20VC: Codex vs Claude Code vs Cursor: Who Wins, Who Loses | Will All Coding Be Automated - Do We Need PMs | The Real Bottleneck to AGI | The Three Phases of Agents and What You Need to Know with Alex Embiricos, Head of Codex at OpenAI

Alexander Embiricos is the Head of Codex at OpenAI, leading the development of the company's flagship AI coding systems that power automated software generation, debugging and developer workflows. Under his leadership, Codex has become one of the most widely adopted AI developer platforms. AGENDA: 05:13 Will Coding Be Automated? Why AI Could Create More Engineers, Not Fewer 07:17 Do We Need PMs? The "Undefined" Product Role and When It Matters 08:06 The Real AGI Bottleneck: Human Prompting, Validation, and "Too Much Effort" 13:04 Three Phases of Agents: Coding → Computer Use → Productized Workflows 13:52 Enterprise Reality Check: Security, Permissions, and Safe Agentic Browsing 17:57 Is Inference the New Sales and Marketing? 18:49 What % of Codex Was Written by AI? 21:33 Do OpenAI Use AI for Code Review? 23:31 Is there any stickiness to AI coding tools? 28:22 What Does "Winning" Mean at OpenAI? Mission, Competition, and Moats 32:04 The Future UI: Chat or Voice 34:10 Agent-to-Agent Workflows: Designing for Approvals, Compliance, and Automation 35:39 Do Coding Models Have a Data Moat? 36:50 How does Codex View Data: Will They Build Their Own Mercor and Turing? 37:27 How Does Codex View Consumer: Will They Compete with Lovable? 41:56 Benchmarks vs "Vibes": How People Actually Judge Models 42:43 Cursor's Edge and the Case for Building Your Own Models 47:37 Is SaaS Dead? What Still Defends Value (Humans + Systems of Record) 51:28 Talent Wars and Career Advice for New Engineers in the AI Era 01:01:03 Guardrails, the Fully AI-Managed Stack, and a 10-Year Vision for Everyone

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Harry Stebbings?

  Welcome to 20 Product with me, Harry Stebbings. Now, 20 Product is the monthly show where we sit down with the best product leaders to reveal their tips, tactics and strategies to scaling the best products and product teams. Now, the real question is who's going to win? Is it Codex? Is it Claude Code? Or is it Cursor? Well, today, joining us in the hot seat, we have Alexander Ambirikos, Product Lead for Codex at OpenAI. This is an incredible discussion. Time to get the notebook out. I want your feedback. Let me know what you think, [email protected]. But before we dive into the show today, the early story of Atlassian is probably very similar to your own. Atlassian knows firsthand the challenges that startups face every day and that the

• 0:43Harry Stebbings?

  right tools are essential to go from MVP to IPO. That's why Atlassian for startups gives eligible companies up to 50 seats free on the premium edition for products like Jira, Confluence, Loom, Jira Product Discovery, Compass and Bitbucket, so your team can use the best-in-class tools to plan, track and collaborate on work, whatever that work may be. Many of today's most successful startups like Cloudflare, Canva and Rivian relied on Atlassian for their growth trajectory, and Atlassian wants to give that same opportunity to the next generation of builders and investors. We know how important it is to focus on building the right things early. Whether you're in the sticky note stage or well on your journey, teams at any stage can work smarter together.

• 1:26Harry Stebbings?

  It's never too early to start with Atlassian. Head on over to atlassian.com/startups/harry for more details and eligibility. After Atlassian helps your team build and ship great products, Intercom helps you support the customers using them. If you're looking for a way to transform your customer service, let me introduce you to Finn, baby. Finn is the number one AI agent for customer service, resolving up to 93% of customer queries automatically. There is no other agent that can do that. Not 93% of customer queries, okay? No other agent can do that. So why choose Finn? Finn is the best performing AI agent for CS. Finn doesn't just answer questions, it takes actions. It automates the most

• 2:10Harry Stebbings?

