← Lenny's Podcast: Product | Career | Growth
Lenny's Podcast: Product | Career | Growth
“Engineers are becoming sorcerers” | The future of software development with OpenAI’s Sherwin Wu
01:19:39
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- [00:00]
Sherwin Wu
95% of engineers use Codex. 100% of our PRs are reviewed by Codex for engineers.
- [00:06]
Lenny Rachitsky
I don't know what job has changed more in the past couple years.
- [00:09]
Sherwin Wu
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.
- [00:17]
Lenny Rachitsky
What do you think people aren't pricing in yet?
- [00:20]
Sherwin Wu
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.
- [00:31]
Lenny Rachitsky
I've been hear there's this stress people feel when their agents aren't working.
- [00:35]
Sherwin Wu
There's a team that's actually doing an experiment right now within OpenAI 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.
- [00:47]
Lenny Rachitsky
You've shared that listening to customers is not always the right strategy in AI.
- [00:51]
Sherwin Wu
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.
- [01:00]
Lenny Rachitsky
What's your advice to folks that are like, okay, I don't want to miss the boat.
- [01:02]
Sherwin Wu
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. He likes saying this is the worst the models will ever be.
- [01:12]
Lenny Rachitsky
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, adyen, 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 Lenny that's getdx.com 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 5 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. Seer, 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 Seer for free at Sentry IO Lenny and use code Lenny for $100 in Sentry credits. That's S E N T R Y IO Lenny. Sherwin thank you so much for being here and welcome to the podcast.
- [03:20]
Sherwin Wu
Thank you, thank you, thank you for having me.
- [03:21]
Lenny Rachitsky
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?
- [03:34]
Sherwin Wu
I do write code occasionally now. Still, I 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 codecs on a daily basis. So 95% of engineers use codecs. 100% of our PRs are reviewed by Codex daily as well. So basically any code that goes into production that's merged in Codex 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 codecs more open, way more PRs, so they're actually opening 70% more PRs and than the engineers who aren't using codecs as much. And the gap is widening. So I feel like, you know, the people who are opening more PRs are starting to, you know, learn how to use the tool more and more, get more efficient, and that 70% gap keeps growing over time, and so might have actually increased since I last looked at the. At the number.
- [05:07]
Lenny Rachitsky
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.
- [05:19]
Sherwin Wu
Yep, yep.
- [05:20]
Lenny Rachitsky
It's. It's like, crazy that that's almost like, not crazy anymore that we're just, like, getting used to this.
- [05:26]
Sherwin Wu
I think there's still some getting used to, to be clear. There's also, I think, some, you know, engineers who I think, trust codecs 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 how much they trust the model to do on its own goes up over and over time. And there's a quote from Kevin Whale, our VP of science here. 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 see the models get better and better as well. Yeah.
- [06:04]
Lenny Rachitsky
Kevin Wheel, former podcast guest, he said exactly that line on this podcast and.
- [06:08]
Sherwin Wu
He had a few times.
- [06:10]
Lenny Rachitsky
Yeah. Peter, the claudebot slash Moltbot 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.
- [06:28]
Sherwin Wu
Yeah, yeah, he's a great user of Codex. I know he's in close touch with the team, gives us great feedback. Not surprised that he uses it. I mean, sorry, it's called Open Claw.
- [06:38]
Lenny Rachitsky
Open Claw, Yeah.
- [06:39]
Sherwin Wu
Open Claws is a great product. And then I saw that this morning. I mean, this is very recent, but this morning, I think Mult Book, kind of like with shared as well, and seeing all of the AI agents talk to each other is pretty, pretty Surreal.
- [06:51]
Lenny Rachitsky
It's basically her is happening in real life is what. 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 years, like job that we didn't expect to change this much. We're just like, the job of an engineer is so different in the entire lifespan of an engineer. Like, in the past couple 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?
- [07:28]
Sherwin Wu
Yeah, it's. 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 going 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, you know, 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, I see this moving. So I think there's a common thing that everyone's saying, which is, you know, 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 SICP 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 in 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 sorcery. It says software engineers are wizards and programming languages are incantations. And you're issuing these spells and these spells are kind of going out and doing things for you. And the challenge is what incantation do you have to say to make the, the program do what you want? And this book was written in 1980, so this is a while ago. And I think that metaphor has actually 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 codex, 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 the source analogy because I think our current state 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 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, like sets the, the brooms off on a task and then goes to sleep. And so, you know, it's like vibe coding at its, at its, at its greatest. And then eventually the old sorcerer comes back and cleans everything up. And when I see engineers kind of doing these 20 different codex threads at a time, there is some skill and there's some seniority and 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 completely go away and ignore the thing. But it's also Extremely high leverage. Like, you know, a very senior engineer who's, who's really proficient with these tools can now just do way more things via what they're doing. And I think it's also what makes it fun. Like, it literally feels like we're wizards now. You know, it feels like we're closer to having 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.
