# OpenAI Engineering: AI Agents and the Future of Work

**Podcast:** Lenny's Podcast: Product | Growth | Career
**Published:** 2026-02-12

## Transcript

95% of engineers use codecs.
100% of our PRs are reviewed by Codex for engineers.
I don't know what job has changed more in the past couple 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.
A 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.
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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.
And 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 codecs is a tool has gotten for us.
And uh it's it's a little hard for us to exactly measure how much of the code is is written because the vast majority of it, I'd say like close to 100% is is usually generated by AI first.
Uh what we do track though is is you know, at this point, uh the vast majority of engineers use codecs on a daily basis.
So 95% uh of engineers um use codecs.
Um 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 uh suggests improvements, suggests changes uh uh in the PRs.
And so uh that's kind of what we're seeing internally.
But by and large, the most exciting is just the energy that that there that there is.
Um another observation that we've had is uh engineers who tend to use codecs uh more uh open way more PRs.
So uh they're actually opening 70% more PRs uh and uh than the engineers who aren't using codecs as much.
Uh and the gap is widening.
So I feel like you know the people who are opening more PRs um are starting to, you know, learn how to use the tool more and more, get more efficient, and that 70% gap keeps uh uh growing over time.
And so might have actually increased since I last looked at the 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% uh engineers at OpenAI is written by AI.
It's written and then they review it.
Yep.
Yep.
It's 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.
Uh there's also I think some, you know, uh engineers who I think trust uh codex a little bit less.
But um basically every day I talk to someone who who uh is blown away by something that I can do, and and kind of like the their bar of trust kind of uh uh or like how much they trust the model to do on its own goes up over and over uh over time.
And uh there's a quote from Kevin Whale, our our um uh 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 Wheel, former podcast guest, uh, he he said exactly that line on this podcast in a few times.
Yeah.
Uh Peter, the ClaudeBot slash Multbot slash open claw is what it's called now.
Uh developer uh recently shared that he uses codecs 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 um user of codex.
I know he's in close touch with the team, gives us great feedback.
Um not surprised that he uses it.
I mean, uh sorry, it's called open claw.
Open claw.
Yeah.
Open claw is a great is a great product.
And then I saw that this more, I mean, this is very recent, but this morning, I think MOLT book uh kind of like uh uh with Sheridan as well, and seeing all of the uh AI agents talk to each other is pretty uh 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, it's I mean, it's always been really cool to see.
Um, uh, and it's part of where the excitement is because uh like the job is likely going to change pretty significantly over the next one or 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 uh I see this moving.
So I think there's a common thing that everyone's saying, which is uh, 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.
Um I know many of the engineers on my team basically have like 10 to 20 uh threads kind of being pulled on at the same time.
Obviously, not active running codex uh jobs, but uh just a lot of parallel threads.
They're checking in on what they're doing, they're steering the agents uh in codex and and and and giving it feedback.
And so their job is 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 uh kind of metaphor that that I kind of always come back to here is actually from this uh is from this uh programming textbook uh that I read back in college called SICT.
I don't know if you've heard of it, uh structure and uh interpretation of computer programs.
So S I S I C P.
Um at MIT, it was it was really popular and and it was actually used as the uh uh introductory, it was the textbook for the intro programming course for a very long time.
Um and it kind of has this cult following.
Um it teaches you programming, uh it teaches you a dialect of Lisp called scheme.
Uh and so it like introduces you to like functional programming.
It's like very mind mind opening that way.
But the thing that was memorable for me about that book, so I I kind of read it in college.
Um, the very beginning of it kind of describes programming as a discipline and draws this metaphor to basically like sorcery.
Like it says like software engineers are like wizards, and you're like you're like programming languages are like incantations, and you're like, you know, you're you're saying 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 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 uh 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 and the trend has been that these it's been easier and easier to kind of get them the computer to do what you want uh via programming.
And I think the current wave of AI is is probably the next stage of that evolution.
It is now literally incantations because you can tell, you know, your uh you can tell codex, you can tell cursor uh exactly what you want to do, uh, and then it'll all go do it for you.
Uh and I particularly like the wizard and like the the sorcery analogy because uh I think our current state is starting to move towards kind of like the the sorcerer's apprentice, uh, you know, from Fantasia, uh, where Mickey Mouse is like, you know, he finds the sorcerer's head and he tries to do all these things.
And I actually think it's a really apt analogy because one, uh, it's just it's really powerful now.
