Codex vs Claude Code vs Cursor: Who Wins, Who Loses

I I’m a maximalist. I’m definitely much more motivated by the idea of winning than the fear of losing. But I’ll admit to you something. When I was running a startup before joining OpenAI, and one of my darkest moments, and there were many dark moments while I was running the startup, was recognizing that I’d spent the past few months trying to avoid losing. All of a sudden, was like, oh my god, that is why I’m so unhappy, and that’s probably why the startup isn’t going well. You know, I basically, every now and then, have to re catch myself and, like, flip back into this idea of winning.

But really what motivates me even more than that is I think I just love building things and building things for people. And, man, I am so excited for this year because many amazing things that don’t exist yet are gonna be built and given to a lot of people.

For sure, would agree that coding is one of the first domains where LLMs are really good. But, you know, what does it mean for coding to be automated? It’s like kind of a heavy statement. Right? For example, now that we no longer write assembly, like when that change happened, and we moved to higher level languages, did we say coding is automated? Not really, right? We were just able to write much more code. And then as a result, actually, there was much more demand for code, there were many more software engineers required.

But yeah, part of what they used to do is automated in the same way that like, do you know the origin of the word computer?

I might pronounce the location wrong, but I think it was at Bletchley Park, there were all these machines for like decoding German Enigma. And like, there were humans who would like punch out punch cards and like put them into the machine and do a bunch of like tabulated math. I’m probably butchering this. But basically, there was an intensely manual part of work. And even like the first spreadsheet software was kind of loosely based off this idea that you would have an office full of desks arranged in a grid and people doing tabulations and then passing their sheets to the next person.

And so all these things, like those specific tasks have become automated. But every time that’s happened, there’s been an explosion in demand for the output. And so you need many more people actually to do that kind of work, even if the specific task has changed.

Yeah. And I, you know, sometimes we change what terms mean, right? Like the term computer now refers to something else, but now we have the term software engineer. And so I definitely think we’ll have many more builders. And, you know, something interesting that I’m observing now is like there’s this compression of the talent stack. You know, you still need software engineers today, you still need designers. I’m a PM, do you need PMs? You know, you can have a fun fun some fun jokes about that. I don’t think you need them.

But maybe when you say engineer, you might be thinking of someone who’s like much more full stack than has been true before. Like, even if you go back a few years, you had many more places where there was like the back end engineer and the front end engineer. Whereas like now, at least if I think about the Codex team, like, that’s much less the case and things are much more full stack. Right? And so I think this comp this talent stack will compress, but we’ll still have people building.

Yeah. Yeah. It’s it’s my fun joke. I think well, first of all, I think it’s incredibly hard to define what a PM is, what a product manager is. I kind of think of the role as like, actually explicitly undefined, and your goal is just to adapt to whatever the team or business needs. Often, if you have a bunch of people like trying to build as quickly as possible, then what a what a product manager can do is spend time like taking a few steps back and trying to look around corners and figure out what to do, you know, collaborate with the folks in go to market, and maybe be the the team’s like greatest cheerleader and quality razor.

But like all of those things I just described, which are maybe my current role could be done by a really strong eng lead or a designer who thinks a lot about product. And so I think it’s, like, often useful to have product managers, but you probably don’t want many of them until the team is really large.

For sure. Okay. That’s a that’s a fun one. I think there are multiple bottlenecks, but that’s maybe the most sort of click baity one. So if you don’t mind, I will do this slightly Socratically, like how many times would you say use AI today? 30 plus times a day. Okay, cool. How many times do you think assuming it was like zero energy expenditure from you, how many times do you think AI could help you per day?

Exactly. And like, I hear things now from engineers like at OpenAI and also outside who were telling me like, you know, I constantly have Codex running, I never close my laptop. And if it’s not running while I’m in a meeting, I’m like wasting my time, I need to make sure Codex always has work for me that it’s doing. And that’s like super cool and super exciting. But that’s a lot of work, right, to like manage these agents and make sure they’re always working. And going back to the 30 times per day thing.

Yeah, like when we look at how often Codex users are using Codex, it’s like kind of this like 10s of times kind of range. And I think AI should be helping us 10s of 1000s of times per day, you know, compute budget permitting will and we’ll get there over time. But the problem is, at least if I think of myself, like I work on this stuff, I know I should be using AI for everything. But I’m too lazy to like type out that many prompts. And I am too uncreative to figure out all the ways that AI can help me.

And so I end up kind of at a similar number as you. I still am at the point where when I use AI to do something cool, like prep for this conversation with you, I’m like kind of proud of myself. I’m like, oh, cool, I managed to use AI in this new way. That’s fine for people like you and me who are like, really interested in this topic, right? But I don’t think most people we should expect in order to benefit from AGI should need to like, put so much effort into how to use this tool, it should just be effortless for them.

I think the world we want to get to is one where to use AI, you don’t really need to, like, figure out the right way to prompt. It’s just super easy for you. And you don’t even need to recognize that AI could help you. It’s just like knows you connected to your context and chimes in helpfully.