- [11:51]
Lenny Rachitsky
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 you can, 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.
- [12:06]
Sherwin Wu
How big? Yeah, or it might be like the monkey's paw type thing where, you know, it's like you got 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 sigv. 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.
- [12:28]
Lenny Rachitsky
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, do you feel that, do you feel that across your team at all?
- [12:44]
Sherwin Wu
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 these, with, with, with Codex or with these AI agents to, 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, that's actually doing an experiment right now within OpenAI where they are basically maintaining a 100% Codex written code base. So, you know, like, you know, some, you know, 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, 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 for the experiment, this team doesn't have that escape hatch. Then the challenge how do I get the agent to do this? I actually think we're going to be publishing a blog post from some of our learnings here, but a lot of fascinating 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 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 under specified 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, can better do its task. There's a whole bunch of other learnings from this, this group, which I think is fascinating to, to explore. But yeah, kind of giving. 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.
- [15:09]
Lenny Rachitsky
Another issue people run 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 in 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?
- [15:27]
Sherwin Wu
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 at a 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 and 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. Uh, 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 2 to 3 minute task because you have a, a bunch of suggestions already already baked in. A lot of the times people will, especially for small PRs like you, 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 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 heavily lean into the general CI process and like the post kind of push and like deployment process has also been heavily automated via Codex internally. At this point, if you talk to a lot of engineers, the thing that annoys the most is after you've written your beautiful code, like, how do you get it into production? You know, you got to, you know, run through all these tests. You gotta like, you know, lint errors. Yeah, all the code review. There's a lot of automated stuff you can do with codecs. And so we've actually built some tools internally that, that help automate that process. Automate the lint 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 as into as. As little work for an engineer as possible, which. And. And the byproduct of which is they can. They can now merge and push out a lot more peers.
- [17:54]
Lenny Rachitsky
Codex writing the code, Codex reviewing its own code. I'm curious if you are open to using other models to review your model's work. Is that. Is that a path or is it just. It's good enough. We don't need anything else.
- [18:03]
Sherwin Wu
So I will say there's. There's definitely a circular thing here and like going back to sourcer's apprentice, like, you want to make sure you're not letting the brooms go crazy here. And so, you know, we were 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 100% attention to 30% attention, which just helps things push through. In terms of multiple models, we obviously test a lot of models internally, and so we have a lot of those. We use external models less. We think it's important to dog food our own models and kind of get feedback there. But you can also. There are a lot of internal variants of models that you can use to give you a different perspective here as well. And we found that to work quite well.
- [18:51]
Lenny Rachitsky
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 want to move on to a different topic. 100% of code across OpenAI is written by Codex at this point. Is that the way to frame it?
- [19:08]
Sherwin Wu
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's. But almost every engineer heavily uses codecs in all of their tasks at this point. And so if I were to guesstimate the vast majority of code at this point, it was probably authored by AI.
- [19:28]
Lenny Rachitsky
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 think? Where do you think managers, what's the role of a manager in the future?
- [19:49]
Sherwin Wu
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 kind of more managery tasks that I do. I'd say a couple 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 to. One thing that's becoming increasingly clear is Codex really empowers top performers to get a lot, to be a lot more productive. And so it really. And I think this is maybe true for AI more broadly across society, which is the people who really lean in are the people who have high agency or will really get good at these tools will 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, 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 unblocked, 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 going to just like really be shooting ahead using these tools. I think, I think one example is the team that's maintaining a 100% Codex generated code base, 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 going to 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 ChatGPT with organizational knowledge, like being able to do research and understanding organizational context a lot better. Another good example is we're doing performance reviews right now and it's actually really Easy to use ChatGPT with internal knowledge hooked up to GitHub and micro notion Docs and Google Docs to get a really good sense of what this person has done over the last 12 months in 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 people managers to be higher leverage and will allow them to manage teams of way more 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 the non engineering domains like support or operations, where it's like, previously the size of a support team might be limited, but 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 there are ems managing 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.
- [23:10]
Lenny Rachitsky
I love this advice that the way you described it is you've always leaned into top performers and spent more time with them, unblocked them, make sure they're happy. The way Mark Andreessen, he was just on the podcast, the way he phrased it is AI makes good people better. Better and it makes great people exceptional.
- [23:24]
Sherwin Wu
Yeah, yeah.
- [23:25]
Lenny Rachitsky
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.
