“Engineers are becoming sorcerers” | The future of software development with OpenAI’s Sherwin Wu

95% of engineers use Codex. 100% of our PRs are reviewed by Codex. For engineers, I don't know what job has changed more in the past couple of years. Engineers are becoming tech leads. They're managing fleets and fleets of agents. It literally feels like we're wizards casting all these spells. And these spells are kind of like going out and doing things for you. What do you think people aren't pricing in yet? The second or third order effects of the one person billion dollar startup. To enable a one person billion dollar startup, there might be a hundred other small startups building bespoke software. So I think we might actually enter into a golden age of B2B SaaS. I've been hearing more and more there's this stress people feel when their agents aren't working. There's a team that's actually doing an experiment right now with an open AI where they are maintaining a 100% Codex written code base. They run into the exact problems that you're describing. And so usually you're like, all right, I'll roll up my sleeves and figure it out. This team doesn't have that escape hatch. You've shared that listening to customers is not always the right strategy in AI. The field and the models themselves are just changing so, so quickly. They tend to like disrupt themselves. The models will eat your scaffolding for breakfast. What's your advice to folks that are like, okay, I don't want to miss the boat. Make sure you're building for where the models are going and not where they are today. There's a quote from Kevin Whale, our VP of science here. And he likes saying this is the worst the models will ever be. Today, my guest is Sherwin Wu, head of engineering for OpenAI's API and developer platform. Considering that essentially every AI startup integrates with OpenAI's APIs, Sherwin has an incredibly unique and broad view into what is going on and where things are heading. Let's get into it after a short word from our wonderful sponsors. Today's episode is brought to you by DX, the developer intelligence platform designed by leading researchers. To thrive in the AI era, organizations need to adapt quickly. But many organization leaders struggle to answer pressing questions like, which tools are working? How are they being used? What's actually driving value? DX provides the data and insights that leaders need to navigate this shift. With DX, companies like Dropbox, Booking.com, Adian, and Intercom get a deep understanding of how AI is providing value to their developers and what impact AI is having on engineering productivity. To learn more, visit DX's website at getdx.com slash Lenny. That's getdx.com slash Lenny. Applications break in all kinds of ways, crashes, slowdowns, regressions, and the stuff that you only see once real users show up. Sentry catches it all. See what happened, where, and why. Down to the commit that introduced the error, the developer who shipped it, and the exact line of code, all in one connected view. I've definitely tried the five tabs and Slack thread approach to debugging. This is better. Sentry shows you how the request moved, what ran, what slowed down, and what users saw. Sear, Sentry's AI debugging agent, takes it from there. It uses all of that Sentry context to tell you the root cause, suggest a fix, and even opens a PR for you. It also reviews your PRs and flags any breaking changes with fixes ready to go. Try Sentry and Sear for free at sentry.io slash Lenny, and use code Lenny for $100 in Sentry credits. That's S-E-N-T-R-Y.io slash Lenny. Sherwin, thank you so much for being here, and welcome to the podcast. Thank you, thank you for having me. I want to start with what's feeling like a barometer of progress in AI, especially in engineering. What percentage of your code, if you even write code anymore, and your team's code, is written by AI at this point? I do write code occasionally now still. I'd actually say for managers like myself, it's way easier to use these AI tools than to manually code at this point. And so I know for myself and some of the other EMs, engineering managers at OpenAI, all of our code is written by Codex at this point. But more broadly, there's just been this, there's just so much energy. There's like a tangible energy internally around just how far these tools have gotten, how good Codex as a tool has gotten for us. And it's a little hard for us to exactly measure how much of the code is written, because the vast majority of it, I'd say like close to 100%, is usually generated by AI first. What we do track though, is at this point, the vast majority of engineers use Codex on a daily basis. Like 95% of engineers use Codex. 100% of our PRs are reviewed by Codex daily as well. So basically any code that goes into production that's merged in, Codex kind of has its eyes on and suggests improvements, suggests changes in the PRs. And so that's kind of what we're seeing internally, but by and large, the most exciting is just the energy that there is. Another observation that we've had is engineers who tend to use Codex more, open way more PRs. So they're actually opening 70% more PRs than the engineers who aren't using Codex as much. And the gap is widening. So I feel like the people who are opening more PRs are starting to learn how to use the tool more and more, get more efficient, and that 70% gap keeps growing over time. And so it might've actually increased since I last looked at the number. Okay, so just to make sure we hear what you're saying, you're saying all of the code of these 95% engineers at OpenAI is written by AI. It's written and then they review it. Yep, yep. It's like crazy that that's almost like not crazy anymore, that we're just like getting used to this. I think there's still some getting used to, to be clear. There's also, I think, some engineers who I think trust Codex a little bit less, but basically every day I talk to someone who is blown away by something that I can do and kind of like their bar of trust kind of, or like how much they trust the model to do on its own goes up over and over over time. And there's a quote from Kevin Whale, our VP of science here. And he likes saying, this is the worst the models will ever be. And so this is the worst that the models ever be for software engineering as well. And so over time, you just see people trusting it more and more, and then we'll see the models get better and better as well. Yeah, Kevin Whale, former podcast guest. He said exactly that line on this podcast. Yeah, yeah, yeah. A few times. Yeah. Peter, the Clodbot slash Moltbot slash OpenClaw is what it's called now, developer recently shared that he uses Codex for his work and he feels like anytime it does things, he just trusts that it has done the right job and he's just like almost certain he could just commit it to master and it'll be great. Yeah, yeah. He's a great user of Codex. I know he's in close touch with the team, gives us great feedback. I'm not surprised that he uses it. I mean, sorry, it's called OpenClaw now. OpenClaw, yeah. OpenClaw is a great product. And then I saw that this morning, I mean, this is very recent, but this morning, I think Moltbook kind of like was shared as well. Seeing all of the AI agents talk to each other is pretty surreal. It's basically her is happening in real life is what I'm hearing. Yeah, yeah. So just like coming back to this crazy moment we are living through for engineers in particular, we've gone from you write every line of code to now AI is writing all of your code. I don't know what job has changed more in the past couple of years, like job that we didn't expect to change this much, where just like the job of an engineer is so different in the entire lifespan of an engineer. Like in the past couple of years, it's now shifted to I don't write any more code. How do you imagine the role of an engineer and the job of a software engineer looks in the next couple of years? Just like, what is that job? Yeah, I mean, it's honestly been really cool to see. And it's part of where the excitement is because like the job is likely to change pretty significantly over the next one to two years. It kind of feels like we're still figuring things out though. And so there's like this excitement I know, especially from some of the software engineers of like, we're in this rare moment, maybe over the next 12 to 24 months where we'll kind of get to figure things out ourselves and set our standards for ourselves. In terms of where I see this moving. So I think there's a common thing that everyone's saying, which is, people are generally, like I see engineers are becoming tech leads. They're basically like managers now. They're managing fleets and fleets of agents. I know many of the engineers on my team basically have like 10 to 20 threads kind of being pulled on at the same time. Obviously not active running codecs jobs, but just a lot of parallel threads. They're checking in on what they're doing. They're steering the agents and codecs and giving it feedback. And so their job has kind of really changed from just writing the code itself into being almost like a manager. In terms of where I think this will go one to two years from now. So one kind of metaphor that I kind of always come back to here is actually from this programming textbook that I read back in college called SICP. I don't know if you've heard of it. Structure and Interpretation of Computer Programs. So S-I-C-P. At MIT, it was really popular and it was actually used as the introductory, it was the textbook for the intro programming course for a very long time. And it kind of has this cult following. It teaches you programming. It teaches you a dialect of Lisp called Scheme. And so it introduces you to functional programming. It's very mind opening that way. But the thing that was memorable for me about that book, so I kind of read it in college. The very beginning of it kind of describes programming as a discipline and draws this metaphor to basically like sorcery. Like it says software engineers are like wizards and programming languages are like incantations and you're issuing these spells and these spells are kind of like going out and doing things for you. And the challenge is like what incantation do you have to say to make the program do what you want? And this book was written in 1980. So this is a while ago. And I think that metaphor is actually like kind of persisted over time. And I think it's actually playing out as we move into this new era of vibe coding or just like what software engineering