These incantations you can do can is it's 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 uh the brooms off on a task and then goes to sleep.
Uh and and so you know, it's like vibe coding at its at its at its greatest.
And then eventually the the old sorcerer comes back and like cleans everything up.
And um, you know, when when I see engineers kind of like doing these, these these 20 different uh codex threads at a time, there is some skill and there's some seniority and like you know, uh a lot of thought that needs to go into this because you want to make sure that the the models aren't going off the rails.
Uh you definitely don't want to just like completely uh go away and and you know like ignore ignore the thing, but it's also extremely high leverage, like you know, a very senior engineer who's who's really prolif uh proficient with these tools, uh, can now just do way more things via uh 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 to to to having uh uh uh to to making making it feel like this like magical experience where we're you know 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.
Uh 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.
Yes, how big is it?
Yeah, or it might be like the monkey's paw type thing where you know it's like you caught what you want, but what are the side effects?
Um yeah, yeah, I think that the analogy is great.
And um, yeah, the crazy thing for me is just the staying power of that book, Sickby.
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 um, we're we've basically reached that point now, which is which is 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.
Do you fire off all these you know codex agents and then you have to keep stay on top of them?
Oh shit, one's not working, I'm wasting time.
Uh, do you 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 that 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 uh with with codecs 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 that's actually doing an experiment right now uh with an open AI where they are basically maintaining a 100% codex written code base.
Uh so you know, like, you know, uh uh some, you know, you you'll have the AI write code, but you'll obviously end up like rewriting a lot of it and 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.
Uh and they run into the exact problems that you're describing, which is like, you know, their challenge is, you know, uh, 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 codecs, I might use like tab complete and and cursor and and things like that.
But this team uh uh for the experiment, this team doesn't have that escape hatch.
Uh and so then the challenge, like, how do I get the the the agent to do this?
And um, I actually think we're gonna be publishing a blog post from some of our learnings here.
Um, but a lot of fascinating like paradigms and best practices are falling out of this.
Um interesting thing that we've noticed, I don't know if this is what you you kind of feel, but we definitely feel it here is a lot of the time uh 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 you've either underspecified or there's just not enough information around how to do something available to the agent, available to codex.
Uh and so uh when when you have to solve it through through that, uh the challenge is then to add documentation and actually work around this 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, dot MD files, skills, any type of additional resources within the repository so that the model can um uh can better do its task.
There's a whole bunch of other learnings from this uh this group, which I think is fascinating uh to explore, but yeah, kind of giving removing that escape hatch of of no longer using the AI has allowed them to start piecing together a lot of the problems that uh we'll have to solve if we really want to lean into agents.
Another uh issue people run into, you talked about how people are shipping PRs like crazy, a lot more PRs if they're working with AI.
Uh 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 as 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 uh I actually think so.
We tend to hand to the models immediately tend to be the things that annoy us or like are the most boring parts of uh software engineering.
It's also why it's more fun now because we get to do more, you know, more of the fun things.
Um for me, um, speaking more for myself, I really hated code reviews.
It was like one of the worst things for me.
And then I remember in my first job uh uh out of college, uh, it was that it was at Quora.
Um, I owned, I was working on the newsfeed.
And so I owned the code for the newsfeed.
And so I was a reviewer for newsfeed, and uh 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 like 20 to 30 code reviews.
I should be like, oh my goodness, I gotta like, you know, get through all of these.
Um 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 uh 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 uh a bunch of suggestions uh already, already baked in.
Uh a lot of the times people will uh, especially for small PRs, like you you actually don't even need people to review.
We kind of trust codex in this way.
Um 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 uh that's something that that we've heavily leaned into.
Um the general CI process and like the post uh 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 you the most is after you've written your beautiful code, like how do you get it into production?
You know, you gotta you gotta run through all these tests, you gotta like you know, limp errors, you all have code review.
Um, there's a lot of automated stuff you can do with codex.
And so we've actually built some tools internally that that help automate that process, automate the lint, you know, if there's like a link to error, it's a very easy codex fix.
Uh, and then just it could just patch it and then kind of restart the CI process.
Um, so all of that is we're trying to collapse as as into as as little work for an engineer as possible, which and the byproduct of which is uh um uh they can they can now merge and push out a lot more PRs.
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.
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 beroms go crazy here.
Um and so you know, we we're very thoughtful, I'd say, around which PRs kind of are completely just codex uh reviewed.