  Complex customer queries like refunds, transaction disputes, technical troubleshooting with speed and reliability. I wish my team was speedy and reliable, beats every competitor in every head-to-head bake-off. Completely configurable and code optional setup, my word. I mean, the benefits just go on and on. It's easy and efficient implementation. It works on any help desk with no tedious migration needs. It's trusted by over 6,000 customer service leaders, including top AI companies like Anthropic, Loveable, Synthesia, Clay, Vanta. So if you're ready to transform your customer service team, scale your support, and give team members time to focus on the really high-level strategic work, learn more about Finn at fin.ai/20vc.

• 2:54Harry Stebbings?

  While Finn scales your support without losing speed, Reforg shows you how to translate that scale into durable product-led growth. Everyone's shipping faster than ever. Cursor, Claude Code, Codex, AI is making code and writing code faster than ever. But here's the problem: speed means nothing if nobody uses what you ship. That's where Reforg comes in. Reforg is building a product discovery engine that sits upstream of your coding agents. Not another prototyping tool, Research Repo, or AI Interviewer, but a product that will ingest your customer data, generate variations of product solutions, validate the solutions before code is written, and hand off winning directions to your team.

• 3:37Harry Stebbings?

  Reforg kills product debt before it starts because every unused feature you ship isn't just wasted engineering time, it's a maintenance burden, complexity tax, and surface area that you cannot shrink. Used by product teams at companies like Toast, Vimeo, Klavio, and many more, Reforg helps teams ship more features that actually get used. Try Reforg at reforg.com/build and use the code 20vc, that's 20vc, for one month free of Pro.

• 4:07Harry Stebbings?

  You have now arrived at your destination. Alex, I'm so excited for this dude. I told you, I've been at a P conference and all I could think was, thank God I've got Alex next because this is gonna be a great one. So, thank you so much for joining me, man.

• 4:20Speaker 02

  So excited to be here, thank you.

• 4:22Harry Stebbings?

  Now, this is a weird first start, but roll with it. You'll understand my British intricacies. I'm fascinated by people's motivations. Are you motivated more by the fear of losing or like the thrill and excitement of winning?

• 4:36Speaker 02

  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 fast few months trying to avoid losing. All of a sudden, I 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, basically every now and then I have to 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.

• 5:14Harry Stebbings?

  I'm diving right in. Elon said that coding is one of the first professions to be largely automated. Do you agree, given your position and what you see day to day?

• 5:23Speaker 02

  For sure, I 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 and 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"?

• 5:53Harry Stebbings?

  No.

• 5:54Speaker 02

  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 is changed.

• 6:32Harry Stebbings?

  So you think we will have more engineers in five years, not less?

• 6:35Speaker 02

  Yeah, and 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 could have 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 backend engineer and the frontend 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?

• 7:13Harry Stebbings?

  Totally.

• 7:14Speaker 02

  This talent stack will compress, but we'll still have people building.

• 7:17Harry Stebbings?

  Why do you think we don't need PMs in this world? You dangled the carrot.

• 7:21Speaker 02

  Yeah, 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 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 and go to market. And maybe be the team's like greatest cheerleader and quality raiser. 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.

• 8:06Harry Stebbings?

  I was stalking the shit out of you for the last few days, which was a very fun expedition into your writing, into your tweets, into your prior interviews. And you said that human typing speed and validation work is the key bottleneck to AGI, not model, compute or architecture. And it kind of left there and I was like, help me understand why human typing speed and validation work is the key bottleneck? And what you really meant by that?

• 8:31Speaker 02

  For sure. Okay, that's a fun one. I think there are multiple bottlenecks, but that's maybe the most sort of clickbaity one. So if you don't mind, I will do this slightly socratically. Like, how many times would you say you use AI today?

• 8:42Harry Stebbings?

  Thirty-plus times a day.

• 8:44Speaker 02

  Okay, cool. How many times do you think, assuming it was like zero energy expenditure from you, how many times you think AI could help you per day?