- [23:33]
Sherwin Wu
Yeah. A very good example right now is there are, I would say like a group of engineers internally who are really codex pills 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 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, you know, elevate everyone. And, and I view that as like, you know, another example of this trend that, that we're seeing where the top performers really get exceptional people just like.
- [24:15]
Lenny Rachitsky
Have a sense this is big. AI is changing so much. The world is changing. 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.
- [24:30]
Sherwin Wu
So one of my favorite kind of like phrases or like things that have come out of this whole AI wave is the idea of the one person billion dollar startup. I think, I actually think Sam may have keyed it or like Sam may have been the first one to say it, but it's fascinating to think about, right? It's like yeah, if, if, you know, if people are so high leverage at some point there will likely be a one person billion dollar startup. 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 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 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 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 where anyone can build software for anything, right? Like one, 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, like creating some AI tool for some vertical tends to work quite well because you know, you really lean into 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 SaaS 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, as it's easier and easier to, you know, run a company, you might actually just end up seeing way more of these, these, these startups. And so the way I, I, I've been thinking about is like, yeah, there might be one, one person billion dollar startup, but there might be like a hundred, you know, $100 million startups. There might be tens of thousands of $10 million startups. And as an individual it's actually pretty great to have a $10 million business like that's like enough for, you're set for life at that point. And so you know, we might really see, see an explosion in that way. And, and I feel like people aren't, aren't really, you know, pressing that in. There's another kind of like third order effect of this. And again all of these, as you get to the further and further out predictions, I think there's a lot of uncertainty. I think if we end up moving to this world where you end up with these kind of micro companies building software that works for one or two people who own the company and are working there, I think the startup ecosystem will change. I think the VC ecosystem will change. We might end up in a world where there's just 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 100 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 companies which are not great for venture salary returns but are great for the individuals, the high agency individuals who are now, you know, really lean into AI to build these businesses for themselves.
- [28:13]
Lenny Rachitsky
I love how many order like order effects we've been through. When I'm here, the fourth order effect now Sherwin. I'm just joking.
- [28:22]
Sherwin Wu
Fourth order is too gigabrain for me. I can't think that far ahead.
- [28:27]
Lenny Rachitsky
It's like Inception where just everything gets slower every time you go deeper into something, every layer. Okay, so the billion dollar startup I've been, I think about this a lot because 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 seeing 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 simply because of the support costs. Even if AI is helping you at a billion dollars, just like unless your ACVs are 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, 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.
- [29:31]
Sherwin Wu
So I think that's true and actually I think my view on it 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, 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.
- [30:29]
Lenny Rachitsky
I would buy that.
- [30:30]
Sherwin Wu
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, 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.
- [30:57]
Lenny Rachitsky
I could see that. I also think about Peter at Claudebot Slash Mobbot Slash Open claw of just like how barraged he is right now by all these asks and emails and pings and dms and PR is just like, I'm curious to. And he's not even making any money off this thing.
- [31:11]
Sherwin Wu
Yeah, I can't imagine what it's like.
- [31:13]
Lenny Rachitsky
To be him right now.
- [31:13]
Sherwin Wu
It must be like absolutely insane. It's probably like, you know, like the. The months after we launched Chat gbt. The craziness that was as one.
- [31:22]
Lenny Rachitsky
As one man. He's coming out on the pod, by the way, in a week.
- [31:25]
Sherwin Wu
Oh, that's exciting. Yeah.
- [31:27]
Lenny Rachitsky
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 stuff. Okay. 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. 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 key to your success as a manager of engineers and just people?
- [32:13]
Sherwin Wu
Yeah, I think a lot of the lessons that I've learned here, I don't know how specific it is to the OpenAI API or some of our enterprise products in particular. I think my management philosophy has obviously changed over time, but I think it's probably stayed the same more than it's changed over time. One of these principles is what I talked to you about before, which is spending a lot of time with top performers. Actually spending. And to be very concrete, it's more than 50% of your time with your top performers, maybe your top, like 10% performers, and really, really trying your best to empower them. The way that I think about it is kind of come back to this analogy of software engineer as a surgeon, which comes from the Mythical Man Month book. So it's funny, so I pull it from the book. But in the book they actually describe this world where I think they were predicting the future, because I think the book was written in the 70s or something. They said that software engineering might end up moving into a world where the software engineers are like surgeons or like in a surgery room, there's like one person doing the work and you know, there's the one person like cutting or 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 and the resident and the fellow. And then the surgeon's like, I need a scalpel and they give them scalpel and then they're like, I need, you know, this tool and it's machine and they'll bring it over. Everyone's there to just like, you know, support the one surgeon. And so the mythical mammoth actually predicted that that is kind of the direction that software engineering 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 that analogy is actually what I strive to kind of like emulate in my own management philosophy, which is 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. And insofar 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 and looking around corners and giving them everything that they need when it's really just me as the, 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 if, if people are just like cranking PR after pr, the main thing, bottlenecking 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 management and especially engineering management. And so that's something that, that's really, really 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 my career.