Yeah, totally. So I think that it is our job to make sure that we have the models with the with amazing capabilities, and then eventually to get to a world where this is, like, highly productized. And so you just have this like magic text box or audio input, or whatever, or you can just add AI to your like group chat, and it just starts to help. But I think there’s quite an interesting in between stage. And I think that that is actually where the most value lies right now.

So here’s what I mean. You could try to productize like a specific feature of AI for a specific market. And I, you know, that many companies are doing this. But I think it’s a little bit hard to know what exactly will work, what is the right form factor. And someone was on your podcast earlier, and I they said something that I thought was quite interesting about how you cannot adopt AI at enterprise without FTEs.

Yeah. So so even though I am literally hiring FTEs, and if you’re an FTE, please apply for a job with me, I actually disagree with that entirely. So what I think we need to do is build tools for people. Like you can use FTEs, as as Patrick said on the podcast, to automate workflows, right? But then you’re limited by like what you from your top down perspective can do and what you from your FDE staffing can can staff to be built, right? But for me, the most exciting future with AI is one where everyone just feels like a superhuman, just like empowered by AI.

And for that, we need tools that are for people, for individual users, and that everyone feels fluent with. I think the the phase that’s most interesting that we’re at now is building for the kind of people who are interested in figuring out how to use AI. So what we need to ship, and I think this was like the genius of like when Claude Code first shipped, what they really got right was they had this tool that was super easy to use in whatever context you want just in your terminal, and people started experimenting with where to use it.

And so I think as we think about AI being used outside of coding work, one of the most important things we can do is not overly build it like, okay, this is AI capabilities, but only specifically for finance, only for specifically for this workflow, but actually build a much more open ended tool that someone can just use for any given task creatively.

Yeah. So that’s why I think it’s the bottleneck. So basically, here are the three phases in my mind. First, let’s have agents work really well for software engineering and coding because LLMs happen to be good at that. Next, let’s realize that for an agent to be useful more generally, it using a computer is super valuable. And also, we’ll realize that all agents are actually coding agents because coding is just the best way for an agent to use a computer. So let’s take that same super flexible idea, but make it available to anyone who’s excited to explore and tinker.

And we’re already seeing people start to do this with like the Codex app, like Codex app is built for software builders, but we’re seeing builders use it for all sorts of non coding tasks. Then finally, once we see what’s working, let’s build that productization that you were talking about, where you have highly specific features that just work immediately out of the box for people. And I think we’re going to speed run this entire like one, two, three journey in the next months.

I think you’re right. If you’re trying to go like all the way from zero to one and you have this like, and I said, I don’t mean grand negatively here. But if you have like a grand vision for some like ultimate workflow automation system, then yeah, you’re going to have to clear through all of these security hurdles, all these like compliance hurdles that are really real, right? Build connections to all these data systems and like systems of record and action. Yeah, so you’re going to need an FTD to do that.

What I’ve seen is that when we do these things, top down, we end up like massively underleveraging the potential of AI in like helping that company, Whereas you can maybe do that in parallel, right? But if you can just give AI to the people like actually doing the work, they can start to like get a mental model for how AI can help. And then they can start pulling AI into their workflows at the same time. Here’s just like an analogy or something here is like, imagine if, you work in like a customer support role, and AI is being brought into your role and starting to automate like meaningful chunks of your work, but you’ve never heard of ChatGPT, nor are you allowed to use it.

So in that scenario, you have like no intuition for what this thing is. Whereas in a world where actually you’ve been using ChatGPT for work at the same time as like parts of your work are getting automated by an LLM, you have much more intuition for how this works. And, you know, I would argue you feel much more empowered about this idea that it’s being accelerated, and you have some degree of control to steer like where these automations are built, as opposed to like, it’s like this complete, like, ex machina kind of thing that is quite disempowering.

So bringing this back, like I think there is a way to do this because the data control issues you mentioned are real. But at the end of the day, every tool, every feature, every workflow is for a human who is somewhere, an employee somewhere, and that employee is accessing that tooling via their browser or via their file system, like at the end of the day. And so, at the end of the day, everything comes to an interface that an agent running locally on your computer can work with.

And I think it’s quite unusual, like an OpenAI, we’re building a browser Atlas. And you might wonder why. And there are many reasons why. But I think one of the key reasons is that by building a browser, and by controlling it like tightly end to end, we can build like safe agentic browsing for enterprise. That is a way to access things agentically that is that are otherwise not yet built out by FTEs.

I mean, these simple answers, it’s it’s super important. We And so is it like an inference monopoly? Like, you have it now and competitors don’t? This is just my opinion, but I don’t think we’re gonna end up in, like, this kind of monopolistic world. I think there’s so much competitive pressure that there’ll be like multiple answers to this. But I will say that we have like news coming out about that partnership soon, and I’m very excited for these kinds of things to ship. It’s going to be awesome.

But even so, like, you know, with GPT-5.3 Codex, that model is like significantly more efficient than prior models. And so we’ve in the feedback we’ve heard is that people actually feel like now this is like a very competitively fast model than before. So there’s a lot of things you can do just in terms of the model. There are also things you can do with like improving how you do inference. So we recently rolled out a change where in the API, like those models are served like 40% faster and in Codex, they’re served 25% faster.