• 8:52Harry Stebbings?

  I mean, in everything, I think we'll have inference running 24 hours a day across every single thing.

• 8:58Speaker 02

  Exactly. And like, I hear things now from engineers, like at OpenAI and also outside, who are 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 tens of times kind of range. And I think AI should be helping us tens of thousands of times per day. You know, compute budget permitting, we'll, and we'll get there over time. But the problem is like, 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,

• 9:43Speaker 02

  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. You know, 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.

• 10:26Harry Stebbings?

  That's what I think like Claude has done well in terms of the packaging they've done, like Claude for legal, Claude for Excel, where you can implement it and have a DCF model, I'm not into models, but like better than one could do before. Do you think it is your job then to productize the prompts and the human actions to remove that bottleneck?

• 10:44Speaker 02

  Yeah, totally. So I think that it is our job to make sure that we have the models 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 you know, the 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 they said something that I thought was quite interesting about how you cannot adopt AI at enterprise without FDEs.

• 11:30Harry Stebbings?

  Yeah, it was Matt Fitzpatrick from Invisible AI.

• 11:33Speaker 02

  Yeah, so even though I am literally hiring FDEs, and if you're an FDE, 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 FDEs, as Fitzpatrick said on the podcast, like 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 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 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.

• 12:17Speaker 02

  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.

• 12:47Harry Stebbings?

  But does that not put the onus or the effort back on the user, back to the point of your bottleneck of human action and lack of activity on them? If you don't define the task, you put the responsibility on them for defining the task, which humans lack the ability or inclination to do.

• 13:03Speaker 02

  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, 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 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.

• 13:48Speaker 02

  And I think we're going to speedrun this entire like 1-2-3 journey in the next months.

• 13:52Harry Stebbings?

  My challenge with what you said about kind of FDEs and implementation within enterprise is data security, sensitivity, permissioning, access provisions is really fricking hard. And people are much less intelligent and confident than we give them credit for, I think, especially in large enterprise, sorry. And I think you actually need an FDE to go in and custom-fit a lot of the different horizontal solutions to make it work. Am I wrong?

• 14:17Speaker 02

  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 FDE to do that. What I've seen is that when we do these things top-down, we end up like massively under-leveraging 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.

• 15:00Speaker 02

  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.

• 15:44Speaker 02

  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 at 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.

• 16:29Harry Stebbings?

  There are so many questions that I have to ask you. I want to go back before I lose thread. You mentioned about engineers like not closing their laptops because they don't actually want to lose productivity and time with building with Codex. You partnered with Cerebrus and Cerebrus is the fastest provider, obviously of inference out there. Amazing win, I think for both bluntly. How important is speed for developers when using Codex and in the future of AI code?

• 16:57Speaker 02

  I mean, the simple answer is it's it's super important. We

• 17:00Harry Stebbings?

  And so is it like an inference monopoly? Like you have it now and competitors don't.

• 17:05Speaker 02

  This is just my opinion, but I don't think we're going to end up in like this kind of monopolistic world. I think there is 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 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 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.

• 17:48Speaker 02

  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.

• 17:56Harry Stebbings?

  You mentioned earlier about putting it in the hands of users and we talked about inference there. One of my dear friends is Jason Lampkin from Sasta. And he says that actually inference is the new sales and marketing. Instead of sales and marketing teams, you're paying for inference. So users can onboard quickly, easily see value and you will actually see the removal of sales and marketing teams. It's kind of like next gen of PLG.

• 18:18Speaker 02

  I don't know, I think I struggle with that. I think, you know, 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, 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 markets, any given market gets more competitive with more software out there.

• 18:48Harry Stebbings?

  How much of internal code for you today is produced by Codex? I remember like Claude for work, Boris said was like 100% or nearly 100%. How much is internal Codex used?

• 19:01Speaker 02

  So I'll speak for myself and then for the team. I 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 touch point for this was like G P T five point two codex. Where all of a sudden the model was like way better at 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 before G P T five point two codex, the kinds of A I 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 know, you still needed to be at your laptop with your hands on the keyboard ish.