- [35:18]
Lenny Rachitsky
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.
- [35:26]
Sherwin Wu
Yeah, that's actually a really good point. I haven't tried this yet, but I wonder what would happen if I ask Chad. GPT hooked up to company knowledge. You know, like what are the active blockers? Look through all the notion docs. What are 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? Now that's very interesting. I have not thought about that, but you're right.
- [35:48]
Lenny Rachitsky
I just had an insight right here. Yeah.
- [35:50]
Sherwin Wu
Yeah.
- [35:51]
Lenny Rachitsky
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.
- [35:57]
Sherwin Wu
Yeah, you asked the, you asked the model, you asked the AI to do the second and third order things. Anticipate that and anticipate what the bloggers will be next month too.
- [36:05]
Lenny Rachitsky
I think we've got a, we've got a good idea right here.
- [36:07]
Sherwin Wu
Yeah.
- [36:08]
Lenny Rachitsky
Yeah. This episode is brought to you by Datadog, now home to epo, the leading experimentation and feature flagging platform. Product managers at the world's best companies use datadog, 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, datadog 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 experimentation at Airbnb built epo. Datadog 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. Datadog is more than engineering metrics. It's where great product teams learn faster, fix smarter, and ship with confidence. Request a demo@datadoghq.com Lenny that's datadoghq.com Lenny okay, I'm going to shift to talking about the API and the platform that you all build. 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 ROI on their AI deployments, which I think is what a lot of people 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 roi.
- [38:00]
Sherwin Wu
Yeah. So to be clear, I don't like explicitly see quantitative numbers around this. You know, 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 negative roi. I mean, part of this too is I think there's also general sentiment from folks around the country, like basically outside of tech, that AI is being forced onto them. And I think part of this is probably a symptom of some negative ROI AI deployments. A couple things I've observed around this. So one thing is, and I think I come back to this again and again, I think we in Silicon Valley just forget that we live in a bubble. We are so like Twitter is a bubble, sorry, X is a bubble. Silicon Valley is a bubble. Software engineering is a bubble. Most people in the world, most people in the US are not software engineers, are not very AI pilled, are not following every single model release. And so we're just like highly out of the loop on how to use this technology. And so we always talk about all these best practices for codecs, all these codex pill people within OpenAI. Sure. Everyone on X who posts are crazy power users of these AI tools. They lean into skills, they lean into agents, md, mcps. Yes, yeah, all of that. And 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, that kind of ties into what I think more companies do or like what could do or what a more ideal AI deployment setup looks like. And this is kind of how we've run things within OpenAI too. 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 suites, like, you know, we want to become an AI first company. So there's buy in they buy the tools, they have, you know, exact 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 this technology and are willing to learn, evangelize, build best practices and kind of like knowledge share within the organization. We've seen this a lot internally. So like obviously OpenAI has always wanted to be a very AI centric company, but when it really started taking off was when was with the introduction of Codex and these tools where like people then 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 very different. It's very unique. Software engineering is different than finance, is different than operations, is different than go to market and sales. And so there's a lot of these 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 AI 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 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.
- [41:56]
Lenny Rachitsky
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?
- [42:04]
Sherwin Wu
Yeah, it's interesting. So also a lot of companies don't have software engineers. And so the pattern I've seen is it tends to be these software engineering adjacent basically technical people, but are not software engineers. I think those are the ones who tend to get most excited around this. It's like maybe the support team operations lead who doesn't code but loves using these tools and is 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 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 software engineers. Is actually kind of a rarity. They're hard to find, they're expensive. And so it's these other types of folks.
- [42:58]
Lenny Rachitsky
What I'm hearing is the anti pattern is top down. This is very. The CEO found an exec team. Just like we are going to go AI first, we're going to lead into AI. Everyone's going to 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 gospel. You find that doesn't work.
- [43:24]
Sherwin Wu
Yeah, yeah, exactly.
- [43:25]
Lenny Rachitsky
And the advice is find the people that are most excited and instead of kind of having them spread out through the organization, 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 org.
- [43:40]
Sherwin Wu
Yeah, I mean another, it's kind of like hearing you, you play back to me. Another way to think about it, kind of tying back to my own imaginative philosophy 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 of create the seeds of excitement internally.