Most people still obviously take a look at their PRs.
Uh, 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 which just helps things push through.
Uh in terms of like multiple models, uh, so we we obviously test a lot of models internally, and so we have a lot of those.
Um we use uh external models less.
Um it's we we think it's important to kind of dog food our own models and kind of like get feedback there.
But uh you can also, you know, there are a lot of like internal variants of models that you can use to give you different perspective um here as well.
And and we found that to work quite well.
Okay, so just to just to make sure we get a like a barometer of today's world at OpenAI in terms of AI and code, uh, just so I understand, and then I want to move on to different topic.
Uh 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 was is written by AI.
Uh and and just it's kind of hard to to do attribution there.
But the like almost every engineer heavily uses codecs 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, it's 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 is your life as a manager changed with the rise of AI?
And just what do you where do you think managers, what's the role of a manager in the future?
Is that something changed less than an engineer?
Uh there's no you know codex for managers, just uh yes yet.
However, I use codex quite a bit for for some of the um uh some some of some of the like kind of more managery tasks that I do.
I'd say a couple of things are are changing.
They're like some trends.
So I don't think it's changed that much yet, um, but I see trends, and I think if you play it out, you can kind of see where where a lot of this is going.
One thing that that's becoming increasingly clear is codex really empowers like top performers to to get a lot like to be a lot more productive.
And so it really like, and I think this is maybe 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 get get good at these tools, will kind of supercharge themselves.
Uh and so I'm kind of noticing this now as well, which is like the top performers kind of end up uh uh uh being a lot more a lot more productive.
Uh and so you see a broader spread uh in in team productivity in this way.
One so one thing that I've always done as as a management philosophy is to spend uh actually the majority of my time with top performers, just like make sure they're unblocked, make sure they're happy, make sure you know they're they feel productive and they feel heard.
I think this is even more true uh in an AI world where you know your top firmers are gonna just like really be shooting ahead uh using these tools.
I think I think one example is is the the team that's you know maintaining a 100% codex generated code base, like just letting them kind of rip and see what's happening there is something that's that's paid dividends.
So I think that that's kind of one trend that I'm seeing where you where you're where um spending even more time with top performers for managers, I think is is likely gonna um uh continue.
The other thing is I I so this is more uh 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 uh um 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 give it get a really good sense of what this person has done over the last 12 uh months uh and writing a little you know 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 you know, like software engineers are managing 20 to 30 codexes.
Um my sense of these tools will allow managers, uh, people manage to be higher leverage.
Um, and uh it will allow them to manage you know teams of way more than than the current best practice of I think is like six to eight, right?
For software engineering.
You kind of see this applied to, you know, like uh the non-uh engineering domains like support or uh operations, where it's like, you know, previously um uh where previously like the 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 um people management as well, especially in tech companies.
Um we're already seeing this.
There's some teams uh where uh their EMs 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 uh and operate in that way.
I love this advice that what 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 Mark Andreessen used just on the podcast, the way you phrase it is AI makes good people better and it makes great people exceptional.
Yeah.
Yeah.
And what you're saying here is 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 uh 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, uh, whatever best practices come out of this, you know, we we have to share with the org.
Well, we'll, you know, uh we'll we'll uh 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 this I 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 gonna 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 you're 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 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 in the second or third order effects of this.
And and really what, you know, because because what the one person billion dollar star 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 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 billion, 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 just gonna 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 become 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 100 X more of these startups.
Uh 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 uh other types of you know, small, small one person, you know, billion dollar startups.
And so I think we might actually end uh enter into a golden age of like B2B SaaS uh and just like software and startup in general.
And so I think I think that's that's a really interesting trend 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 hundred 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 set for life at that point.
And so, you know, we might really see see an explosion in that way.
And I 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, uh, all of these, like as 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 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, uh, which are not great for venture seller 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.
I love how many uh order like uh order effects we've been through.
Uh whenever we have the fourth order effect now, sure.
I'm just joking.
I I can't, I it's too fourth order is too too is too gigabrained 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 selling every layer.
Um, so the billion dollar startup, I've been, I think about this a lot because I I'm not gonna 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 Oscar 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 think that's true.
Uh and actually, I think my view on it is slightly different, which is I think that your, you know, Lenny's podcast might end up becoming a billion-dollar startup.