• 19:44Speaker 02

  And like it might go off and do a little bit of work, but you know, you you kind 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 G P T 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 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 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 A I.

• 20:28Speaker 02

  And I would say that now probably like most people are not even like opening I D E's. Maybe if they are opening I D E's to like, maybe you want to own the interface, right? So you'll like help flesh out like the interface between like two modules and then like A I fills it out. Or maybe you want to like collaborate on a plan, but then have A I fill it out. The code itself is not being written by humans anymore.

• 20:47Harry Stebbings?

  Will we have I D's as a part of the stack in twenty four months' time? Depends how you define IDE.

• 20:53Speaker 02

  So the formal definition, right? Integrated Development Environment. I mean, 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, I think of an IDE as 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, scheduling tasks or monitoring deploys or something, but it doesn't have text editing.

• 21:32Harry Stebbings?

  If we assume a large percentage is done by Codex in terms of the code produced, how do you do coding reviews and is AI responsible for internal coding reviews?

• 21:42Speaker 02

  There are a few things here. First off, the spec for what you want to do or the plan becomes more important than ever. Think like architecturally, like how should this code work? You know, 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 to have input. And this is very similar to like if you had a new hire who was new to your codebase. You know, they had to present a sort of a 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.

• 22:25Speaker 02

  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 like on the team and elsewhere like to like just ask Codex to review,

• 23:09Speaker 02

  you can then also set it up to just like automatically review. So like nearly all code at OpenAI is reviewed by Codex automatically whenever you push it to a Git 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.

• 23:30Harry Stebbings?

  You said something really interesting that you said for those that maybe haven't tried it yet or, you know, coming back to it. How do you think about retention with this category? I remember Tom Blomfield, who's a YC partner, tweeted months and months ago, but it stuck with me, weird brain, about the ease of transition between different providers, whether it was Curse or Claw Code or Codex. I can't remember which one it was, to be honest. But how sticky are users and how do you think about retention?

• 23:56Speaker 02

  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 is even a heavy word. It was just, we just established a 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 our standard for like giving the agent instructions and scripts. We pushed for those to be sorted in sort of a neutral named folder called agents instead of in like Codex or something.

• 24:39Speaker 02

  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 like 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.

• 25:25Speaker 02

  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 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.

• 26:02Harry Stebbings?

  But I'm a fan of Seven Powers, this brilliant book, which talks about kind of seven ways that businesses accrue value and sustainability. And like, you know, your stickiness or your retention is one. If we're on the same team with Codex, how do we create retentive patterns, behaviors, programs to ensure that people stay with Codex? And they don't flip to Cursa when there's a better model or Cloucode when there's a better model?

• 26:26Speaker 02

  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 you know, our mission here is to like ensure that like we safely deliver the benefits of AGI to all humanity. And so something that's like unintuitive to people about like the Codex team.

• 26:41Harry Stebbings?

  Alice, you actually, I know, but your job is the success of Codex. I get that as well.

• 26:46Speaker 02

  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.

• 26:59Harry Stebbings?

  This is so difficult for me as a venture capitalist to understand. You are aware of this.

• 27:04Speaker 02

  Yeah, I'm totally aware of it. Like, 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.

• 27:19Harry Stebbings?

  You learn because if they're closed and they improve, you don't learn.

• 27:24Speaker 02

  I don't think so. For example, there are a bunch of recent launches, like even 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, I think 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.

• 28:07Speaker 02

  And so I think we're, we're playing to win and we have 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.

• 28:22Harry Stebbings?

  Can I ask you, what would be the defining factor of winning? And I know I'm using venture language and you're brilliant and kind of much more free and open, but what was like the defining factor of winning? Again, if I push you, is it like GTM, which is like the biggest enterprise in the world do want to work with OpenAI? I have many friends in your sales team. The inbound that you get from the largest brands is incredible. So GTM, because of the incredible brand, product execution and just Codex being a fricking awesome product, or compute inference, speed, actual compute advantage? Which one is the defining winner?