- [43:57]
Lenny Rachitsky
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, not always the right strategy in AI and it might often lead you astray.
- [44:14]
Sherwin Wu
I don't know if it's that hot of a take. I think the main thing here is so 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 kind of 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 start, fintool and 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 are 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. Like agent frameworks. There's vector stores I think was really popular back then and just like a whole smattering of tools here. And as you've kind of 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, you know, that's no longer useful, where the model can actually, you know, manage all that themselves. Or like, you know, or, or, or there might be, you know, it's hard to predict but like might move on to some new paradigm where you no longer need this file based like skills, skills type thing. You have literally seen this play out, right? Like the agent frameworks I think are a little less useful now. There was a period of time like 2023 where we thought vector stores and is going to be like 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, the right time. All of that is scaffolding because the model was not good enough. And 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 kind of 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. And so tying this back to the, like, you know, it's, you know, you don't always have to listen to your customers because the field is changing so much at any point in time. You know, a lot of people are kind of in this local, local maximum. And if you just blindly listen to your customers, they, they'll be like, yeah, I want a better vector store. Like, I want a better, I want a better, you know, agent framework for this. And if you had just kind of only chased down that path, it actually would have led you to, you know, build something that, again, is the local maxima. Whereas as the models get better, we've had to reinvent and kind of rethink the right, right abstractions and the right tools and frame to build around these models. And the cool, exciting, 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 the 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 to two years.
- [48:11]
Lenny Rachitsky
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 the way and let it just compute. Basically just give it more power to get.
- [48:32]
Sherwin Wu
Yeah, there's literally a version of the bitter lesson applied to, like, building with AI where, you know, we were trying to architect all this stuff around and turns out the models will just kind of, you know, eat it all away. And, and, and, and honestly, like, OpenAI API team has, like, been guilty of this where we kind of like took some, you know, 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.
- [48:58]
Lenny Rachitsky
So what would be the key takeaway for folks building on, say, the API or just building agents and, you know, having to build a little bit of this around for now, is it just. Yeah. What would be the advice?
- [49:08]
Sherwin Wu
My general advice, and I've been giving this to people for a while and I think still true today, is make sure you're Building for where the models are going and not where they are today. You know, the, the, it's, it's clearly moving target. And I think a lot of the companies that I've seen, startups that I've seen really, really do well is they build a product for an ideal type of capability that is like 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 03 at some point it suddenly works, but 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. 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.
- [50:17]
Lenny Rachitsky
So to follow that thread, where are like in the next six to 12 months, where is the API heading, Where's 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 and however much you can share.
- [50:34]
Sherwin Wu
I mean, 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 track software engineering tasks. And how long, you know, like 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, you know, I actually think products today really optimize for tasks that the model can do for like minutes at a time. Like even codecs and like the coding tools. I'd say like, you know, it's in the cli, you're kind of like seeing it be interactive. It's really, you know, quite optimized. Well, for like maybe at most 10 minute type tasks. I have seen people push Codex to the limit and do like multi hour long tasks, but again I think that's more 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 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, really excited about seeing. Another thing over the next 12 to 18 months where I think be really cool is improvements in the multimodal models. And actually by multimodality, I'm mostly thinking about audio here, where the models are pretty good at audio. I think they're going to get a lot better at audio over the next six to 12 months, especially the likes, you know, the native multimodal model, 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 in a business setting, I think is a hugely underrated domain still. Everyone talks about coding, it's all text, but 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 and audio. And so I think that 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.
- [53:17]
Lenny Rachitsky
Amazing. So, quick summary. Expect agents and AI tools to run longer to that, 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. Yeah, extremely cool. Okay, I want to go back to one of your hot takes, another hot take that I've seen you discuss your big. You're very bullish on business process automation as an opportunity in the world of AI. Talk about that.
- [53:47]
Sherwin Wu
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 a tech company, there's a lot of business processes. And so what I mean by this is I generally delineate it as there's. Software engineering is kind of like open ended knowledge work, right? 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, right? 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 the space. I think like data science is kind of in the space as well, even some of the like strategic finance stuff. But as you move further and further away from software engineering and like what, what is current tech? A lot of jobs are just business processes. They're like repeatable things, repeatable operations that, you know, some manager at a company has kind of like 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. You know, there's like in software engineering the ingenuity isn't, isn't deviating. But a lot of, a lot of the work being done in the world is actually just running through these procedures and operations. Like if I, you know, 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. And so I'm just extremely bullish on this general category of like. And 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 can 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 and how can we actually make that process better? Because I actually think there's a lot of opportunity and a lot of work to be done in that area and we just don't talk about it because it's a little bit less in our wheelhouse.