But um, what I think might happen is uh instead of you kind of being the one person who has to dispassion 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, uh, there might be like 10 or 20 startups that build support software for podcasts and newsletters.
And uh that might be a one-person startup.
Like it doesn't need to be a big one.
And uh 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 um as the one-person billion dollar startup.
I would buy that.
I would buy that.
Yeah, there's like a question of like what you in-house and what you what you like kind of uh outsource.
And what I think might happen is because the cost of writing software and building products is is collapsing so much, you might end up outsourcing a lot of this, and in doing so, reducing the size of your company.
Uh 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 leverage company that might actually reach a billion dollars.
I could see that.
I also think about Peter at ClaudeBot slash MoldBot slash open claw, of just like how he barraged he is right now by all these asks and emails and pings and DMs and PRs, just like, oh, I'm curious to see and he's not even making any money off this thing.
Um yeah, I I can't imagine what it's like to be him right now.
It's it must be like absolutely insane.
It probably it's probably like um uh, you know, like the the the months after we launched ChatGPT, the craziness that was uh as one as one.
Uh he's coming out on the pod by the way, in 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 stuff.
Okay.
Uh I wanted to come back actually to your management stuff.
So I really loved your insight about spending more time with our 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?
Yeah.
I think a lot of the lessons that I've learned here, I don't know how specific it is to the open API or some of our enterprise products in particular.
I think my management philosophy is obviously changed over time, but I think it 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 it is kind of come back to this analogy of software engineer as as a surgeon, um, which comes from the the mythical Man Month book.
So it is actually it's funny.
So I 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, uh, 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 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 assist and the resident and the fellow, and then the surgeon's like, I need a scalpel, and they give them scalpel, and then uh uh they're like, I need you know, this tool and that's 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 engineers are gonna 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 uh empower them, make them feel like they're a surgeon.
Um, and insofar as 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 I 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 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 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 uh um management and and especially uh engineering management.
And so that's something that that's really, really um stuck with me over time.
And uh, 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 uh rest of my career.
I love that.
And I I feel like I wonder if that's something AI can help with is look around corners and predict here, this engineer is gonna be blocked by this decision.
We need to figure this out.
We need to get a lot of.
Yeah, that's actually a really good uh point.
I haven't tried this yet, but I wonder what would happen if I ask uh Chad GPT 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 help?
Um, is that very interesting?
I have not thought about that, but you're right.
Yeah, yeah, yeah.
Uh 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?
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 vloggers will be next month too.
Uh I think we've got a we've got a good idea right here.
Yeah, yeah.
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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 your on your tools.
You told me that you find that a lot of companies actually have negative ROI on their AI deployments, which uh I think is what a lot of people read about and feel and think.
And it's interesting you're 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?
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 I would not be surprised if uh a lot of AI deployments are actually, you know, negative ROI.
I mean, part of this too is that I think there's also general sentiment um from uh folks uh around the country, um like basically outside of tech that AI is being forced onto them.
Um and I think part of this is is is uh uh uh probably a symptom of some negative ROI uh AI deployments.
A couple of things I've observed around this.
So one thing is, and I think I 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, sorry, X is a bubble, um, Silicon Valley is a bubble, software engineering is a bubble.
Most people uh in the world, most people in the US 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 codecs, all these like codex pill people within open AI.
Um sure everyone on X Who posts are like crazy power users of of these AI tools, you know, they 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 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 uh 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 um more companies do, or like what should do or or what what a more ideal AI deployment setup looks like.
Um, and and this is kind of how we've run things within open AI too.
Um the companies where I think it's it started to work really well have a combination of both top-down buy-in.
So it's like the C-suite's like, you know, we're we're we want to become an AI, AI first company.
Um 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, OpenAI has always wanted to be uh a very AI-centric company, but where when it really started taking off was when was with the introduction of codecs and these tools where like people then like actual employees themselves could start applying it to their work.
Uh 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.
Uh software engineering is different than finance, is different than operations, different than go to market and sales.
Uh, 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 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 is 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 um, I'm not sure what to do.
Uh and they look around, no one else is doing it, there's no one else to learn from.
Uh, 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 um explore the full extent of the capabilities, apply to specific workflows, do the knowledge sharing, uh, create excitement uh within folks uh who might want to use this technology.
Uh, because in the absence of that, it's very difficult to, it's actually very difficult to pick up.
And who 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 uh 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 they those are the ones who get 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 times 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, uh, 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 has software engineers.