will look like because programming languages were basically these incantations. They've changed over time. And the challenge is always, and the trend has been that these, it's been easier and easier to kind of get the computer to do what you want via programming. And I think the current wave of AI is probably the next stage of that evolution. It is now literally incantations because you can tell codecs, you can tell cursor exactly what you want to do and then it'll all go do it for you. And I particularly like the wizard and like the sorcery analogy because I think our current status is starting to move towards kind of like the sorcerer's apprentice, from Fantasia where Mickey Mouse is like, he finds the sorcerer's hat and he tries to do all these things. And I actually think it's a really apt analogy because one, it's just, it's really powerful now. These incantations you can do is extremely high leverage, but you kind of have to know what you're doing, right? Like in sorcerer's apprentice, the whole plot is like Mickey goes wild, the brooms like go crazy and everything's flooding. I think he literally sets the brooms off on a task and then goes to sleep. And so it's like vibe coding at its greatest. And then eventually the old sorcerer comes back and like cleans everything up. And when I see engineers kind of like doing these 20 different codecs threads at a time, there is some skill and there's some seniority and like a lot of thought that needs to go into this because you want to make sure that the models aren't going off the rails. You definitely don't want to just like completely go away and like ignore the thing, but it's also extremely high leverage. Like a very senior engineer who's really proficient with these tools can now just do way more things via what they're doing. And I think this is also what makes it fun. Like it literally feels like we're wizards now. It feels like we're closer to making it feel like this like magical experience where we're casting all these spells and having software do all these things for you. I was thinking of the Sorcerer's Apprentice exactly as the metaphor, as you were describing that. So I'm glad you went there. A previous podcast guest described it as you have a genie that grants you wishes. And it's a useful frame because you have to be very clear about the wish you want. Like if you want to be big, like how big can you be? Yeah, or it might be like the monkey's paw type thing where you know, you caught what you want, but what are the side effects? Yeah, yeah, I think that, and the analogy is great. And yeah, the crazy thing for me is just the staying power of that book, Sick Bee. Like it's called the wizard book. You know, people call it the wizard book because that is the metaphor that they kind of weave throughout the book. And we've basically reached that point now, which is really cool. There's two kind of threads I want to follow here. One is, I've been hearing more and more, there's this like stress that people feel when their agents aren't working. You fire off all these, you know, codex agents and then you have to keep staying on top of them. Oh shit, one's not working, I'm wasting time. Do you feel that? Do you feel that across your team at all? Yeah, yeah, I mean, it happens all the time. And I actually think like this is where the interesting part of all of this lies right now because these models aren't perfect, these tools aren't perfect, and we're still trying to figure out how to best interact with codex or with these AI agents to get work done. We see this come up all the time. There's a particularly interesting team that we have internally. So there's a team that's actually doing an experiment right now with an open AI, where they are basically maintaining a 100% codex written code base. So, you know, like, you'll have the AI write code, but you'll obviously end up like rewriting a lot of it and you might need to like double check and change things. But this team is just fully codex-pilled and just like leaning in entirely. And they run into the exact problems that you're describing, which is like, you know, their challenge is, you know, I want to get this thing, this feature built, but I can't get the agent to do it. And so usually there's an escape hatch where, you know, then you're like, all right, I'll roll up my sleeves and like figure it out. And then instead of using codex, I might use like tab complete and cursor and things like that. But this team, for the experiment, this team doesn't have that escape hatch. And so then the challenge is like, how do I get the agent to do this? And I actually think we're going to be publishing a blog post from some of our learnings here, but a lot of fascinating like paradigms and best practices are falling out of this. One interesting thing that we've noticed, I don't know if this is what you kind of feel, but we definitely feel it here is, a lot of the time when the coding agent is not doing what you want, it's usually a problem with context and just like information that you've given it. It's just either underspecified or there's just not enough information around how to do something available to the agent, available to codex. And so when you have to solve it through that, the challenge is then to add documentation and actually work around this limitation and basically encode more tribal knowledge that's in your head somehow into the code base, either via, you know, code comments itself or code structure itself, or via text files like, you know, .md files, skills, any type of additional resources within the repository so that the model can better do its task. There's a whole bunch of other learnings from this group, which I think is fascinating to explore, but yeah, kind of removing that escape hatch of no longer using the AI has allowed them to start piecing together a lot of the problems that we'll have to solve if we really want to lean into agents. Another issue people ran into, you talked about how people are shipping PRs like crazy, a lot more PRs if they're working with AI. Obviously code review is becoming a bigger challenge. Is there anything you've figured out on your team to help speed that up, to make that scale and not just create this terrible job for people where they're just sitting there reviewing PRs all day? Yeah, I mean, one thing is codex reviews 100% of all of our PRs at this point. And so I actually think, so one really interesting thing that's happened is the things that we tend to hand to the models immediately tend to be the things that annoy us or are the most boring parts of software engineering. It's also why it's more fun now because we get to do more of the fun things. For me, speaking more for myself, I really hated code reviews. It was one of the worst things for me. And then I remember in my first job out of college, it was at Quora, I was working on the newsfeed. And so I owned the code for the newsfeed. And so I was a reviewer for newsfeed. And it was just like the central piece of code that everyone would touch. And so I would just, every morning I'd log in and be like 20 to 30 code reviews. I was just like, oh my goodness, I gotta like, you know, get through all of these. I would procrastinate and then it grows to like 50. And so there's just like a lot of code reviews. Codex is really good at reviewing code. So actually one thing that we've noticed that 5.2 in particular has gotten extremely, strongly adept at is reviewing code. And especially when you kind of steer it in the right direction. And so for code reviews, yeah, we create a lot of PRs, but Codex reviews all of them. And it makes, you know, code reviews go from a, you know, I don't know, 10, 15 minute task to sometimes even just like a two to three minute task because you have a bunch of suggestions already baked in. A lot of the times people will, especially for small PRs, like you actually don't even need people to review. We kind of trust Codex in this way. The original author kind of looks at Codex. It is, you know, the benefit of code reviews is to have a second pair of eyes to make sure that you're not doing anything dumb. Codex is a pretty smart second pair of eyes at this point. And so that's something that we've heavily leaned into. The general CI process and like the post kind of push and like deployment processes also have been heavily automated via Codex internally at this point. If you talk to a lot of engineers, the thing that annoys them the most is after you've written your beautiful code, like how do you get it into production? You know, you gotta run through all these tests. You gotta like, you know, lint errors, you gotta do all the code review. There's a lot of automated stuff you can do with Codex. And so we've actually built some tools internally that help automate that process, automate the lint. You know, if there's like a lint error, it's a very easy Codex fix. And then it could just patch it and then kind of restart the CI process. So all of that is, we're trying to collapse as little work for an engineer as possible, which, and the byproduct of which is they can now merge and push out a lot more peers. Codex writing the code, Codex reviewing its own code. I'm curious if you're open to using other models to review your model's work. Is that a path or is it just, it's good enough, we don't need anything else? So I will say there's definitely a circular thing here. And like going back to Sorcerer's Apprentice, like you wanna make sure you're not letting the berms go crazy here. And so, you know, we're very thoughtful, I'd say, around which PRs kind of are completely just Codex reviewed. Most people still obviously take a look at their PRs. And so it's not like it's going to zero. It's more like going from, you know, 100% attention to like 30% attention, which just helps things push through. In terms of like multiple models, so we obviously test a lot of models internally. And so we have a lot of those. We use external models less. It's, we think it's important to kind of dog food our own models and kind of like get feedback there. But you can also, you know, there are a lot of like internal variants of models that you can use to give you different perspectives here as well. And we found that to work quite well. Okay, so just to make sure we get like a barometer of today's world at OpenAI in terms of AI and code, just so I understand, and then I wanna move on to a different topic. Is 100% of code across OpenAI is written by Codex at this point? Is that the way to frame it? I wouldn't make the statement that 100% of code running in production