• 28:59Speaker 02

  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 that generate revenue. And also that something 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

• 29:41Speaker 02

  like 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.

• 30:26Harry Stebbings?

  You said the word revenue there, which is one metric to measure a business against. When you think about like your metric of success, which you sit down with Sam or Brad or whoever it is. And say, hey, this is what we're optimizing for. What is the metric that you use as the defining North Star for your progression?

• 30:45Speaker 02

  It's actually not revenues, the primary, the primary is active users.

• 30:49Harry Stebbings?

  How do you measure active users, like daily active users?

• 30:52Speaker 02

  Yeah, we, so we measure weekly active users and it's, um, you know, did this person like actually do a turn in our product? You know, did they send a prompt?

• 31:00Harry Stebbings?

  Is weekly active a frequent enough metric, do you think? Sounds nice, but if this is actually replacing the IDE, is daily active not better?

• 31:11Speaker 02

  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 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 chat GPT. 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.

• 31:55Harry Stebbings?

  You said about chat that again, I jump around. Sorry, my brain.

• 31:58Speaker 02

  Oh, yeah.

• 31:59Harry Stebbings?

  My mother has to walk with me around London and she deals with this manic episodic brain. But you said about chat and the interface there. I'm really fascinated by this because it is a seemingly incredibly efficient input function for busy humans. But I spoke to Anish Akaya, who's a GP at Andreessen, and it came out the other day. And he's like, no, no, this was created by Sam and Elon and it works for very efficient people. But most of the planet want browser-based discovery interactions UIs. Do you think that chat will be the enduring UI in the next wave of AI interaction with humanity?

• 32:32Speaker 02

  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 believe that sci-fi is a really good predictor of what the future should look like. And usually it's pretty simple because it's a story and I think simple is usually right. It's going to be some just like entity that I can talk to however I want about whatever I want, right? 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'm just going to talk to a thing and it's just going to help. So I think what we're going to have is that we'll have chat or voice. Basically, conversational interface will be sort of the pillar of everything that you can talk to about anything.

• 33:17Speaker 02

  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 like 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 at some point, you want to you want to get to the show notes and like look at them yourself and like edit them yourself, right? You want to 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.

• 34:01Speaker 02

  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.

• 34:09Harry Stebbings?

  Totally get that. And it it kind of wrongly assumes on my behalf, a consumer interaction at some point in that journey. And I want to ask you, how do you think about like agent to agent experiences and designing experiences for agents? So we spoke about, for example, going to large enterprises and how you can be helpful. I'm just choosing the most boring thing ever, expense approval. You could have agent submission of expenses on my behalf for my trip to San Francisco. And then the agent on the flip side doing approvals for that from OpenAI's compliance department. How do you think about that and that paradigm shift?

• 34:43Speaker 02

  That's interesting. You know, to be honest, I'm not sure what that's going to look like. My like quickest answer to this is that like 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 like when people ask like, oh, like, how can I make my code base like more efficient for the agent to work with? The answer is often like, well, have you looked at it yourself? And is it is it easy for a human to work with? So like a very specific example would be like running tests in a code base. Naively, if you just like set up most test runners, they just like emit 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.

• 35:24Speaker 02

  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.

• 35:39Harry Stebbings?

  I mentioned our show on LinkedIn and a wonderful investor from a different company. It's like Harry Potter, you know Voldemort, and it's like, you know, he who shall not be named. I don't want Sam to kill me. But from another company was like, you got to you ask him, ask him, how do you think about a coding data mode? And does Anthropic have all the data now?

• 35:59Speaker 02

  I think that from what we've seen, and you know, and 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 like 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 a business, but have have a lot of like data, like say their Slack or something. Yeah, I think that that kind of knowledge work task distribution is like much harder than coding.

• 36:43Harry Stebbings?