Um is actually kind of a rarity.
They're they're they're hard to find, they're expensive.
Uh, and so it's it's these other other types of folks.
What I'm hearing is the anti-pattern is top down.
This is very the CEO 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 gospel, you find that doesn't work.
Yeah, yeah, exactly.
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 T AI kind of evangelist team that finds ways 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 imaginative philosophy is just 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 uh of excitement internally.
Okay, amazing.
There's a couple hot takes I wanna hear uh from you.
Something that I've seen you talk about and share.
One is um 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 so obviously you should talk to your customers.
Like it's it's like useful to talk to customers.
I just think the AI field, um, especially what I've seen over the last kind of like three years, um uh working on the API and and and seeing kind of all that evolve, is the field and the models themselves are just changing so so quickly.
They tend to like disrupt themselves, especially around the like tooling and the scaffolding space.
So uh there's this quote that I read actually uh earlier this week from uh it's from an ex article uh by this guy named Nicholas, who's who's the founder of a start called FinTool, uh, where uh I think he was he was sharing a lot of the best practices that he has learned through building AI agents for financial services, I think at a at a start FinTool.
Um this phrase that I thought was really good, which is uh the models will eat your scaffolding for breakfast.
Like if you look, if you rewind back to 2022, right when ChatGPT launched, um, these models are pretty raw.
And there was like all this product scaffolding and 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 like like vector stores, I think was like really popular back then, uh, and just like a whole smattering of tools here.
And as you've kind of seen the feel play out, that the models have just changed so much uh that uh and gotten so much better that they ended up yeah, literally eating some of some of the scaffolding.
Um, and I think this is even true today.
So I think the the article from Nicholas um actually, you know, the the current scaffolding, which is uh fashionable is skills files-based context management.
I could see a world where at some point, you know, that's no longer useful, uh, where the model can actually you know manage all that themselves, or like, you know, uh uh, 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.
Um, there's a period of time like 2023 where we thought vector stores and is is is going to be like the main way for you to, you know, bring organizational context into the models.
And you need to, you know, uh vectorize and embed every bit of your corpuses, and then you do all this work to like figure out the vector search to like optimize that to pull out the right information at the right time.
All of that is scaffolding because the model, you know, was not good enough.
And turns out, you know, in this case, it turns out as the models get better, uh 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 uh and agents MD uh to kind of steer it uh as well.
Obviously, there's still a place for vector stores.
I know a lot of companies are still using it, but the the the entire scaffolding around that and building an entire ecosystem around that and assuming that's the only scaffolding that you need has has really changed and so tying this back to the like you know uh it 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'll they'll be like yeah I want a better vector store like I want a better uh I want a better you know agent framework for this and uh 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 the right uh uh abstractions and the right tools and frameworks to to build uh around these models um and the cool slash exciting slash kind of crazy annoying part is it's a moving target and so yeah like the current current smattering of of tools and frameworks right now will likely need to evolve and change pretty significantly over time um as the models get smarter and better but that is just the nature of building this space I think that's what makes it exciting uh but it also means when you talk to customers, you kind of need to balance the exact feedback that they want uh with uh where you think the models are going and where you think things will uh trend over the next one or two years.
It's interesting how this is um the bitter lesson is uh you know, this big lesson that AI and ML folks learned, which is just like uh don't the less you overcomplicate the less logic you add to to machine learning to AI, the more it'll be able to scale and grow and just like take it all the way and let it just just compute basically, just give it more power to get there on its own.
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 it turns out the models will just kind of you know eat it all away.
And and and and honestly, like open AI API team has like been guilty of this, uh, where we kind of like took some you know left and right turns uh when we shouldn't have.
Um, but uh yeah, the models still end up, models get better, and uh 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 you know, having to build a little bit of this around for now?
Is it just yeah, 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.
Um, you know, the the it's it's clearly a 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 like type of capability that is like maybe 80% of the way there today.
And it like they end up, you know, having a product that like kind of works, but it's like just almost there.
But then as the models get better, you know, suddenly it might click.
And then their product now is incredible because it works, you know, like uh uh like maybe with like oh three at some point it suddenly works with 5.1, 5.2, suddenly it unlocks it.
But they're building these products with the like the model capability improvements in mind.
And with that, you end up creating an experiment experience that's way better than if you had assumed that it's it's static in the first place.
Um and so that would be my general uh advice, which is you know, build for where where the models are going and not not where they are today.