today is written by AI. And it's kind of hard to do attribution there. But the, like almost every engineer heavily uses Codex in all of their tasks at this point. And so I, you know, if I were to guesstimate like the vast majority of code at this point is it was probably authored by AI. Incredible. Okay, so there's a lot of talk and we've been talking about kind of the IC role, the work of an IC engineer. There's less talk about the changing role of a manager, especially an engineering manager. How has your life as a manager changed with the rise of AI and just what do you, where do you think the managers, what's the role of a manager in the future? It's definitely changed less than an engineer. There's no, you know, Codex for managers just yet. However, I use Codex quite a bit for some of the, some of the like kind of more manager-y tasks that I do. I'd say a couple of things are changing. There are like some trends. So I don't think it's changed that much yet, but I see trends and I think if you play it out, you can kind of see where a lot of this is going. One thing that's becoming increasingly clear is Codex really empowers like top performers to get a lot, like to be a lot more productive. And so it really like, and I think this may be true for AI more broadly, like across society, which is like the people who really lean in are like the people who have high agency or like will really get good at these tools will kind of supercharge themselves. And so I'm kind of noticing this now as well, which is like the top performers kind of end up being a lot more productive. And so you see a broader spread in team productivity in this way. So one thing that I've always done as a management philosophy is to spend actually the majority of my time with top performers, just like make sure they're on block, make sure they're happy, make sure they feel productive and they feel heard. I think this is even more true in an AI world where your top performers are gonna just like really be shooting ahead using these tools. I think one example is that the team that's maintaining a 100% Codex generated code base, like just letting them kind of rip and see what's happening there is something that's paid dividends. So I think that's kind of one trend that I'm seeing where spending even more time with top performers for managers, I think is likely gonna continue. The other thing is, so this is more an observation, but my sense is with a lot of these AI tools available to managers, so less like writing code, but just things like chat GPT with organizational knowledge, like being able to do research and understanding organizational context a lot better. Another good example, a lot better. Another good example is we're doing performance reviews right now and it's actually really easy to use chat GPT with internal knowledge hooked up to GitHub and like our Notion docs and Google docs to get a really good sense of what this person has done over the last 12 months and writing a little deep research report for it. My sense is I think managers will be able to manage much larger teams in this world. Kind of like how software engineers are managing 20 to 30 codexes. My sense of these tools will allow managers people manage to be higher leverage and will allow them to to to manage you know teams of way more than than the current best practice of I think it's like six to eight right for software engineering. You kind of see this applied to you know like the non-engineering domains like support or operations where it's like you know previously where previously like the size of a support team might be limited but like as you can pass off more things to agents you can actually do more work and also manage more people this way. I think the same thing might happen for people management as well especially in tech companies and we're already seeing this there's some teams where their EM's managing you know quite a few people and they're doing it pretty adeptly because of some of these tools where they can get higher leverage and understand what their team's doing understand organizational context a little bit better and operate in that way. I love this advice that the way you described is you've always leaned into top performers and spent more time with them unblock them make sure they're happy. The way Marc Andreessen he was just on the podcast the way he phrased it is AI makes good people better and it makes great people exceptional. Yeah yeah. And what you're saying here is just just doing this more and more is probably the right move spending more time with the best people on your team to unblock them make sure they have everything they need. Yeah a very good example right now is there are I would say like a group of engineers internally who are really codex filled and are thinking through what the best practices are for interacting with this model and that is just an extremely high leverage thing for them to do and so just like as a manager I'm just like yeah go explore this you know whatever best practices come out of this you know we we have to share with the org well we'll you know we'll we'll we do all these knowledge sharing sessions we'll we'll like share documents and like best practices everywhere so things like that just uh you know elevate everyone and uh and so I view that as like you know another example of this trend um uh that um that we're seeing where the top performers really get exceptional. People just like have a sense this is big AI is changing so much the world is changing uh it's going to be a huge deal. What do you think people aren't pricing in yet into what will change into where things are heading just like what's an example of something you think are like okay we're not realizing this yet. So one of my favorite kind of uh uh like phrases or like things that have come out of this whole AI wave is is the idea of the one person billion dollar startup. I think I actually think Sam may have keyed it or like uh Sam may have been the first one to say it but it's fascinating to think about right it's like yeah if you know if people are so high leverage at some point there will likely be um a one person billion dollar startup um and while I think that's really really cool I think people aren't really pricing the second or third order effects of this and and really what you know because because what the one person billion dollar startup implies is that there's you know one person can just have so much more agency and so much more leverage using one of these tools um that it is just super easy for them to get everything done that they need to for for their business to you know ultimately create something that's a billion dollars. But I think there are a couple other implications of this so one of them is uh uh if it's easy for a person to create a one person bill or if it's possible for a person to create a one person billion dollar startup it also means it's way easier for people to just create startups in general like I actually think this will like one second order effect of this is I think there's going to be a huge like startup boom and like small like SMB style boom um where anyone can build software for anything right like uh uh one uh you're kind of starting to see starting to see this play out in the AI startup scene where software's became a lot more vertical oriented where like these verticals uh like creating some AI tool for some vertical tends to work quite well because you know you really lean into uh that particular domain you like really understand the use case for it and so if you play out AI there's no reason why you can't have like 100x more of these these startups and so I think I think one world that we might end up seeing happen is in order to enable a one person billion dollar startup there might be like a hundred other small startups building bespoke software that works extremely well to support other types of you know small small one person you know billion dollar startups and so I think we might actually enter into a golden age of like b2b sass and just like software and startups in general and so I think I think that's that's a really interesting trend to to kind of see because as it's as it's really as it gets easier and easier to build software um as it's easier and easier to uh you know uh uh run a company um you might actually just end up seeing way more of these these these startups and so the way I've been thinking about is like yeah there might be one uh one person billion dollar startup or there might be like a hundred you know uh 100 million dollar startups there might be tens of thousands of 10 million dollar startups and as an individual it's actually pretty great to have a 10 million dollar business like that's like enough for your stuff for life at that point and so you know we might really see see an explosion in that way and I feel like people aren't aren't really you know pressing that in um there's another kind of like third order effect of this you know and again all of these I guess you get to the further and further out predictions I think uh are there's a lot of uncertainty I think if we end up moving to this world where you end up with these like kind of micro companies building software that works for one or two people uh who own the company and and are working there um I think the startup ecosystem will change I think the VC ecosystem will change you know it might we might end up in uh in a world where there's just like a handful of big players that are offering platforms and supporting all of these startups but you know the types of venture scale return startups that can really hundred or thousand x your your investment might actually end up shrinking if you end up having a bunch of these you know smaller 10 to 50 million dollar uh companies which are not great for venture style returns but are great for the individuals the high agency individuals who are now you know really leaning to AI to to build these businesses for themselves I love how many uh order like uh order effects we've been through uh when I hear the fourth order effect now sure I'm just joking I can't I it's too fourth order is too too it's too giga brain for me I can't I can't think that far ahead it's like inception where just everything gets slower every time you go deeper into someone yeah every layer uh okay so the billion dollar startup I've been I think about this a lot because I I'm not going to be a billion dollar startup because what I'm doing is not venture scale in any way and not super high leverage but just could see how many support tickets I get