So I think like speed matters a lot. And we’re kind of approaching it from all angles, like both the hardware, how you do inference and the model level.

I don’t know. I think I struggle with that. I think, fundamentally in this new world where anyone can build and it is increasingly easy to build things, what is hard? Right? I think having a good relationship with the customer and knowing what they need is as hard as ever, maybe even harder, as it’s just like there’s just more stuff in the market to choose from. You know, the other things that are harder, like building the right thing, having a really high quality thing.

But going back to the sales and marketing thing, like, I don’t think that goes away because I think that’s as like I said, I think that’s just gotten harder as the as the markets any given market gets more competitive with more software out there.

So I’ll speak for myself and then for the team. Would say, like, most people that I know are basically not opening editors anymore. And this was a step function change that happened in it’s been happening gradually, but I’d say the key external market touchpoint for this was like GPT-five 0.2 Codex, where all of a sudden the model was like way better running for longer, handling tasks end to end, managing its context, and following instructions. And so we kind of saw this inflection point. And that’s actually what part of why we built the app.

So I think what before gpd 5.2 Codex, the kinds of AI features we were using to write code were like, tab completion, or maybe you were pair programming with the model. And in my mind, you you still need it to be at your laptop with your hands on the keyboard ish. Like, And it might go off and do a little bit of work, but you know, you’re of still need to be there and like drive, it’s just like handling these small things for you. And then at the time of GPT five point two Codex in December, we kind of switched to like, actually, I’m just going to fully delegate this task.

It’s like, you know, I’m going to do a plan with it, make sure we like the spec that it’s going to do, and then I’m just going to let go let it cook. And this is quite a different way of working. So it’s like it’s changing, like, literally as we speak. And so part of why we built this Codex app that we released last week is because we wanted to build like a form factor or user experience where it felt like very ergonomic to be delegating instead of pairing with an agent.

And so like delegating to multiple agents at once. And so even at OpenAI, this is changing massively. I don’t have a percentage stat for you. But I would say like the vast majority of code is written by AI. And I would say that now, probably like most people are not even like opening IDEs. Maybe if they are opening IDEs to like, maybe you want to own the interface. So you’ll like help flush out like the interface between like two modules, and then like AI fills it out.

Or maybe you wanna, like, collaborate on a plan, but then have AI fill it out. The code itself is not being written by humans anymore.

stack in twenty four months’ time? Depends how you define IE so the the formal definition, right, integrated development environment. I mean, that that phrase is so squishy that, like, literally anything could be an IDE. Right? So I don’t think that’s very useful. If that’s the answer, then yes. You could even argue the Codex app is an IDE. I don’t think it is. Like, for me, think of an IDE is like a really powerful editor. And we explicitly didn’t build editing into the Codex app because we wanted it to be really clear how you’re meant to use it.

So, you know, it has a lot of affordances for managing multiple agents, for delegating, for reviewing changes. It has really prominent skills, which are an open standard that are really useful for doing non coding work, stuff like, you know, triaging tasks or monitoring deploys or something, but it doesn’t have text editing.

There are a few things here. First off, the spec for what you wanna do or the plan becomes more important than ever. Think like architecturally, like how should this code work? We recently shipped like a very prominent plan mode that works a little differently than others where you have the agent go off and like propose how it’s going to do something. It’s like quite a long plan. And it asks you questions about if you agree on how it wants to do it, or if you want have input.

And this is very similar to like if you had a new hire who was new to your code base, you know, they had to present a sort of request for comments to the rest of the team before they started doing the work. So even though that’s not formally code review, I would say review of the plan is actually something that’s becoming more important because we’re entering more of this like delegation phase of working with agents. So that’s an underrated thing. Then, okay, there’s actual code review.

I think a problem that I hear a lot of people talking about, especially in the open source world is like a lot of AI slop, like people will just be submitting PRs to these open source repos, and they’re trash. And like, maybe the user hasn’t even the person submitting the PR hasn’t even tested them or definitely hasn’t reviewed the code. I think this is a problem. And so a common practice with Codex is to have Codex like review its own PR or its own change. And Codex is actually incredibly good at this.

We’ve explicitly trained the model to be good at code review. And you know, that included things like making sure it’s like really good at creating like high signal feedback. So it’ll, like, basically have few false positives of criticism, which means you can really trust when it has feedback. And so not only do we encourage people, on the team and elsewhere, like, to, like, just ask Codex to review, you can then also set it up to just, like, automatic review. So like nearly all code at OpenAI is reviewed by Codex automatically whenever you push it to a good repo.

Actually, like one one fun thing for people who haven’t tried Codex yet or didn’t try it recently, sometimes the way that people like see how good our models are is by asking Codex to review a different model’s code. And basically, they’re like, oh, shoot. I should probably just be using Codex to write my code in general.