  That's so interesting you say there about kind of the data that doesn't exist, so to speak. How do you think about your interactions with the data providers, your McCaws, your Churings, your Invisibles, your da, da, da, da, da, of the world? Like, will you spend 10x there? Or will you go, we are spending too much on data, we should do it ourselves and do data acquisition?

• 37:03Speaker 02

  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.

• 37:21Harry Stebbings?

  On the consumer side for Codex, we've spoken about like enterprises and going into them, how to engage in terms of developer experience, developer relations. Do you compete with a lovable and a rapid on a like low end consumer basis in a year or two's time? Is that a business where you're like, you know what, Codex is not for every person to create an about me or a small business to create their own site? How do you think about consumer in that way?

• 37:44Speaker 02

  Yeah, I would say that right now, it doesn't feel like we're competing super directly. But you know, I don't know if you saw our Super Bowl ad, the tagline of which is just 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 worldly. 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 chat. 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 GoChatGPT 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.

• 38:29Harry Stebbings?

  What would you most like to do differently, but for whatever reason you can't?

• 38:33Speaker 02

  I feel like it's been a very good few weeks for us. So we're very, I'm pretty jazzed about everything that's happening.

• 38:39Harry Stebbings?

  That's really interesting. You said it's been a very good few weeks for us, and I feel that. Does the team feel the changing winds of momentum, both in positive and negative cycles?

• 38:51Speaker 02

  Absolutely. We are very attuned to it, right? Like, if 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 to 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 twenty 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.

• 39:34Speaker 02

  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 free 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 like 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 service was the IDE extension and our CLI, which is a 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.

• 40:19Speaker 02

  And people are just loving using, even our biggest critics are converted. So yeah, and then we, and then we had the Super Bowl ad and then we went to free. And so 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.

• 41:04Speaker 02

  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 micro system 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

• 41:47Speaker 02

  also from like a safety perspective and a controls perspective.

• 41:50Harry Stebbings?

  How much weight should we place on benchmarks and evals?

• 41:53Speaker 02

  Probably, uh, 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.

• 42:28Harry Stebbings?

  How vibe-based life is. People wanna work with people they like is the lesson that I give to kids. People wanna work with them all they like. In terms of like market composition, as an investor, I have to think through how do I think about the eventual state of this given market, kind of a terminal state. How do you think about that? Is it like Uber and Lyft and like the majority of the market will be on CodeX or Claude Code? Or is it like a AWS, Azure, Google Cloud and a 333333?

• 42:57Speaker 02

  Okay, so I think this might end up with fewer providers that are capturing a lot of value i- in the long run. And here's why, like, 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 gonna end up with agents that kind of can do anything for you. This is kind of what I was saying earlier, like there's just like a super assistant, you talk to it about anything.

• 43:38Speaker 02

  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 you because then they won't achieve fluency. And 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 in 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, 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.

• 44:21Speaker 02

  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, there will just be this giant pull and everyone will talk about how they use that one agent for things.

• 45:03Speaker 02

  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.

• 45:09Harry Stebbings?

  You said about kind of agents not really proliferating in terms of usage other than coding. And actually, maybe this being the time and customer support is one of the examples. My question to you is, I'm an investor today. I'm looking for companies which will accrue value over time and provide incredible products to customers. There is a belief that the durability of revenue of large SaaS companies today is zero and that SaaS is dead because the model providers, you, Anthropic, others, are going to come for our lunch, so to speak. What would you advise me?

• 45:42Speaker 02

  Like, things are built for humans. Like, otherwise, what's the point, right? 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. You know, or does the SaaS company own some like really important system of record? It's probably not going away. Maybe those, 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? Well, I'm not the expert here, but I'm more nervous about that kind of company.

• 46:17Harry Stebbings?