from just like the most ridiculous things it's hard for me to imagine one person like I'm bearish on this billion dollar startup I just want to share this thought uh simply because of the support costs even if AI is helping you at a billion dollars just like unless your ACVs are you know very high and you have very few customers it's just dealing with support and people are like you know like they can solve their own problems but they're like I'll email support I'll ask about this thing just dealing with that is hard to scale is in my experience so unless you have in my opinion unless you have a bunch of contractors which I don't know does that count as a single person company I feel like it's very difficult to scale a billion dollar startup and not have someone helping you with at least the support work and AI I think will only take you so far so I I think that's true and actually I think my view on it is is slightly different which is I think that you're you know Lenny's podcast might end up becoming a billion dollar startup but what I think might happen is instead of you kind of being the one person who has to dispatch an AI to solve and fix those support tickets I think what might end up happening is there might be a whole smattering of other startups that are building software and super and like super tailored towards what you might need and so you know there might be like 10 or 20 startups that build support software for podcasts and newsletters and that might be a one-person startup like it doesn't need to be a big one and it's and you know they might be able to just code up this product very very easily they are able to kind of like build their own thing and because it's so tailored and unique and hopefully you know useful for you it might be something that you purchase as the one person billion dollar startup I would buy that yeah there's like a question of like what you in-house and what you what you like kind of outsource and what I think might happen is because the cost of writing software and building products is is is collapsing so much you might end up outsourcing a lot of this and in doing so reducing the size of your company and so that's kind of the world that I think might end up happening again there's like high uncertainty in what might play out here but the end result still might be a one like one person driving this like high high massive leveraged company that might actually reach a billion dollars I could see that I also think about peter at clodbot slash multbot slash open claw of just like how he barraged is right now by all these asks and emails and pings and dms and prs just like I'm curious to and he's not even making any money out of this thing yeah I can't imagine what it's like to be him right now it must be like absolutely insane it's probably like uh uh you know like the the months after we launched chat tvt the craziness that was uh as one as one man uh he's coming on the pod by the way in a week oh that's exciting yeah uh maybe the fourth order effect is distribution becomes increasingly important because there are so many freaking things trying to get your attention so people with an audience and platform I think become more and more valuable which is good good stuff okay uh I wanted to come back actually to your management stuff so I really loved your insight about spending more time with top performers has been really successful to you just thinking about you as a manager of a team that is building the platform that powers basically the entire ai economy like every ai startup is building on your api uh clearly you're doing a great job what other kind of core management lessons have you learned what do you find is really important and and key to your success as a manager of engineers and just people yeah um I think a lot of the lessons that I've learned here I don't know how specific it is to the opening api or some of our enterprise products in particular I think my my management philosophy has obviously changed over time but I think it's uh probably stayed the same more than it's changed uh over time uh one of these principles is kind of what I talked to you about before which is you know spending a lot of time with with top performers like actually spending and like to be very concrete like it's like more than 50 of your time with your top performers with maybe your top like 10 uh performers and really really trying your best to empower them the way that I think about it is um is is kind of come back to this analogy of software engineer as as as a surgeon um which comes from the the mythical man month book so it's actually it's funny so I pull it from the book but in the book they actually describe this world where um I think they were like predicting the future you know because because I think the book was written like in the 70s or something um they said that software engineering might end up moving into a world where that software engineers are like surgeons or like in a surgery room there's like one person doing the work um and uh you know there's the one person like cutting whatever and like doing all the surgery and everyone else in the room is there to just support them right it's like the nurse and like the assistant the resident and the fellow and then the surgeon's like I need a scalpel and they give them scalpel and then uh they're like I need you know this tool and this machine and they'll bring it over everyone's there to just like you know support the one uh surgeon and so the the mythical mammoth actually predicted that that is kind of the direction that software is going to go I don't think that's exactly played out where like you know it's much more collaborative and like it's not only one person doing the work but I've always really liked that analogy and and and and uh that analogy is actually what I strive to uh uh kind of like emulate in my own management philosophy which is um software engineering isn't really like surgery where it's not just one person doing work but the way in which I like treating the people on my team and the way that I act as a manager is I want to empower them make them feel like they're a surgeon um and in so far at like as like making sure that I'm supporting them and making sure they have everything that they need to do their work and it feels like they have an army of people kind of supporting them um and looking around corners and giving them everything that they need when it's really just me as the manager and so like the example that I give is is looking around corners and unblocking people especially from an organizational perspective is extremely extremely useful and again going back to the AI conversations even more important nowadays right like uh if people are just like cranking PR after PR the main thing bottlenecking uh progress and and you know shipping something tends to be organizational or like process oriented and if you as a manager can kind of look around corners and kind of unblock the team if you can you know like if the surgeon needs scalpel but you know the manager kind of already has a scalpel ready for them that that's the best case scenario that's kind of the way that I approach uh um management and especially uh engineering management and so that's something that that's really really um stuck with me over time and even though you know software engineers aren't exactly surgeons that metaphor has always kind of stayed in my mind as of as of uh for my career. I love that and I feel like I wonder if that's something AI can help with is look around corners and predict here this engineer is going to be blocked by this decision we need to figure this out we need to get that's actually a really good uh point I haven't tried this yet but I wonder what would happen if I ask uh chat gbt hooked up to company knowledge you know like what are the active blockers uh look through all the notion docs what are that maybe slack messages you know it's probably in slack somewhere what are the active blockers on my team and is there something I can do to to help um now that's very I have not thought about that but you're right just had an insight right here yeah yeah and it's I think even more interestingly what do you anticipate will be a blocker for this engineer or this team in the in the coming months or yeah you asked that you asked the model he asked the AI to do the second and third order things anticipate that and anticipate what the blockers will be next month too uh I think we've got it we've got a good idea right here yeah yeah this episode is brought to you by data dog now home to epo the leading experimentation and feature flagging platform product managers at the world's best companies use data dog the same platform their engineers rely on every day to connect product insights to product issues like bugs ux friction and business impact it starts with product analytics where pms can watch replays review funnels dive into retention and explore their growth metrics where other tools stop data dog goes even further it helps you actually diagnose the impact of funnel drop-offs and bugs and ux friction once you know where to focus experiments prove what works I saw this firsthand when I was at Airbnb where our experimentation platform was critical for analyzing what worked and where things went wrong and the same team that built the experimentation at Airbnb built epo data dog then lets you go beyond the numbers with session replay watch exactly how users interact with heat maps and scroll maps to truly understand their behavior and all of this is powered by feature flags that are tied to real-time data so that you can roll out safely target precisely and learn continuously data dog is more than engineering metrics it's where great product teams learn faster fix smarter and ship with confidence request a demo at datadoghq.com slash lenny that's datadoghq.com slash lenny okay i'm going to shift to talking about the api and the platform that you all build some so you work with a lot of companies implementing your api your platform building on on your on your tools you told me that you find that a lot of companies actually have negative ry on their ai deployments which i think is what a lot of people you read about and feel and think and it's interesting actually seeing that what what's going on there what are they doing wrong what do you what what's happening in the world of ai and deployments in our way yeah so to be clear i i don't like explicitly see quantitative numbers around this uh you know uh it's actually really hard to measure these things but especially