We’ve taken this, like, kind of counterintuitive approach with Codex to just build it super openly. So, like, the Codex core harness is open source, and we’re always trying to make it easier for people to switch. So for instance, when we first launched Codex last year, we created like created as even a heavy word. It was just just established convention, which is called agents. Md. This is basically a file that you can put instructions for the agent in. And instead, we didn’t call it Codex. Md, we just wanted it to be something that all agents can use.

And pretty much every agent except Claude uses agents. Md, which is awesome. And then just last week, actually, we helped push for putting skills, which are a standard for like giving the agent instructions and scripts. We push for those to be sorted in sort of a neutral named folder called agents, instead of in like Codex or something. And again, everyone has jumped on it except the usual suspect. I think it’s really great for the developers to have a lot of choice. And we’re trying to make it even easier for people to try different things.

Now that said, these coding tasks, right, where you’re asking an agent to write some code, they’re quite hermetic. And what I mean by this is maybe an analogy in TV would be like episodic, right? Like, you can come in, and you’ve got this like open ended, like agents file that any agent can read from, you’ve got these skills that any agent can use, and you can ask the agent to write some code, and it produces a patch and that patch goes into Git. So kind of like both ends of this are pretty neutral, vendor neutral, so very easy to move between for now.

As agents start to do work that is not writing code, but more general work, again, for software engineers or beyond for any builder, they’re going to need to start interfacing with other systems. So as they start, maybe your agent is talking to Sentry, right, or it’s talking to your Google Docs or something, then I think these agents become much stickier because actually deciding to connect an agent to that system is a sticky decision. And if you’re an enterprise, really trusting that the agent is going to have access to these tools, but there are really good secure guardrails and sandbox and like controls over how the agent works with these systems, I think is critically important.

And that’s not something that you’re going to want to to do multiple times. And so, you know, we’ve been kind of building Codex knowing that this is coming. And so we have like the most conservative sandboxing approach. Sandboxing is kind of like a set of controls, OS level controls over what the agent can do.

Yeah. I mean, it’s interesting because I think on the one hand, like we think about this, obviously we’re running a business, but our mission here is to ensure that we safely deliver the benefits of AGI to all humanity. And so something that’s unintuitive to people about the Codex team.

actually, our job is the distribution of intelligence. And so we’re obviously building out Codex, and this is really unintuitive to a lot of listeners. But, like, we put all this effort into training these models, and then we serve these models to our competitors. And from our perspective, this is so difficult

Yeah. Totally aware of it. Like we’re OpenAI is like a really interesting and unusual place to work. But basically, because we’re playing such a long game for us, if the competition gets better, we learn. It’s actually helpful for us. And so we’re pushing really hard at growing Codex. Do you learn? Because if if if they’re closed and they improve, you don’t learn. I don’t think so. For example, there are a bunch of recent launches. Like, today, I literally just like, quote tweeted a thing this morning about a launch from Warp.

No particular affiliation. Right? And there are a bunch of cool ideas in there about how they like framed up the way that their agent can work in the cloud at the same time as working locally. And for me, that’s like inspiring. And I think I see all these things from various companies. And like, one of the coolest things about the space is it’s like, we’re all kind of inevitably reaching the same conclusions together and then building things out. And so, you know, on the Codex team, think I we have some massive advantages, right?

We have the massive distribution advantage with ChatGPT, we have the massive like capability advantage of training our own models to be good in our harness, and building our harness to be good at the new models, and like no one else has early access to those. And so I think we’re we’re playing to win, and we have a a really big advantage or a number of advantages, but we’re also playing this long game where, you know, again, we serve our models to everyone, where we push for open standards so that everyone can use, like, all the things that we’re pushing for as well.

Okay. So I think if we’re going to talk about it more from an OpenAI perspective, obviously, this is way above my pay grade, but I would say it’s compute advantage and having the best models. And in order to achieve that, we then need to build businesses to generate revenue. And also that something we’ve that’s really interesting we noticed with having the Codex team, which is a sort of combined team of research and product is also by building these these successful products, we create a lot of pressure to improve the model in sort of a faster way.

That’s maybe the company perspective. Right? If we come to the product perspective, I think the single most important thing we can do is build a a really good product that people want to use. And like I was saying earlier, I think we really want to build products for individuals, and then allow people to become fluent in those products, and then like pull in automation. And I think that may be counterintuitive, but will result in way more impact than anyone purely approaching it from like the enterprise workflow perspective.

You know, I think that’s mostly a question of product execution. And then that works for say, like prosumer. When it comes to enterprise, the go to market side is really important. Something that I’ve learned the hard way is if we go to an enterprise, and we’re just like, hey, we’re here, like, feel free to use the stuff that doesn’t work. There’s actually quite a lot of education that needs to be done. And there’s a lot of, like, configuration that we need to support and sort of like education of the broader team.

So like, that motion looks much more like coming in, pitching, meeting the head of developer experience or whatever, understanding how they want their team to operate, and then giving them tools to like propagate that mechanism of operating to the rest of the team.

north star for your progression? It’s actually not revenue is the primary. The primary is active users, How which you do you measure active users, daily active users? Yeah. So we measure weekly active users and it’s, know, did this person like actually do a turn in our product? You know, did they send a prompt?