- [56:20]
Lenny Rachitsky
So your take here, just to make sure I fully understand it, is you think there is a much bigger opportunity outside of Engineering for AI to impact productivity of companies and also jobs of these folks that are doing these kind of repetitive, easily automated tasks impact jobs.
- [56:36]
Sherwin Wu
And also just impact how work is done. Like, so much of work is done in this way. Like, you think about, you know, like what a. Basically I talk to customers all the time, big enterprises, like, like, how will AI transfer my company? Like, how will it run in a world with AI in like 20 years? And you know, 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, and the work there is, is pretty substantial. It's actually interesting. I don't know, like from an absolute percentage or absolute basis, I don't know if it's bigger or smaller than software engineering. Like, software engineering is pretty huge and pretty expensive as well, but it is pretty massive. And it's definitely bigger than, you know, it's, it's bigger than you would think it is based off of how, how people talk about it or don't talk about it on X or Twitter.
- [57:23]
Lenny Rachitsky
Okay. In going in a slightly different direction, having built the platform, building the API, people building on API, the biggest question on people's minds is always just, how do I not have OpenAI squash my idea and build their own thing and then, you know, destroy this market I created? What's the general policy? What's the general philosophy of how startups should think about where OpenAI is unlikely to go?
- [57:49]
Sherwin Wu
My general answer here is the market is so big and so massive. Like, I actually think, you know, startups should just not overly think about where OpenAI or these labs are going. I've talked to a lot of startups, you know, that have, you know, not worked out startups that are doing really well. Every startup that I've seen that is kind of fizzled out is not because OpenAI or, you know, Big Lab or Google or something has, has come to squash them. It's because they built something and it like really didn't resonate with the customers. Whereas the ones that take off even in very competitive spaces, like coding cursor is huge at this point, and it's because they build something that people really love. And so my general advice is like, 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 in building with AI is so big. A good example of this is like 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 like, investing in like competitive companies left and right. It's just like the space is so big because the opportunity is unlike anything that we've seen before. And while, you know, that affects how VCs operate from a startup perspective, it's like 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 massive, massively valuable business. And so that's why I tell people, don't over anything about it. The other thing I also think is important to remember, at Least from an OpenAI 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 a 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. And so if you kind of look at the decisions we make, this is all weave through it. Every single model we've released in one of our products gets released in the API. Like even, you know, 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 like all of our, you know, 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. And so, you know, we don't block competitors. We allow people to have access to our models. We also want, you know, like, we've recently been testing more of like the sign in with ChatGPT, you know, product as well. And so we want to foster this ecosystem. I think it's really important that we do. So the general, like, thinking about this is like, you know, a rising tide, like lifts all boats and, you know, we might be an aircraft carrier, we're like pretty big at this point, but we think it's important to raise the tide because everyone kind of benefits. And I think we'll benefit as well. Like our API itself has grown pretty significantly because we act in this way. And so I'd really encourage people not to view OpenAI as this kind of like, you know, thing that'll just shove people out of the way, but instead focus on building something valuable and we remain committed to providing an open ecosystem.
- [60:51]
Lenny Rachitsky
Why is that important to OpenAI? Just this focus on building a platform, creating A way for people to build businesses. Just like, is that just. That's been the vision from the beginning. We want this to be a platform.
- [61:04]
Sherwin Wu
It's been the vision from the beginning. It comes, goes back to our charter, actually, like our mission. So the OpenAI's mission has always been to one, to build AGI, so we're obviously doing that. But then the second thing is to like spread the benefits of it to all of humanity. And there's kind of like a lot of, you know, the main part there is all of humanity, like, and obviously ChatGPT is trying to do this. You know, we're trying to reach however many, you know, the whole world, but very early on. And this is why we launched the API back in, I think it was like 2020 or something, like really early. We don't think we as a company will be able to reach all of humanity. Right? Like there's, I don't know, every, every corner of the world's like, like pretty, pretty, pretty deep. And so 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, you know, the customer support bot for podcasters and newsletter hosts, because we're not going to be able to do it ourselves. And so 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. But yeah, it's been there since day one because it's kind of, we view it as an expression of our mission.
- [62:12]
Lenny Rachitsky
And you haven't even mentioned the App Store that you guys are launching. The ChatGPT app store.
- [62:16]
Sherwin Wu
Yeah.
- [62:17]
Lenny Rachitsky
Is that under your umbrella, by the way, or is that a different Oregon team?