You end up building a better product.
You may need to, you know, like wait a little bit, but like, you know, the models are getting so much better so quickly you you often don't need to wait um that long.
So to follow that thread, where are like in the next six to twelve 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 more successful about, or do you think that people should start to prepare for and however much you can share?
I mean, so the obvious one is um how long of a task uh these models can do coherently.
Um so there's like the the meter benchmark that that I think tracks software engineering tasks and how long, you know, like how long of a task can these models do uh 50% of the time, 80% of the time.
Uh I think we're at something like multi-hour tasks being able to be done by uh software engineering tasks being able to be done by um uh these frontier models uh 50% of the time.
And then I think 80% is something like just under an hour.
But the the the sobering thing about that that chart is they plot all the uh previous models uh 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 it's in the Cly.
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 codecs to the limit and do like multi-hour long uh tasks.
Uh, but again, I I think that that's more of the exception.
But I uh if you follow this trend, like I think like in the next 12 to 18 months, we could see models that could do multi-hour long tests very, very coherently.
At some point it might reach like you know, six hours a day-long task where you kind of like dispatch it and have it do, you know, do things on uh 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 but you probably don't.
Um, and and then the the universe of things you can have the model do really expand.
So that's something that I'm really um really excited about seeing.
Another uh thing over the next 12 to 18 months where I think would be really cool is uh improvements in our in the multimodal models.
So uh, and and actually by by multimodality, um, I'm mostly thinking about audio here, where uh the models are pretty good at audio.
I think they're gonna get a lot better um at audio over the next six to 12 months, especially the likes, you know, the um native multimodal models, the speech to speech ones.
I think there's also interesting work uh being done around um new types of models and architectures on the uh multimodal audio side uh as well.
But uh audio, especially in the enterprise and in a business setting, I think is a hugely underrated uh domain still.
Like everyone talks about coding, it's all text.
Uh, but uh we're talking uh in audio uh a lot of the world's business is done via audio uh a lot of services and operations are done via uh talking and audio and so uh I think that that area is gonna look very exciting in the next 12 to 18 months and I think there will be uh even more unlock for uh what we can do uh with with audio models uh there as well amazing so quick summary uh expect agents and uh 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 and native and better and 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 uh you're very bullish on business process automation as an opportunity in the world of AI talk about that.
Yeah this go this goes back to the thing that I said previously which is um we we we live in a bubble in Silicon Valley and um a lot of the work that we do that we're used to software engineering you know product management building products uh is very differently shaped than the work that goes on um that runs our entire economy.
And I see the saying and now when I talk to customers.
If you if you talk to any like you know company that's not based in, it's not a tech company, um, there's a lot of business processes.
And so what what I mean by this is is you know, I generally delineate it as you know, there's like uh like software engineering is kind of like open-ended knowledge work, right?
It's like, and this is why I think uh tools like codex tend to be quite quite good because it's exploring and you're giving it these like open-ended things.
But software engineering fun is fundamentally like pretty open-ended uh and is not very repeatable, right?
So, like you build a feature, you're not trying to build the exact same feature over and over again.
And a lot of like 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 is core and tech, a lot of jobs are just business processes.
They're like repeatable things, uh repeatable operations that you know, some manager at a company has kind of like iterated on.
Um, there's usually a standard operating procedure that people want to do, uh, and you don't want to deviate from it that much.
You know, there's like in software engineering, the ingenuity isn't is in deviating, but a lot of a lot of the the work being done in the world is actually just um running through these procedures and operations.
Like if I, you know, if I call um 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.
Uh and so I'm I'm just extremely bullish on this general category of like, and 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 um AI and some of the tools and frameworks that we have towards this business process automation, towards automated automating and making easier um repeatable business processes with high determinism that is fully integrated with business data and business decisions and and and different systems within an enterprise.
And how can we actually make that 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 we just don't talk about it because it's it's uh a little bit less uh uh in our wheelhouse.
So your take here just to make sure I fully understand it is you think there's a much uh bigger opportunity outside of engineering for AI to impact uh productivity of companies and also jobs of these folks that are doing these kind of repetitive easily automated tasks.
Impact jobs 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 like basically we I I talk to customers all the time big enterprises like like how how will AI transfer my company like how will it run in in in in a world with AI in like 20 years?
And 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 extensive uh as well.
But it is pretty massive.
And it's definitely bigger than you know, uh uh uh 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.