I think daily active will be better soon. We just happen to use weekly active. It’s like a standard here. And I think as we were getting started, it made sense, but I I I actually agree with the criticism there. It’s like, we should probably just be a daily. Like, I think we we need to be getting to a world where for any given task that you have, your first instinct is to ask an agent to help. Right? It’s kind of like, you know, how, like, with Google search, it’s just like, okay, anything I need to do, I just like go into this text box and I can get navigated to the right location.

Then you had ChatGPT. It’s like for any information I need, I can go into this text box, type it out and get information that helps me. And I think the next phase that we’ll see this year is like for any task I need to do, as opposed to just get information, I go to this text box or this input, and something happens that helps me, even if it’s not the full task, even if it’s only a small part of it.

The simple answer is yes, but actually I think there’s two components here. Like, if we if we just imagine the future, like, just like, let’s think of some sci fi movie. Right? Like, what does AI look like? I I believe that sci fi is a really good predictor of what the simple the future should look like. And usually, it’s pretty simple because it’s the story, and I think simple is usually right. It’s gonna be some just, like, entity that I can talk to however I want about whatever I want, right?

I felt like I shouldn’t have to navigate to a place where I work with like my coding AI, and then I have this like different place for my like sales AI. And I have to like be like, hey, I’m now talking to sales thing and like do that. It’s just like, I just gonna talk to a thing and it’s just gonna help. So I think what we’re gonna have is that we’ll have chat or voice, basically conversational interface will be sort of the the pillar of everything that you can talk to about anything, and that you can add into any group chat or whatever.

So it can like discover how to help you. But then if you’re, a power user and you’re very good at a specific thing, you probably don’t want to be disintermediated by having to talk to another person. It’d be like if you had an executive assistant, but you can only work by talking to them. That’s, like, super annoying. Right? So So at some point, you wanna you wanna get to the show notes and, like, look at them yourself and, like, edit them yourself. Right? You wanna edit the thing yourself.

So I think we’ll pair chat with, like, functional, like, graphical interfaces that are bespoke to, like, what someone needs. So, like, in my case, I will probably chat to, like, do my, you know, podcast prep. But when it comes to like, actually looking at product and code, I probably want like the Codex app that I can go into and get deep in. Right? Whereas maybe if we’re talking to a marketer, maybe that marketer will like chats to ask questions about the product. They’re not going to download the Codex app just to ask questions about the product.

But maybe they’ll have like a super custom GUI for like ad analytics or something that they go into.

That’s interesting. To be honest, I’m not sure what that’s gonna look like. My quickest answer to this is that we’ve noticed as we build Codex that the best interfaces for Codex to do work are also tend to be the best interfaces for humans. So when people ask, oh, how can I make my code base more efficient for the agent to work with? The answer is often like, well, have you looked at it yourself? And is it easy for a human to work with? So like a very specific example would be running tests in a code base.

Naively, if you just set up most test runners, they just in it all the outputs of all the tests. And so like as a human, it’s really annoying because you have to go in and like find the one that failed, and it’s like you’ve got to read hundreds of thousands of lines. Turns out that’s terrible for AI as well. But if you filter it down to just only emit the failed test, better for humans, also better for agents. So probably the agent to agent interaction points will be very similar to like if there was a human in the loop.

And that’s nice because it means you can kind of atomically replace individual systems.

I think that from what we’ve seen, and, you know, and I’ll I would defer to my research team on this, but I feel like we we feel like we have plenty enough data to build really good coding models. I actually think the the place that’s more interesting for getting data now is like as we get into, knowledge work tasks, that’s kind of data that’s, like, not really, like, available most places on the Internet. And so you start to have, like, really interesting brainstorms for, like, how to help a model be good at it.

Like, maybe you have to, like, pay people to, like, simulate doing tasks so that you can, like, learn these trajectories for the model. Maybe you should acquire startups, you know, that are no longer in business but have have a lot of, like, data, like, say, Slack or something. Yeah. I think that that kind of knowledge work task distribution is, like, much harder than coding.

Yeah, I mean, I think the way that we think about these things is just like, how do we move as quickly as possible? And so becoming able to set these things up in house is like very expensive in time, and we’re a small team. So what I have observed so far is that if we need to run a data campaign at scale, we’re usually going to enlist help from one of these companies.

Yeah. I would say that right now, it doesn’t feel like we’re competing super directly. But I don’t know if you saw our Super Bowl ad, the tagline of which is this, you can just build things. With the app, we noticed that like many people who are less technical are starting to build things. And so the kinds of things they’re building are much more hello worldy. And so I think that we will see some overlap in use cases where you have people just pulling up Codex because they have it as part of their ChatGPT.

