Um, but this is like people uh sort of the joke at least on the sub 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 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.

>> Wow. I've not I don't think anyone's recommended that. I know you're reading, you mentioned before I started recording Lord of the Rings right now. Uh if you want another AIish sci-fi book, uh have you read Fire Upon the Deep?

>> No, I haven't.

>> Okay. It's uh incredibly good. It's like a a sci-fi space opera sort of epic tale with uh super intelligence.

>> Cool.

>> Yeah. Somewhat mostly not optimistic, but somewhat optimistic. Okay. Next question. Is there a favorite recent movie or TV show that you've really enjoyed?

>> Yeah, there's an anime called Jiu-Jitsu Kaisen, 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 anime and cartoons where 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 those like Evangelian or Akita and like those characters the protagonists are like deeply flawed like quite unhappy um that they didn't start the genre but it was like a trend for a while to sort of poke 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 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. Um and I'm not saying this is better, but at least it's quite fun to have like these like really positive protagonists who are just trying to help everyone around them.

>> I love how much we're learning about your uh personality during these recommendations. Nice protagonists, optimistic futures.

>> I think, you know, if you don't believe it, you can't r will it into existence. So, you're in a balance.

>> This is your training data.

>> Is there a product you've recently discovered you really love? Could be an app, could be some clothing, could be some kitchen gadget, tech gadget, a hat.

Yeah. So I have been like quite into uh you know combustion engines um and cars. Actually the reason I came to America initially was cuz I wanted to work on like US aircraft. Um but you know now I work in software. Um and so for the longest time I basically only had like quite old sports cars. Uh old just because they were more affordable. Um and then uh recently um we got a Tesla instead. And I have to say that I find the Tesla software like quite inspiring. Um, in particular, it has like the self-driving feature. And you know, I've mentioned a few times like today like 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, maximally in control, but yet you're getting a lot of help. And I think they did a really good job with enabling sort of the car to drive itself, but all these different ways that you can adjust what it's doing without turning off the self-driving. So like you can accelerate, you know, it'll like listen to that, you can turn a knob to change its speed, you can steer slightly. Um, I think it's it's actually a masterass in like building an agent that still leaves the human in control.

>> This reminds me Nick Turley's whole mantra was are we maximally accelerated?

>> Yeah. Yeah.

>> feels like it's completely infiltrated everything at OpenAI, which makes sense. That tracks. 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 motto, but maybe I can tell you about the number one value, 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. Yeah. And we had to put them together because we as founders realized that we often would be nice and it 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 needed to be even more candid. So then the question is like okay like how how should we be candid? It's like okay well let's let's think of being candid 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 to people.

>> That is a beautiful uh way of summarizing how to how to lead well. What's the uh the book about dare uh challenge directly but care deeply uh radical cander.

>> Oh yeah yeah.

>> yeah. So it's like another way of thinking about radical cander. 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 Emiricos and I was talking at JGPT and it told me the most famous individuals with the surname are the influential Greek poet and psychoanalyst Andreas Emiros and his relative the wealthy shipping magnate and art collector George Mureos. So the question is which of these two do you most 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 he uh he loved the island that our family's from.

>> Wait, you know those 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 or aunt in Malaysia, too, if that makes sense.

>> Yeah. But yeah, he he loved this island that the family sort of like initiated from. I believe I don't actually know where that chipping magnate 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. Um, 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 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. You know my phone my phone turns black and white at like 9:00 p.m. at night. Uh but yeah, so Twitter or XM Rico. Um, and uh, yeah, if you post in r/codeex, I'll probably see it. Uh, so you know, you can go there. Um, how can listeners be useful? Um, I would say please try codeex. Please share feedback. Let us know what to improve. We pay a ton of ton of attention to feedback. I think it's like honestly like the growth has been amazing, but it's still very early times. 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 working on the future of coding agents and then agents generally then please uh 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 and you especially just so nice and uh as you like the examples you shared optimism and kindness you know this is what we want to be this is these are the kinds of people we want to be building these tools that are going to 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.

>> 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 lennispodcast.com. See you in the next episode.