from observing some companies kind of trying to do ai i would not be surprised if a lot of ai deployments are actually you know negative roi i mean part of this too is i think there's also general sentiment um from uh folks uh around the country uh like basically outside of tech that ai is being forced onto them um and i think part of this is is is uh uh probably a symptom of some negative roi uh ai deployments a couple of things i've observed around this so one one thing is and i think i come back to this again and again like i think we in silicon valley just forget that we live in a bubble like we are so like twitter is a bubble it's our x is a bubble um silicon valley's a bubble software engineering is a bubble most people uh in the world most people in the u.s are not software engineers are not very ai-pilled um are not following every single model release and so uh uh and so we're just like highly out of the loop on how to use this technology and so you know like we um we always talk about all these like best practices for codex all these like codex pill people within open ai i'm sure everyone on x who posts are like crazy power users of these ai tools you know they lean into skills they lean into agents.md mcps uh yes yeah all of that and uh when i talk to some of these companies and i and i talk to the actual employees using these it's like the most basic thing that they're trying to do and they like have very little understanding of exactly how this technology works and so that that's that's kind of like one big observation for me which is like they're asking very simple questions of these things they're really not not pushing it just yet and so that kind of goes back to uh that kind of ties into to what i what i think more companies do or like what should do or what what a more ideal ai deployment setup looks like um and this is kind of how we've run things within open ai too um the companies where i think it started to work really well have a combination of both top down buy-in so it's like the c-suite it's like you know we're we want to become an ai ai first company so there's buy-in they buy the tools they have you know exec support but it also has bottoms up adoption and buy-in and so what i mean by that is it has like actual employees doing the work who are really excited about the technology and are willing to learn evangelize build best practices and kind of like knowledge share within the organization we've we've seen this a lot internally so like obviously open ai has always wanted to be a very ai centric company but where when it really started taking off is when was with the introduction of codecs and these tools where like people them like actual employees themselves could start applying it to their work and i think you really need this because at the end of the day everyone's work is like very different it's like very unique software engineering is different than finances different than operations different than go-to market and sales and so there's like a lot of these like last mile intricacies of work that needs to really be done in a bottoms up fashion and so my sense is a lot of these these air deployments don't have like don't have bottoms up adoption like it was like an exec mandate and it's extremely top down and it's very divorced from what the actual work looks like and as an end result you end up with a giant workforce that doesn't really understand the technology is like i know i'm supposed to use this and maybe it's like on my performance review too but i'm not sure what to do and they look around no one else is doing it there's no one else to learn from and so my my you know my recommendation for companies kind of pushing this is is find or maybe even staff a full-time team internally that is this kind of tiger team internally that can explore the full extent of the capabilities apply to specific workflows do the knowledge sharing create excitement within folks who might want to use this technology because in the absence of that it's very difficult to it's actually very difficult to pick up and who would you put on this tiger team is it like engineer led do you find in your experience is it a cross-functional sort of team yeah it's it's interesting so um also a lot of companies don't have software engineers uh and so the the pattern i've seen is it tends to be these like software engineering adjacent like basically technical people but are not software engineers i think those are the ones who tend to get most excited uh around this it's like you know maybe the it's like maybe the like you know support team operations lead who doesn't code but loves using these tools and you know is like an excel wizard or something and so it's like technical adjacent or like coding adjacent and like you know pretty technical those are the kinds of like those are the kinds of people i've seen in these companies who just like really light up and get excited around this um and you can usually build a team a team around that but yeah it's like oftentimes not software engineers software engineers i think will understand this but not every company has a software engineers um it's actually kind of a rarity they're they're hard to find they're expensive uh and so it's these other other types of folks what i'm hearing is the anti-pattern is top down this is very the co found an exec team just like we are gonna go ai first we're gonna lead into ai everyone's gonna be judged on their performance using ai tools how much your productivity is increasing thanks to ai and without with that being just top down and not creating a team that is bottom up spreading the the gospel you find that doesn't work yeah yeah exactly and the advice is find the people that are most excited and instead of kind of having them spread out through the organization you're what you find works is create a little ai kind of evangelist team that finds ways to use it and kind of spreads it across the work to use it and kind of spreads it across the work. Yeah, I mean, another, it's kind of like hearing you play back to me, another way to think about it, kind of tying back to my own management philosophies, is find the high performers in AI adoption and empower them, you know, let them build hackathons, let them, you know, hold seminars, do knowledge sharing, kind of create the seeds of excitement internally. Okay, amazing. There's a couple hot takes I want to hear from you, something that I've seen you talk about and share. One is you've shared that talking to customers and listening to customers is not always the right strategy in AI and it might often lead you astray. I don't know if it's that hot of a take. I think the main thing here is obviously you should talk to your customers. It's useful to talk to customers. I just think the AI field, especially what I've seen over the last three years working on the API and seeing all that evolve, is the field and the models themselves are just changing so, so quickly. They tend to disrupt themselves, especially around the tooling and the scaffolding space. So there's this quote that I read actually earlier this week from an X article by this guy named Nicholas who's the founder of a startup called FinTool where I think he was sharing a lot of the best practices that he has learned through building AI agents for financial services I think at a startup, FinTool. And he had this phrase that I thought was really good, which is, the models will eat your scaffolding for breakfast. If you rewind back to 2022, right when ChatGPT launched, these models were pretty raw and there was all this product scaffolding and things, especially in the developer space, to basically try and steer the model and build a scaffolding around it to get it to do what you want. Agent frameworks, there's vector stores I think was really popular back then, and just a whole bunch of different tools here. And as you've seen the field play out, the models have just changed so much and gotten so much better that they ended up literally eating some of the scaffolding. And I think this is even true today. So I think the article from Nicholas actually, the current scaffolding which is fashionable is skills, files-based context management. I could see a world where at some point that's no longer useful. Where the model can actually manage by itself, or it's hard to predict, but it might move on to some new paradigm where you no longer need this file-based skills type thing. You have literally seen this play out. The agent framers I think are a little less useful now. There was a period of time in 2023 where we thought vector stores is going to be the main way for you to bring organizational context into the models. And you need to vectorize and embed every bit of your corpuses, and then you need to do all this work to figure out the vector search, to optimize that, to pull out the right information at the right time. All of that is scaffolding because the model was not good enough. And it turns out, in this case, it turns out as the models get better, a better approach is actually to take out a lot of that logic and trust the model and give it a set of tools for search. It doesn't need to be a vector store. You could actually just hook it up to any type of search. It could literally be files on a file system like skills and agents MD to steer it as well. Obviously, there's still a place for vector stores. I know a lot of companies are still using it. But the entire scaffolding around that and building an entire ecosystem around that and assuming that's the only scaffolding that you need has really changed. Tying this back to the you don't always have to listen to your customers, because the field is changing so much at any point in time, a lot of people are in this local maximum. And if you just blindly listen to your customers, they'll be like, yeah, I want a better vector store. I want a better agent framework for this. And if you had just only chased down that path, it actually would have led you to build something that, again, is local maxima. Whereas as the models get better, we've had to reinvent and rethink the right abstractions and the right tools and frameworks to build around these models. And the cool slash exciting slash kind of crazy, annoying part is it's a moving target. And so, yeah, the current smattering of tools and frameworks right now will likely need to evolve and change pretty significantly over time as the models get smarter and better. But that is just the nature of building in this space. I think that's what makes it exciting. But it also means when you talk to