- [62:20]
Sherwin Wu
It's a different team. So it's under ChatGPT. We obviously collaborate very closely with them and you know, they built like an apps SDK which is built in close collaboration with our team, but that is more within the ChatGPT umbrella. But that is also another, like, that's another example of this, right? It's like ChatGPT is like, we kind of like have these 800 million weekly active users who are just coming over and over again. Like, it's a great asset to have as a business, but like, man, would it be better if we could somehow allow, you know, other companies to come in and, and take advantage of this as well and build for this, this audience as well. And then ultimately we think it'll help us expand that, that, that group as well, right. And so it's all, it all kind of comes back to the mission and we find that being a platform, being open tends to help here.
- [63:06]
Lenny Rachitsky
Just that number, 800 million. I think it's maus just like weekly.
- [63:11]
Sherwin Wu
Weekly.
- [63:11]
Lenny Rachitsky
Weekly, yeah.
- [63:12]
Sherwin Wu
It's crazy.
- [63:13]
Lenny Rachitsky
Billion people using weekly. Like it's absurd how many know how these numbers we're just used to now. But that's insane. Unprecedented.
- [63:22]
Sherwin Wu
Yeah, it's, it's mind boggling for me to think about from a scale perspective, honestly. And the way I think about it is like 10% of the world and growing, by the way. Like, it's just, it's, it's shooting up. Come to ChatGPT and use it every day or, sorry, every week.
- [63:38]
Lenny Rachitsky
And this point, I just want to double down on this point you're making. OpenAI's mission was to make AI available to all humanity. And I think some people diss that. They're like, oh, you know, it costs money. And it's like, like the fact that it, it's. There's a free version of ChatGPT that anybody can use that is not so different from the most powerful AI model that exists in the world for free, that's not gated that anyone could use. Like, if you have, if you're a billionaire, there's only so much more you can get out of AI than what someone, you know in a village in Africa can, can get. And I know that's always been really important to OpenAI.
- [64:12]
Sherwin Wu
Yeah, yeah. I mean, look, that, that's why I think we've leaned into the health work. We've lean into like, like education is going to be a very interesting here. The other insane kind of trend here is, is the free model has gotten so smart over time. Like the free model back in 2022 was, you know, like, was good at the time, but it's like nothing compared to what you get today because you get GPT5 today. And so the like, you know, raising the floor across the world is kind of, you know, 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 like, you know, kind of talking about like the billionaires or whatever. I know people love saying like, you're using the same iPhone that like, you know, Steve, or sorry, like Mark Zuckerberg is probably using or like the billionaires are using like for like $20 a month. You're basically using, you know, like using the same AI that, you know, the billionaires are using for like $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. And so yeah, this kind of democratization and just 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.
- [65:22]
Lenny Rachitsky
One last question, just for folks that are thinking about building on the API or just like, oh 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.
- [65:38]
Sherwin Wu
So fundamentally the API offers a bunch of developer endpoints and these developer efforts 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'll work for a while. So what you can basically do is you can, 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 API. With that, it is super unopinionated and you can do basically whatever you want. It's like the lowest level thing. We've also started building more and more kind of like 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 Responsys 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 the scaffolding actually, you know, we'll see where this all goes, but it makes it a lot easier for you to build these, these, these, these kind of agents giving it guardrails, allowing it to like farm out subtasks to other agents and, and kind of like orchestrate a swarm of agents. The Agents SDK kind of allows you to do that. And then above that we've now Started building tools to help also with kind of like 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, you know, a lot of times these agents kind of look very similar from a UI perspective. And so there's Agent Kit. We also have a smattering of like 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 E Dolls product. And so, yeah, I view it as these various layers, they're all kind of 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, down the stack as low as you want to, basically responses API and build whatever you want because of how low level it is.
- [68:17]
Lenny Rachitsky
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?
- [68:27]
Sherwin Wu
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 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 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.
- [69:19]
Lenny Rachitsky
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. Like what's, what's your advice to folks that are like, okay, I don't want to miss the boat?
- [69:32]
Sherwin Wu
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, you know, is part of the story. Just using the tools. Like you don't, you know, you don't need to be a software engineer to, to lean into this. I think a lot of jobs are going to, going to, 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. Yeah, and so it's basically like getting used and getting, getting used to the technology and getting familiar with it instead of kind of like laying back and letting it, letting it pass.
- [70:06]
Lenny Rachitsky
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 cloud bot this week.
- [70:16]
Sherwin Wu
Oh God.
- [70:17]
Lenny Rachitsky
What Is there something you've 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 learned?
- [70:28]
Sherwin Wu
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 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, 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 ChatGPT 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.
- [71:39]
Lenny Rachitsky
Amazing, Sherwin. With that we reached our very exciting lightning round. I've got five questions for you. Are you ready?
- [71:45]
Sherwin Wu
Yeah, yeah, absolutely.