  If we take that stance, Salesforce and ServiceNow, you know, they're down 20, 30, 40%. I think it's massively exaggerated. I think there are some companies that legitimately should be. Respectfully, I think Dropbox is in a very difficult position. And I think your Monday dot coms of the world, though, for the majority of SMBs and consumers who use it, which is the large majority of their market, actually, could they vibe code a to-do list? Yes. Would it be cost efficient to do so? Not really, actually, by the time you customize it and perfect it. And to be honest, the to-do list is generally pretty bland in terms of what you need to do. Add task, complete task, show historical tasks assigned to new members. It's not very difficult. And so actually, I think you just keep it.

• 47:02Harry Stebbings?

  And so I think it's massively overblown. And I think that's a classic knee-jerk reaction from markets. I do think like, I think you're going to come for customer support and I wouldn't want to be in that category.

• 47:14Speaker 02

  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 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, 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. Cause it's a 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, et cetera.

• 47:52Harry Stebbings?

  So again, if you were on my team as an investor, how would you think about interesting areas for us to invest in? In companies that will accrue value and not be threatened by model providers? Cause again, like you're going into health, you go into code, obviously code access is very clear. You go into customer support. Where are you not going? And why is Claude Code not going?

• 48:11Speaker 02

  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.

• 48:18Harry Stebbings?

  It's a really tough time to be investing today. And my, my answer is kind of twofold actually, which is like, number one, I look for things with physical infrastructure. I don't think you're going into energy supply. And then two is like the fintech and banking integrations, gnarly financial products. I don't think OpenAI is going to go into building 500 relationships with banks in Southeast Asia.

• 48:39Speaker 02

  I tend to agree. I, I, it kind of 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.

• 48:50Harry Stebbings?

  How bad is the war for talent? From the UK, we look at SF and I say to companies, it's better to build in Europe. Because it's impossible to acquire talent and it's impossible to retain it. Am I wrong?

• 49:00Speaker 02

  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 it. It's not like, you don't just get whoever you want for free.

• 49:16Harry Stebbings?

  Can I ask, at the entry price that you get stock at, is it still attractive for the best talent?

• 49:22Speaker 02

  I haven't had anyone tell me anything to the contrary.

• 49:25Harry Stebbings?

  To what extent do you think about like finding the perfect fit versus finding someone who's good enough?

• 49:31Speaker 02

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

• 49:34Harry Stebbings?

  Yeah.

• 49:35Speaker 02

  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, they 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.

• 49:51Harry Stebbings?

  I'm a CS student, okay? I'm at Stanford, I'm at Imperial, I'm at Cambridge, I'm wherever, ETH. Great institution. What would you advise me, knowing all that you know now, that would help me navigate the next five years of my career? I want to be valuable to the AI ecosystem environment as an engineer entering the workforce in the next year.

• 50:14Speaker 02

  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 codebase that you might be hired into has never been faster because you can go ask AI like a ton of questions about the codebase 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 like easier to build things. The thing that becomes scarcer is like agency, taste and like quality.

• 50:53Speaker 02

  I would urge you to like 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 like a normal resume does.

• 51:18Harry Stebbings?

  Final questions before we do a quick fire. You mentioned Dropbox earlier. The alumni from Dropbox is incredible. I mean, really amazing to see the talent that's come out of Dropbox. What's your single biggest lesson from Dropbox that has shaped some of your thinking now with OpenAI?

• 51:34Speaker 02

  Oh, I don't need to think about that one. 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 that tooling as a system of engagement, right? If people don't want to use your tool, if it doesn't 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 were building these agents. I'm like, if we build our agent purely as 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 need to deploy FDEs. It's going to be tough. But if you can build a system that people just love using, even if they only use it for partial tasks, over time, they'll get better and better at using it.

• 52:16Speaker 02

  And then 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.

• 52:23Harry Stebbings?

  How on earth do you reinvigorate growth at Dropbox today?

• 52:29Speaker 02

  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 locally in 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, 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 building really good desktop software?

• 53:13Speaker 02

  And this 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 for.

• 53:21Harry Stebbings?