customers, you kind of need to balance the exact feedback that they want with where you think the models are going and where you think things will trend over the next one or two years. It's interesting how this is, the bitter lesson is this big lesson that AI and ML folks learned, which is just like, the less you overcomplicate, the less logic you add to machine learning to AI, the more it'll be able to scale and grow and just take it all away and let it just compute, basically. Just give it more power to get smarter on its own. Yeah, there's literally a version of the bitter lesson applied to building with AI where we were trying to architect all this stuff around, and it turns out the models have just kind of eat it all away. And honestly, OpenAI API team has been guilty of this, where we took some left and right turns when we shouldn't have. But yeah, the models still end up, models get better, and we're all learning the bitter lesson day in and day out. So what would be the key takeaway for folks building on, say, the API or just building agents and having to build a little bit of this around for now? What would be the advice? My general advice, and I've been giving this to people for a while and I think it's still true today, is make sure you're building for where the models are going and not where they are today. It's clearly a moving target, and I think a lot of the companies that I've seen, startups that I've seen really do well, is they build a product for an ideal type of capability that is maybe 80% of the way there today. And they end up having a product that kind of works, but it's just almost there. But then as the models get better, suddenly it might click, and then their product now is incredible because it works maybe with 0.3 at some point, it suddenly works with 5.1. 5.2, suddenly it unlocks it. But they're building these products with the model capability improvements in mind. And with that, you end up creating an experience that's way better than if you had assumed that it's static in the first place. And so that would be my general advice, which is build for where the models are going and not where they are today. So you end up building a better product. You may need to wait a little bit, but the models are getting so much better so quickly you often don't need to wait that long. So to follow that thread, where are, like in the next 6 to 12 months, where is the API heading? Where is the platform heading? Where are the models heading? As much as you can share, I know there's a lot of secrets here, that maybe you're most excited about, or do you think that people should start to prepare for however much you can share? So the obvious one is how long of a task these models can do coherently. So there's like the meter benchmark that I think tracks software engineering tasks and how long of a task can these models do 50% of the time, 80% of the time. I think we're at something like multi-hour tasks being able to be done by, software engineering tasks being able to be done by these frontier models 50% of the time, and then I think 80% is something like just under an hour. But the sobering thing about that chart is they plot all the previous models on this chart as well. So you can really see the trend of this. That's something that I'm really excited about, which is, I actually think products today really optimize for tasks that the model can do for minutes at a time. Like even codecs and the coding tools, I'd say it's in the cli, you're kind of seeing it be interactive. It's really quite optimized well for maybe at most 10-minute type tasks. I have seen people push codecs to the limit into multi-hour long tasks. But again, I think that's more of the exception. But if you follow this trend, I think in the next 12-18 months we could see models that could do multi-hour long tasks very, very coherently. At some point it might reach six hours a day long task, where you dispatch it and have it do things on its own for a while. The types of products you build around that will look very different. You want to give the model feedback. You obviously don't want it to completely run wild for a day. Maybe you do. But you probably don't. And then the universe of things you can have the model do really expand. So that's something that I'm really excited about seeing. Another thing over the next 12-18 months that I think will be really cool is improvements in the multimodal models. Actually, by multimodality, I'm mostly thinking about audio. The models are pretty good in audio. I think they're going to get a lot better at audio over the next 6-12 months, especially the native multimodal models, the speech-to-speech ones. I think there's also interesting work being done around new types of models and architectures on the multimodal audio side as well. But audio, especially in the enterprise and business setting, I think is a hugely underrated domain still. Everyone talks about coding. It's all text. We're talking in audio. A lot of the world's business is done via audio. A lot of services and operations are done via talking in audio. And so I think that area is going to look very exciting in the next 12-18 months. And I think there will be even more unlock for what we can do with audio models there as well. Amazing. So, quick summary. Expect agents and AI tools to run longer and that trajectory to continue to increase and then audio and speech becoming a bigger deal, more first-party and native and better and core to the experience. Extremely cool. Okay, I want to go back to one of your hot takes, another hot take that I've seen you discuss. You're very bullish on business process automation as an opportunity in the world of AI. Talk about that. Yeah, this goes back to the thing that I said previously, which is we live in a bubble in Silicon Valley and a lot of the work that we do, that we're used to, software engineering, product management, building products, is very differently shaped than the work that goes on that runs our entire economy. And I see this in and out when I talk to customers. If you talk to any company that's not based in, it's not a tech company, there's a lot of business processes. And so what I mean by this is I generally delineate it as software engineering is open-ended knowledge work. And this is why I think tools like Codex tend to be quite good because it's exploring and you're giving it these open-ended things. But software engineering is fundamentally pretty open-ended and it's not very repeatable. So you build a feature, you're not trying to build the exact same feature over and over again. And a lot of tech jobs are in this space. I think data science is in this space as well. Even some of the strategic finance stuff. But as you move further and further away from software engineering and what is core in tech, a lot of jobs are just business processes. They're repeatable things, repeatable operations that some manager at a company has iterated on. There's usually a standard operating procedure that people want to do and you don't want to deviate from it that much. In software engineering, the ingenuity is deviating. But a lot of the work being done in the world is actually just running through these procedures and operations. If I call a support line, they're running through one of these. If I call my utility company, there's a bunch of processes and things that they can and cannot do for me. I'm just extremely bullish on this general category. I think it's underrated because it's so different from what we think about in Silicon Valley. People tend to not think about it. But how do we apply AI and some of the tools and frameworks that we have towards this business process automation? Towards automating and making easier repeatable business processes with high determinism that is fully integrated with business data and business decisions and different systems within an enterprise? How can we actually make that process better? Because I actually think there's a lot of opportunity for more work to be done in that area. We just don't talk about it because it's a little bit less in our wheelhouse. Your take here, just to make sure I fully understand it, is you think there's a much bigger opportunity outside of engineering for AI to impact productivity of companies and also jobs of these folks that are doing these repetitive, easily automated tasks? Impact jobs and also just impact how work is done. So much of work is done in this way. You think about basically I talk to customers all the time, big enterprises like, how will AI transfer my company? How will it run in a world with AI in 20 years? Software engineering is part of the story but there's so much more on the business process side and I actually think it might look even more different on the business process side and the work there is pretty substantial. It's actually interesting. I don't know from an absolute percentage or absolute basis, I don't know if it's bigger or smaller than software engineering. Software engineering is pretty huge and pretty expensive as well. But it is pretty massive and it's definitely bigger than it's bigger than you would think it is based off of how people talk about it or don't talk about it on X or Twitter. Going in a slightly different direction, having built the platform, building the API, people building on the API, the biggest question on people's minds is always just how do I not have open AI squash my idea and build their own thing and then destroy this market I created? What's the general policy, what's the general philosophy of how startups should think about where open AI is unlikely to go? My general answer here is the market is so big and so massive. I actually think startups should just not overly think about where open AI or these labs are going. I've talked to a lot of startups that have not worked out, startups that are doing really well. Every startup that I've seen that has fizzled out is not because open AI or a big lab or Google or something has come to squash them. It's because they built something and it really didn't resonate with the customers, whereas the ones that take off, even in very competitive spaces like coding, Cursor's huge at this point. It's because they built something that people really love. My general advice is don't overly stress about this. Just build something that people like and you will have a space in this. I can't overstate how big of an opportunity there is right now. The opportunity space of building with AI is so big. A good example of this is the space is so big that the Overton window of what is