Actually, like a big announcement, last week was that we’re now offering some Codex to people even on free ChatGPT plans, or on the Go ChatGPT plan. So this is this is massive just in terms of like bringing availability to everyone. And so I think we’re definitely going to see people with like a free ChatGPT plan coming in and just like building simple things where they otherwise might have gone to a specialized tool. What

like to do differently, but for whatever reason you can’t? I feel like it’s been a very good few weeks for us. So we’re very I’m pretty jazzed by everything that’s happening. Feeling that I have the most.

Absolutely. We are very attuned to it. Right? Like, you look at the the history of Codex, the first thing we launched last year was, like, this amazing idea that people were super excited about. It’s like, hey, we’re going give the agent its own computer in the cloud, you’re going to have as many of them as you want work for you in parallel on tasks. Super great idea. To be honest, it didn’t work as well as what we shipped later. It was not the best. And then since August with GPT-five, we started pushing really hard on interactive coding, which is where most of the competition in the market is.

You know, we went on an absolute tear. I feel like the public metric we had was like since August, we grew by like 20 x. And then like even like late in the year, we like doubled from December to now. I forget the exact number there. But, like, that was competing neck and neck. But the the shift that we feel last week is, you know, we we felt like we had the most intelligent model that was cemented with five three Codex. We had feedback around our model being slower and, like, maybe less fun to work with and, like, being less good at communicating with you while it was working.

We addressed that feedback, and that’s true even compared to, like, the the other competitor model that launched, like, twenty minutes before us and was like, maybe this is spicy. It was, soda for twenty minutes. Soda means state of the art. And then we’d always been getting a lot of feedback on, like, the quality of the user experience in Codex. Our most popular surface was the IDE extension, and our CLI, which is the command line interface, was less polished. But with the app, the feedback has been like resounding from the market that this is like a really high quality experience.

It’s like simple, like unintuitively simple, and people are just loving using even our biggest credits are converted. So yeah, and then we and then we had the Super Bowl ad, and then we went to free. And going back to your question of like, what do I most want to do differently? I have two things for you. The first is I actually want to get back to cloud. When we pivoted our strategy from like focusing on the cloud agent last year to working interactively, the thinking was very simple.

It was just and it’s kind of like what I was telling you about FDEs actually. If you go too far ahead to workflow automation before your end user is fluent with the tooling and can get it to work simply, then there’s like this disconnect and you just have this pipe dream idea that’s not like effective except for the most power users. But once you have this base where people are using your tool every day and they’re configuring it and every time they use it, it gets better, then, like, the step up to, like, letting it run independently in the cloud is a much smaller step up.

So I think it’s time for us to, like, get back to, like, building out the cloud product and making it super tightly integrated with the local product. It already is somewhat integrated. And the other thing I want to do differently is, start thinking more about the bottlenecks. Like, code gen, writing code has become, like, basically trivial now. But the hard part is like what you were talking about with like code review, right? Like, how do we know the code quality is good? How do we know we’re doing the right things?

And those bottlenecks, I think are under underappreciated still and under invested in. So like, I think we want to get to a world where you can have an agent that is unbottlenecked that you trust to like own an entire microsystem or internal tool or whatever, and can do the full iterative loop, including feedback from users without having to go through human review. And that is a really hard problem to solve both from an intelligence perspective, but also from like a safety perspective and a controls perspective.

Probably, this is an annoying answer for you. It’s like some, right? Like they do tell you in my mind, they give you a good measure of intelligence. And so you can put weight on those for intelligence And especially before evals are saturated, I think when you see meaningful progress in those benchmarks, it’s like very, very helpful. And then I think you have to pair that though with like what it feels like to use the model. And that’s that’s a vibes thing. Like whenever I talk to any, even internally or even talking to, like, customers of our models, I’m always surprised by how vibes based the evaluation of how it feels to work with the model is.

Okay, so I think this might end up with fewer providers that are capturing a lot of value in the long run. And here’s why. And maybe this is a bit spicy, but I think that we are kind of in this temporary phase where we have agents that are really good at coding. Right? And if you look back last year, like maybe more people thought we would have agents that are good at other domains too, but that didn’t happen last year. So we only have PMF for coding agents, like in the industry overall, I would say, right?

And then there’s some like very narrow other use cases like customer support, etc. But I think that’s probably temporary. And then over time, we’re going to end up with agents that kind of can do anything for you. This kind of what I was saying earlier, like there’s just like a super assistant, you talk to it about anything. And then there is like specific UI that you can go look at if you happen to be deep in a specific function. So in that world, I don’t think you want like 12 agents at the company and you have to like go your employees have to go figure out the right ones to talk to, because then they won’t achieve fluency.

If they don’t want to achieve fluency, then they will also won’t like pull automation into their roles. But if you have this one thing that you can talk to about anything, right? So you’re onboarding, it’s just like go talk to this thing about anything you need, then people will develop muscle memory to go to it, it’ll become the center of gravity of work, and people will pull an automation. So I think that that future makes much more sense. And I think like, as the people building ChatGPT were like really well set up to deliver that.

This is kind of a stretch, but an analogy here is I used to work at Dropbox. And for a while, this is before Slack was big. And for a while, we thought we wondered if people should like go comment on like documents in Dropbox or or if they should like go talk about the documents in Slack. And it was like obvious that it was like more optimal for people to like put comments on the right timestamp in the video in Dropbox or like comment on the document in Dropbox.