  No, I love it and I really appreciate the response. Final one before we do a quickfire promise. I've been brought up in a world where margin matters. Software margins are wonderful and it's what makes software a brilliant category to invest in. We're seeing margin profiles that are very different in inference-heavy plays in particular. To what extent should I put that out of mind and appreciate that costs will come down, cost of tokens will come down? And actually, it's about usage and customer love, margins will come, or no, margins are actually fricking important. Keep that focus.

• 53:53Speaker 02

  I think both costs are going to come down significantly. And I also think that, you know, 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.

• 54:13Harry Stebbings?

  Dude, quickfire answer. I say a short statement, you give me your immediate thoughts. Does that sound okay?

• 54:18Speaker 02

  Yeah.

• 54:18Harry Stebbings?

  What have you changed your mind on most in the last 12 months?

• 54:22Speaker 02

  When I joined OpenAI, I thought that, and this is a little longer than 12 months ago, but when I joined OpenAI, I thought that we would all just be hanging out with our computers, screen sharing, but 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 through video and audio primarily.

• 54:57Harry Stebbings?

  Which lesser-known competitor do you respect most and why?

• 55:02Speaker 02

  The first one that came to mind was AMP.

• 55:04Harry Stebbings?

  AMP?

• 55:04Speaker 02

  I think they're really, yeah, AMP. It's out of the folks at Source Graph. 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 .agent/skills, which are what I was saying earlier about like making it so it's easier for users to manage all these different agents that they're trying. We obviously put out agents.md, but they put out agent.md and basically Quinn started this all by putting out a tweet that said, hey, do you guys buy the domain agents.md? We'll standardize to your spelling. And as small as that was, that initiated this whole standardization that I think has been awesome in the community.

• 55:42Harry Stebbings?

  Do you think the response to Anthropic's ads was the right response?

• 55:46Speaker 02

  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 thought that response was brilliant.

• 56:02Harry Stebbings?

  What's the hardest product decision you've had to make since being at Codex?

• 56:06Speaker 02

  Well, I can tell you the most painful product decision we had to make. For a while, Codex Cloud was 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 it would be harder to wind back at being 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 more reasonable limit, there was a lot of blowback from users and it was a very small minority of users who thought everything should be kind of like Soto free forever. But that blowback affected us everywhere because 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.

• 56:48Harry Stebbings?

  Data's like pricing, grandfathering pricing is just, it's such a hard thing. What do we do today in engineering or product that in five years' time, you'll look back on and go, oh, my God, can you believe that we did that?

• 57:00Speaker 02

  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 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, but basically it's been built to give you really strong deterministic guardrails over what the agent can do and like control of to like world back 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 in that.

• 57:42Speaker 02

  And then maybe eventually you add, 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 painful CI and deploy process, but you're just like having agents do things.

• 57:59Harry Stebbings?

  Weird question, but I'm intrigued. Are you the one providing agent guardrails? And what I mean by that is agents can go anywhere within an enterprise. Are you responsible providing those guardrails? Or is there a third-party matter provider who is saying, hey, Alex, you can't go into that. That's human resources. Or you can't go into that. That's marketing. How do you think about guardrail provisioning? And is that the role of the agent provider or a third-party provider?

• 58:24Speaker 02

  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, so 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 very bespoke things for very bespoke company needs, and there'll probably be a mix of both.

• 59:02Harry Stebbings?

  Final one for you, my friend. What are you most excited about when you look forward 10 years?

• 59:07Speaker 02

  This is probably going to happen in much less than 10 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 people like my grandma. What I'm most excited for is to get to 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 add an agent to our family WhatsApp or something, and it'll just start being useful to the family without anyone having to think harder about it than that. There are many other ways that that could happen, but I think concretely, that's the

• 59:52Speaker 02

  most obvious thing we can do with my grandma.

• 59:55Harry Stebbings?

  Dude, I so appreciate you, I so appreciate you putting up with my wondering questions and my very episodic mind. You've been fantastic, man.

• 1:00:03Speaker 02

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

• 1:00:10Harry Stebbings?

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• 1:03:03Harry Stebbings?

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