acceptable and not acceptable for VCs to do has completely changed here. VCs are investing in competitive companies left and right. The space is so big because the opportunity is unlike anything that we've seen before. While that affects how VCs operate, from a startup perspective, it's the most empowering thing in the world because even if you just build something that some people really, really love, you will end up with a massively valuable business. That's why I told you don't overthink about it. The other thing I also think is important to remember, at least from an open AI perspective, one thing that we've always held very near and dear, which both Sam and Greg helped reinforce from the top as well, is we actually view ourselves fundamentally as an ecosystem platform company. The API was our first product. We think it's really important for us to foster this ecosystem and continue to support it and not squash it. If you look at the decisions we make, this is all we've through it. Every single model we've released in one of our products gets released in the API. We release these Codex models now that are a little bit more optimized for the Codex harness, but they always find their way into the API and all of our customers end up using those. We don't hold back on any of that. We think it's really important to keep our platform neutral so we don't block competitors. We allow people to have access to our models. We've recently been testing more of the sign-in with ChatGPT product as well. We want to foster this ecosystem. I think it's really important that we do so. The general thinking about this is a rising tide lifts all boats. We might be an aircraft carrier pretty big at this point, but we think it's important to raise the tide because everyone benefits. I think we'll benefit as well. Our API itself has grown pretty significantly because we act in this way. I'd really encourage people not to view OpenAI as this kind of thing that'll just shove people out of the way, but instead focus on building something valuable. We remain committed to providing an open ecosystem. Why is that important to OpenAI, just this focus on building a platform, creating a way for people to build businesses? Is that just that's been the vision from the beginning? We want this to be a platform? It's been the vision from the beginning. It goes back to our charter, actually, our mission. The OpenAI's mission has always been one, to build AGI. We're obviously doing that. The second thing is to spread the benefits of it to all of humanity. The main part is all of humanity. Obviously, ChadGPT is trying to do this. We're trying to reach however many, the whole world. But very early on, and this is why we launched the API back in 2020 or something, really early. We don't think we as a company will be able to reach all of humanity. Every corner of the world is pretty deep. We actually feel like in order for us to fulfill our mission, we need to have some platform style thing here where we can empower other people to build the customer support bot for podcasters and newsletter hosts because we're not going to be able to do it ourselves. We've largely seen this play out with the API. This is why we talk to so many of our customers and really love seeing the diversity of things built on. We view it as an expression of our mission. You haven't even mentioned the app store that you guys are launching, the ChadGPT app store. Is that under your umbrella, by the way, or is that a different org and team? It's a different team. It's under ChadGPT. We obviously collaborate very closely with them. They built an apps SDK, which is a built-in close collaboration with our team. But that is more within the ChadGPT umbrella. That's another example of this. ChadGPT is... We have these 800 million weekly active users who are just coming over and over again. It's an asset to have as a business, but man, would it be better if we could somehow allow other companies to come in and take advantage of this as well and build for this audience as well. Ultimately, we think it'll help us expand that group as well. It all comes back to the mission. We find that being a platform, being open, tends to help here. Just that number, 800 million, I think it's MAUs. No, it's weekly. Weekly active users. 800 million people using weekly. It's absurd how many these numbers we're just used to now, but that's insane. Unprecedented. It's mind-boggling for me to think about from a scale perspective. Honestly. The way I think about it is 10% of the world and growing, by the way. It's shooting up. Come to ChadGPT and use it every week. At this point, I just want to double down on this point you're making. A.I.'s mission was to make A.I. available to all of humanity. I think some people diss that. They're like, oh, it costs money. The fact that there's a free version of ChadGPT that anybody can use that is not so different from the most powerful A.I. model that exists in the world for free, that's not gated, that anyone can use. If you're a billionaire, there's only so much more you can get out of A.I. than what someone in a village in Africa can get. I know that's always been really important to open A.I. Yeah. That's why I think we've leaned into the health work. Education is going to be very interesting here. The other insane kind of trend here is the free model has gotten so smart over time. The free model back in 2022 was good at the time, but it's like nothing compared to what you get today because you get GPT-5 today. Raising the floor across the world is something that we're really trying to do, and we view it as part of our mission. The other flip side of this, by the way, is talking about the billionaires or whatever. I know people love saying you're using the same iPhone that Mark Zuckerberg's probably using or the billionaires are using. For $20 a month, you're basically using the same A.I. that the billionaires are using. For $200 a month, you get the same pro model that all the billionaires are using, but they're probably not using pro for everything. They're probably just using the plus tier ones for their day in and day out. This kind of democratization and spreading of this benefit across all of the world is something that's really meaningful to us and something that drives a lot of what we do. One last question, just for folks that are thinking about building on the A.P.I. or just like, oh, wait, I could do cool stuff with open A.I. Wait, I could do cool stuff with OpenAI's models and APIs. What does your API and platform allow people to do? Like, I know you can build agents on top of the platform. Just talk about what you allow. So fundamentally, the API offers a bunch of developer endpoints. And these developer endpoints basically let you sample from our models. The most popular one that we have right now is one called Responses API. And so this is an endpoint, and it's optimized for building long-running agents. So agents that will work for a while. So what you can basically do is at a very low level, you're basically just giving the model text. The model will work for a while. You can kind of pull it to see what it'll do. And then you'll get the model response back at some point. That's like the lowest level primitive that we have for people. And that's actually what a lot of people use. That's the most popular way of building on top of your API. With that, it is like super unopinionated. And you can do basically whatever you want. That's like the lowest level thing. We've also started building more and more layers of abstraction on top to help people build some of these. And so next layer up, we have this thing called the Agents SDK, which has also gotten extremely, extremely popular. This allows you to use the Responses API or some other API endpoints that we have to build what you might more traditionally think of as an agent, like an AI kind of working in an infinite loop. It might have sub-agents that it delegates to. It starts building all this framework, all this scaffolding, actually. We'll see where this all goes. But it makes it a lot easier for you to build these kind of agents, giving it guardrails, allowing it to farm out sub-tasks to other agents and orchestrate a swarm of agents. The Agents SDK allows you to do that. And then above that, we've now started building tools to help also with the meta level of deploying an agent. So we have this product called Agent Kit and Widgets, which are basically a bunch of UI components that you can use to very easily build a very beautiful UI on top of either our API or Agents SDK. Because a lot of times, these agents look very similar from a UI perspective. And so there's Agent Kit. We also have a smattering of evals products, like evals API, where if you want to test and see if your agent or your workflow is working, you can test it in a very quantitative way using our evals product. And so, yeah, I view it as these various layers. They're all helping you build what you want with our AI, with our models, and with increasing levels of abstraction and how opinionated it is. And so you can use the whole stack, and it very quickly allows you to build an agent. Or you can go down the stack as low as you want to, basically, the responses API and build whatever you want because of how low-level it is. Sherwin, is there anything else that you want to share? Anything else you want to leave listeners with? Anything we haven't touched on that you think might be helpful before we get to our very exciting lightning round? The only thing I'd leave folks with is, yeah, I think the next two to three years are going to be some of the most fun in tech and in the startup world that we'll have in a very long time. And I would just encourage people to not take it for granted. I entered the workforce in 2014. It was great for a couple of years. I felt like there was a period of five to six years where it wasn't very exciting in tech. And then in the last three years, it's just been the most insanely exciting, energizing period of my career. And I think the next two to three years are going to be a continuation of that. And so I would encourage people to not take it for granted. I'm trying to not take it for granted. At some point, this wave is going to play out and it's going to be a lot more incremental. But in the meantime, we're going to get to explore a lot of really cool things, invent a lot of new things, and change the world and change how we work. And so that's the main thing I'd leave folks with. I love this message. I want to spend a little more time on it. When you say don't