- [71:46]
Lenny Rachitsky
First question, what are two or three books that you find yourself recommending most to other people?
- [71:51]
Sherwin Wu
I'll talk about one nonfiction one on one fiction book. The fiction book was. I just finished reading it. I really recommend it. There is no Anti Memetics division by qntm. I think he's like an online author but I saw it being shared on X. This, this. It's like a science fictiony kind of book and it was, I basically devoured it in like two days. It was, it's super, super well written, super fascinating. It's about a government agency that's fighting, you know, things that make you forget it. And so it's just a very like smart like creative book that, that and fresh honestly in terms of like source material that, that, that I really like. So I'd recommend that one. The book is also unintentionally hilarious. So 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 kind of like the U.S. 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. First one is the Dan Wang book Breakneck. That one was really, really good. I really liked his analogy of like the lawyerly US is the lawyerly society, China is the engineering society and their pros and cons to each. I read it and I was like yeah, it does does seem like we're run by lawyers in the U.S. so that's one. And the other one is the Patrick McGee book on Apple in China was super, super interesting. I'm a huge Apple fanboy. Like 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 like 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, you know, a very, very timely, timely book as well.
- [73:39]
Lenny Rachitsky
The Anti Memetics book sounds amazing. I'm buying it right now.
- [73:42]
Sherwin Wu
As you're talking. Yeah, yeah, it's like, I think it's only like a couple hundred pages. I literally finished in two dreams. It was just like so, so good.
- [73:49]
Lenny Rachitsky
Okay, great tip. Okay, favorite recent movie or TV show you have really enjoyed?
- [73:54]
Sherwin Wu
Yeah, that one's tough because, you know, with 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 of episodes that there's a new season of this anime called Jujutsu Kaisen that's out. So season three of JJK was, was, 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, haven't really watched much but saw a couple episodes of JJK recently.
- [74:40]
Lenny Rachitsky
Extremely understandable in your role. Yeah. Favorite product you recently discovered that you really love?
- [74:45]
Sherwin Wu
Yeah. Okay, so, so, so I recently had to set up wi fi and like 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 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. And 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 the 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.
- [75:47]
Lenny Rachitsky
All right, I went eero. So I made a mistake.
- [75:50]
Sherwin Wu
Eros are pretty good too, but it's not Ubiquity. Fully converted to ubiquity at the moment.
- [75:53]
Lenny Rachitsky
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?
- [76:00]
Sherwin Wu
Yeah, the one that I always, you know, 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.
- [76:22]
Lenny Rachitsky
Last question. So in your previous life, you worked at Open Door, where you led work on basically figuring out how much to pay for houses. You basically built a model that told the company, here's how much we'll pay for this house. What's like a variable in the price of a house that you didn't expect is really important and impacts the price of a house.
- [76:40]
Sherwin Wu
There's a bunch that were surprising. I'll maybe list the couple of most interesting ones. Power lines and high voltage power lines are super. Actually impact your price quite a lot. I didn't really fully internalize this until I went to Dallas and observed when your house sits next to one of these giant voltage lines is buzzing, and most people have families, you don't want your kids near there. So I think that was one that really, really kind of surprised me.
- [77:10]
Lenny Rachitsky
That makes sense.
- [77:11]
Sherwin Wu
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 of, like, how wide is the kitchen? And like, is it a. What style of kitchen is it? And then like, where's the master bedroom? And. And so it was just really, really hard to quantify. But I remember floor plan was a big one because we'd have a home that wouldn't sell, and then our ops team would go in and be like, yeah, that's the floor plan issue. So how could you tell? It's like, you go inside, you just feel it. It feels. The floor plan feels off. So, yeah, those are ones that were surprising. And then the last one that was more impactful than I thought is general curb appeal and even 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.
- [78:18]
Lenny Rachitsky
That is extremely interesting. And I Love that you had to figure how to do all this in code and not.
- [78:24]
Sherwin Wu
Yeah, yeah, 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 all these homes in Phoenix and Dallas. Yeah, a lot of fun stories from the open door days.
- [78:38]
Lenny Rachitsky
Okay, Sherwin, thank you so much for doing this. This was incredible. Where can folks find you online and how can listeners be useful to you?
- [78:45]
Sherwin Wu
Yeah, so I'm online on Twitter on X. I'm just Erwin Wu. And yeah, I mostly just tweet about OpenAI and the 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 just 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.
- [79:12]
Lenny Rachitsky
Amazing. Sherwin, thank you so much for being here.
- [79:14]
Sherwin Wu
Yeah, thank you, Lenny.
- [79:15]
Lenny Rachitsky
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@lennyspodcast.com See you in the next episode.