Right? So it was more optimal. However, what we saw is that Slack is just such a center of gravity of people just like talking to each other. Like, nobody wants to comment on the document. I just want to Slack you. And so we saw that like there was this really big pull towards things happening in Slack, even if it was less efficient. And I think we’re going to see something similar at work where if there is a single agent you can use for nearly anything, it there will just be this giant poll, and everyone will talk about how they use that one agent for things.

Teams will share best practices with each other. There’ll be hackathons around how to use that best thing. Yeah. And you’ll end up with just a handful of these.

would you advise me? Like things are built for humans, like otherwise, what’s the point? Even SaaS tools are built for humans. So for me, I think my question is like, does this SaaS company own a relationship with a human on the other end of things? And if it does, then I suspect it’s not going away? Or does the SaaS company own some like really important system of record? It’s probably not going away. Maybe both of those two things, the interaction with the human and the system of record are like more important than ever actually.

On the other hand, is the SaaS company like a kind of a glue layer, but it doesn’t own either of those two things? I’m not the expert here, but I’m more nervous about that kind of company.

I think this maybe changes what kind of founder you invest in. Right? Like, I think there was this maybe temporary phase where that I liked personally as a product builder. There was this phase where you would invest in like the the person who can just like build good product, and you could kind of ignore if they had a good thesis around a customer or go to market or distribution or anything like that, because it was so hard to build good product. Right? And I think that was a that was an anomaly.

If we look at where we are now, like, maybe that kind of founder is not the founder you should invest in because it’s, like, kind of relatively easier to build good product, and you need to go back to, like, investing in the founder who’s, like, thought through distribution, who has a good good domain expertise of what to build for a specific customer, etcetera.

I’m tempted to just say, like, I don’t know. I couldn’t I think it’s a hard time to be an investor. It’s a the market is so dynamic. It’s hard to say.

I tend to agree. I I I again, it comes back to, are you going into, like, a gnarly, complicated market where customer relationships and, like, knowledge of the market are everything? That still seems great.

I think that the war for talent is incredibly fierce right now. You know, obviously, at OpenAI, we have an incredibly strong brand, and so we’re able to attract a lot of talent. But even so, we put a ton of effort into, like, closing candidates that we’re really excited about. Even, like, even we feel that it’s not like you don’t just get whoever you want for free.

So, you know, earlier I made my joke about, like, PMs kinda being optional.

I think that’s not actually true. You still need product people. But I do think that they have to be the perfect fit. And if you have someone who’s like not the perfect fit, might just do more harm than good. It’s kind of means that like we’re way more selective than I might have been in other roles.

Basically, there’s actually never been a better time to be an engineer because you have incredible tooling available to you to get incredible amount done. And your ability to like ramp into like a complex code base that you might be hired into has never been faster because you can go ask AI like a ton of questions about the code base, and you can ask it to plan out changes that would otherwise take you like days to research maybe. I think first off, I would say like you should be like very optimistic.

But then of course, like about you want your abilities once you’re at the job. Then now the question is how do you get the job? Because it’s never been easier to build things, the thing that becomes scarcer is agency, taste, and quality. I would urge you to just build things and demonstrate your agency and your taste around what you build and like build things that are of high quality and then share those things. You know, we get a lot of inbound for from folks both applying for jobs through the careers page or also on social.

And this is just me, but when someone writes to me with like some interesting thoughts and, like, a link to an interesting project, that gets my attention much more than, a normal resume does.

Oh, I I don’t need to think about that one. That that’s kind of the thing I was telling you about earlier. Right? Like, when you’re building tooling for people, like, for end users, you have to think about, like, that tooling as a system of engagement, right? If people don’t want to use your tool, if it doesn’t like naturally feel like the easiest way to get something done, then people just won’t use it. Again, I learned that from watching how Slack just absolutely took off. And so I think about that a lot now, when we’re building these agents, I’m like, if we build our agent, surely is like, workflow automation, then it’s always going to be like pulling teeth to get that thing started, right?

You’re going to need to hire Accenture or someone to come in, they’re going to deploy FTEs, it’s going to be tough. But if you can build a system that, like, people just love using, even if they only use it for partial tasks, over time, they’ll get better and better at using it, and then that you’ll get connected to the tools you want over time, and then you can start laddering in automation. Obviously, these aren’t mutually exclusive.

At least from when I was at Dropbox, the thing we were uniquely good at was desktop software. And desktop software, it’s funny, it was never not back. But anyways, it’s so back. Basically, because if you’re solving for productivity and knowledge work, yes, there are systems of record everywhere that you need to connect with. But everything at the end of the day happens on the user’s computer, either in their browser or just like locally and apps on their computer. I do think that the fastest way we’re going to see productivity gains from agents at work is going to be at first meeting users on their computer, working with the stuff that they have available to them, you know, without having deployed FDEs to set anything up.