miss it, what do you recommend people do? Is it just build, lean in, learn, join a company, building really interesting things? What's your advice to folks that are like, OK, I don't want to miss the boat? Yeah, I would just say engage with it. So it's basically like what you said. Lean in, building tools on top of this is part of the story. Just using the tools. You don't need to be a software engineer to lean into this. I think a lot of jobs are going to change here. So just using the tools, understanding the limitations of what it can and cannot do so that you can kind of watch the trend of what it can start to do as the models improve. And so it's basically like getting used to the technology and getting familiar with it instead of kind of like laying back and letting it, letting it pass you. On the flip side of that, there's a lot of, I think, stress and just anxiety around like, there's so much happening. How do I keep up? I got to learn a lot about this week. Oh, God. Is there something you learned about it? Just not like you're at the center of this. How do you not get overly stressed and worried about missing things that are going on? And just can you stay on top of news? What are some things you've done and learned? Yeah, so I think I'm personally a bad example of this because I'm basically chronically online on X and our company Slack. So I actually try and absorb, I end up absorbing a lot of it. What I will say, though, is just like from observing other folks who are less, you know, addicted to this stuff, like I am. Yeah, a lot of it is noise. Like you don't need to, you don't need to have like 110% of this kind of pass your mind, like go into your mind. Honestly, just leaning into like one or two different tools, starting small is already like, you know, more than you need. Here, I think just the combination of like the frenetic pace of the industry, X as a product, just creates like this insane kind of like, yeah, this insane like pace of news, which is honestly very overwhelming. The main thing is like you don't need to be, you don't need to know all of that to really engage with what's happening right now. And even something as simple as just like install the Codex client, play around with it. Install Chadjibhi and connect it to a couple of your, you know, internal data sources, Notion, Slack, GitHub, and see what it can and cannot do. All of that, I think, is a part of it. Amazing. Sherwin, with that, we've reached our very exciting lightning round. I've got five questions for you. Are you ready? Yeah, yeah, absolutely. First question. What are two or three books that you find yourself recommending most to other people? I'll talk about one nonfiction, one and one fiction book. The fiction book was, I just finished reading it. I really recommend it. It's There Is No Anti-Memetics Division by QNTM. I think it's like an online author, but I saw it being shared on X. It's like a science fiction-y kind of book. And I basically devoured it in like two days. It's super, super well-written, super fascinating. It's about a government agency that's fighting things that make you forget it. And so it's just a very smart, creative book and fresh, honestly, in terms of source material that I really like. So I'd recommend that one. The book is also unintentionally hilarious. It's meant to be this sci-fi, almost horror-style book, but it made me laugh a couple times. So that's the fiction book. Nonfiction, so I'm going to cheat and I'm going to recommend two of them. So in the last year, I've been reading a lot more about China and the US-China relations. And I think there are two books that came out in the last year that have been really, really eye-opening for me in that regard. The first one is the Dan Wang book, Breakneck. That one was really, really good. I really liked his analogy of like the lawyerly, the US is the lawyerly society, China is the engineering society. And there are pros and cons to each. I read it and I was like, hmm, yeah, it does seem like we're run by lawyers in the US. So that's one. And the other one is the Patrick McGee book on Apple and China. It was super, super interesting. I'm a huge Apple fanboy. If you could see my desk right now, it's all Apple stuff. But just like, one, it was just super fascinating learning about Apple's relationship to China. And then two, it just had a lot of inside information about Apple as a company that I found fascinating. So it was also quite a page-turner and also a very, very timely book as well. The Antimemetics book sounds amazing. I'm buying it right now as you're talking. Yeah, it's like, I think it's only like a couple hundred pages. I literally finished it in two days. It was just like, so, so good. Okay, great tip. Okay, favorite recent movie or TV show you have really enjoyed? Yeah, that one's tough because, you know, I have two kids and a busy job. And so I really haven't had much time to watch TV shows. I will say in the last couple of weeks, I watched a couple episodes. I'm actually a big anime guy. And so I watched a couple episodes. There's a new season of this anime called Jujutsu Kaisen that's out. So season three of JJK was really good. In general, I'm a huge fan of Japanese anime. I think they create the most novel and unique plots and universes that Western media has shied away from. And so generally a big fan of that. But yeah, I haven't really watched much, but saw a couple episodes of JJK recently. Extremely understandable in your role. Yeah. Favorite product you recently discovered that you really love? Yeah. Okay. So I recently had to set up Wi-Fi and home networking. And I went all in on Ubiquiti routers and security cameras. I'd never heard of it before I had to do this. I always just had a very simple setup. And it's just such a well-built product. I don't know if you've used it before, but it's basically like the Apple of home networking. So beautiful products. But the thing that actually makes it extremely good is its software is good. So they have a really great mobile app to help manage all of the home networking. And so basically Ubiquiti, you can use it to buy wireless routers. You need Ethernet wiring throughout your house to use it. But I actually think what makes it really good are security cameras. So if you have security cameras that are plugged into Ubiquiti ecosystem, they have an incredible mobile app and Apple TV app and iPad app to kind of see the live feed of your cameras. And so they're a little pricey, but not that pricey. But it's been just an incredible product experience. All right. I went Eero, so I made a mistake. Eero's are pretty good too, but I fully converted to Ubiquiti at this point. Okay. Good tip. Okay. Two more questions. Do you have a favorite life motto that you find yourself coming back to in work or in life? Yeah. The one that I always repeat to myself is, never feel sorry for yourself. There's a lot of things that are going to happen, you know, at work, in life, and reminding yourself to never feel sorry and that you always have a sense of agency to kind of pull yourselves up is something that I've had to tell myself a lot. And also something that I repeat to a lot of other folks as well. Last question. So in your previous life, you worked at Opendoor, where you led work on basically figuring out how much to pay for houses. You basically built the model that told the company, here's how much we'll pay for this house. How much is valuable in the price of a house that you didn't expect is really important and impacts the price of a house? There's a bunch that were surprising. I'll maybe list the couple of most interesting ones. Power lines and like high voltage power lines like are super, super, actually impact your price quite a lot. I didn't really fully internalize this until I went to like Dallas and observed like when your house sits next to one of these giant like, you know, voltage lines is like buzzing. And most people have families, you don't want your kids kind of near there. So I think that was one that really, really kind of surprised me. That makes sense. Yeah. And then the other one, which was something that was always something really difficult for us to quantify was floor plans. And so it is very important. Like, yes, of course, it's really important. But just like quantifying what a good floor plan is like and what a really bad floor plan is like. We were doing all these things like how wide is the kitchen? Like, is it a what style of kitchen is it? And then like, where's the master bedroom? And so it was just really, really hard to quantify. But I remember floor plan was a big one because like we'd have a home that like wouldn't sell. And then our ops team would go in and be like, yes, the floor plan issue. So like, how do you how could you tell? It's like you go inside, you just feel it. It feels you know, the floor plan feels feels off. So yeah, those are ones that were surprising. And then the last one that was more impactful than I thought is general like curb appeal and like even like the front door. And so I actually think there's a Zillow book on this where the front door replacement tends to be the highest ROI for homes. But just like the feel of like, as you walk up to the home as a buyer, what you're interacting with and the first moments of the house, I think was I'd underrated its importance. That is extremely interesting. And I love that you had to figure out how to do all this in code and not walk around and look at these houses. Yeah, and floor plans. I have a bunch of stories around like for floor plans, there's like, there's like, it's not digitized. So there's like a handful of people who have like paper floor plans of like all these homes in like Phoenix and Dallas. Yeah, a lot of a lot of fun, fun stories from the open door days. Okay, Sherwin, thank you so much for doing this. This was incredible. Where can folks find you online? And and how can listeners be useful to you? Yeah, so I'm online on Twitter on X, I'm just at Sherwin Woo. And yeah, I mostly just tweet about OpenAI and API and some of the products that we're launching. And then how folks can be useful to me. I love hearing about things that people are building. And so if you're working on a startup, if you're hacking on an idea, you know, would love to reach out to me on X, I would love to hear about what you're building and, and learn about how OpenAI can help support you. Amazing. Sherwin, thank you so much for being here. Yeah. Thank you, Lenny. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's podcast.com. See you in the next episode.