And then over time, you’ll connect in these various systems. And so if I was Dropbox, I’d be thinking about how do we leverage our unique domain expertise in like building really good like desktop software, and the sort of collaborative layer on top of your computer, how do we leverage that to enable productivity agents? It’s a bit broad, but I think that’s the angle you go.

I think both costs are gonna come down significantly. And I also think that if this is the year of agents being deployed like broadly at work, then this is also the year where they’re going to have to be connected to all these various systems. And I think that’s going to be very sticky. And so I view this year as a race. And so I think you want to win that race, and you should be okay taking some hit to margin in the meantime.

When I joined OpenAI, I thought that and this is a little longer than twelve months ago, but when I joined OpenAI, I thought that we would all just be hanging out with our computer screen sharing within a year from there. You know, we’d have this agent that we’re just talking to. That was completely wrong. I think the rate of like progress in like multimodal models was like slower than I expected. Multimodal means, you know, like models that work with like video and audio. So instead, what happened was that we saw that, like, agents that work with your computer through code are the way.

And so for me, that’s been a complete rethink in terms of, like, how we bring the benefits of AI to, like, just people generally. It’s not not through video and audio, primarily.

The first one that came to mind was AMP. AMP. I think they’re building yeah. AMP. It’s out of out of the folks at Sourcegraph. Their product has a great reputation of just being, like, you know, punching way above its weight. But I think the other thing that I really respect is that they helped initiate this whole like standardization around like agents. Md and like dot agents slash skills, which are what I was saying earlier about like, making it so that it’s easier for users to manage all these different agents that they’re trying.

Obviously We put out agents. M d, but they put out agent. M d, and basically, Quinn started this all by putting out a tweet that said, hey. If you guys buy the domain agents. M d, we’ll standardize to your your spelling. And as small as that was, that initiated this whole standardization that I think has been awesome in the community.

I mean, there were so many different responses. The one that I heard, obviously, I think was right. The one that I heard was, well, one company is being pretty negative about the future, and the other company, us, OpenAI, is being really positive and just telling people they can build things into Dream. I I thought that response was brilliant.

Well, I can tell you the most painful product decision we had to make. For a while, Codex Cloud was, like, effectively unlimited, not free, like you needed to pay for ChatGPT, but then you had unlimited usage. Every day that we left it that way, we knew that would be harder to wind back at being like unlimited. But we were just so focused on competing on our other things that had more PMF that we kind of punted that decision out. When we wound back that unlimited use to some like more reasonable limit, there was a lot of blowback from users.

And it was a very small minority of users who like thought everything should be kind of like sudo free forever. But that blowback affected us everywhere because like the social chatter doesn’t really distinguish between these things. I think the lesson I learned the hard way there is, like, you can’t make things unlimited for too long.

Well, one is just editing code by hand. I think probably another one, this is maybe spicier, but another one might even be like actually managing the deployment and monitoring of systems by hand. Like, I basically think that probably big companies will take a long time to like deploy this, but many startups might actually kind of start building on a completely new stack that’s like fully AI managed. To be clear, the stack doesn’t exist yet, but a fully managed AI stack where because the basically it’s been built to give you really strong deterministic guardrails over what the agent can do and like control of to like rollback deploys and everything like that.

And so we’ll get to a world where the way you start a company is you start by getting an agent and just asking it to build things. And then you get more agents than that. And then maybe eventually you add your co founders to this service that you use to work with agents. And so you end up like, maybe your main communication tool is actually your agent communication tool, and then maybe you’re not actually handholding this, like, very point painful CI and deploy process, but you’re just, like, having agents do things.

I think we’ll probably see both. Like we are putting a lot of effort into agent guardrails. Like I said, we have, we’re basically the only company that cares about OS level sandboxing for coding agents. For instance, there’s none that exists on Windows. We’re the ones building that. And we’re doing it in open source, hopefully other people can use it. We think about that a lot. ChatGPT supports connectors. So, you know, you can talk to your, Google Docs or something. And we put a lot of effort into guardrails around what the agent can do with your Google Docs.

Those are just two examples, but we think a lot about this. And I think probably, though, the way that we’ll do it will not be sufficient. Like, there’ll be third parties who provide, like, various bespoke things for very bespoke company needs, and they’ll probably be a mix of both.

This is probably going to happen in much less than ten years, but my mission sort of personally when I joined the company was I just felt like even with the models we had a year and a half ago, there was so much just capability overhang or just ability for these things to be useful, but we hadn’t built the right products around that. And so people like me were getting more benefit than like people like my grandma. What I’m most excited for is to get to, like, a form factor for AI that means that they’re just helping everyone regardless of whether they’re in tech and especially if they’re not in tech or especially if they’re older.

And so, you know, the concrete vision I have is, like, at some point, we’ll, like, add an agent to, like, our family WhatsApp or something, and it’ll just start, like, being useful to the family without anyone having to think harder about it than that. There there are many other ways that that could happen, but I think concretely, that’s the most obvious thing we could do with, like, my grandma.

Thanks so much. I mean, I appreciate you putting up with my wandering answers, so all good. We’re two here.