Okay.
Uh in going in a slightly different direction, uh having built the platform building the API, uh, people building on API, the biggest question on people's minds is always just uh how do I not have OpenAI squash my idea and build their own thing and then you know destroy this 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 my general answer here is um the market is so big and so massive.
Like I actually think you know, startups should just not overly think about where open AI 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 open AI 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 with the customers.
Whereas the ones that take off, like even in very competitive spaces like coding, like cursor's huge at this point, and it's because they build something that people really love.
And so my general advice is like don't, you know, don't overly stress about this.
Just build something that people like and you will you will have a space in this.
I can't overstate how big of an opportunity there is right now.
Like the the opportunity space in building with AI is so big.
Like a good example of this is is like the space is so big that the overturn 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 uh that that affects how VCs operate, from a starter perspective, it's like the most empowering thing in the world because the like even if you just build something that that some people really, really love, you will you will end up with a massive, massively valuable business.
Uh and so I that's why I tell people like don't don't overthink about it.
The other thing, like I also think is important to remember, uh, at least from an open AI perspective, one thing that that that we've always held very near and dear, which both Sam and Greg helped, you know, reinforce from the top as well, is we actually view ourselves fundamentally as a like ecosystem platform company.
The API was our first product.
We think it's really important for us to foster this ecosystem and continue to you know uh support it and not squash it.
And so if you kind of look at the decisions we make, it this is all we've 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, uh customers end up using those.
We don't hold back on any of that.
Uh, we think it's really important to keep our platform neutral.
Uh, and so, you know, we don't block competitors.
Um, we allow people to have access to our models.
Um, uh, we also want, you know, like uh we've recently been testing more of like the sign in with Chat GPT, you know, uh product as well.
And so we we we want to foster this ecosystem.
And we think it's really important that we do so.
Uh 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 like pretty big at this point, but we think it's important to raise the tide, uh, because everyone kind of uh benefits, and I think we'll benefit as well.
Like our API itself has grown pretty significantly because we we act in this way.
And so I'd really encourage people not to view open AI as this kind of like you know, thing that'll just uh uh shove people out of the way, but instead focus on on building something valuable.
Uh and we you know remain committed to to providing an open ecosystem.
Why why is that important to open AI, 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.
It's been the vision from the beginning.
It comes goes back to our charter, actually, like our mission.
Um, so the open AI's mission has always been to one to build AGI.
So, you know, we're always seeing 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, uh the main part there is all of humanity.
Like uh, and obviously Chat GPT 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 we launched the API, you know, 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 is 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, uh, because we're not gonna be able to do it ourselves.
Uh and so we've largely seen this play out with the API.
Uh, this is why we we you know, we we we we talk to so many of our customers and and really you know love seeing the diversity of of things built on.
But yeah, it's been theirs to say one because it's it's it's kind of we view it as an expression of our mission.
And you haven't even mentioned the uh the app store that you guys are launching, the Chat GPT App Store.
Yeah.
Is that under your umbrella, by the way, or is that a different Oregon team?
It's uh it's a different team.
So it's under Chat GPT.
We obviously collaborate very closely with them.
And uh, you know, they built like an apps SDK, uh, which is uh built-in close collaboration with our team.
Uh, but that is more within the Chat GPT umbrella.
Uh, but that is also another, like that's another example of this, right?
It's like ChatGPT is like we we we we 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 uh other companies to come in and and and and uh take advantage of this as well and build for this this audience as well.
And 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 uh we find that being a platform being open tends to help here.
Just that number, 800 million, I think it's M MA's, uh just like weekly, weekly.
Weekly act crazy.
Billion people using weekly.
Like it's absurd how many how these numbers we're just used to now, but that's in insane, unprecedented.
Yeah, it's it's mind-boggling for me to think about from a scale perspective, uh, honestly.
I and the way I think about it is like 10% of the world, uh, and growing, by the way.
Like it's just it's it's shooting up.
Um come to Chat GPT uh and and use it every day.
Or sorry, every week.
And this point I just want to double down on this point you're making.
Open AI's mission was to make AI available to all of humanity.
And I think some people diss that they're like, oh, you know, it costs money.
And it's like uh like the fact that it it's there's a free version of Chat GPT 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 open AI.
Yeah, yeah.
I mean, like uh that that's why I think we've lean into the health work, we've lean into like uh like uh education is gonna be very interesting here.
Um the other in 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 uh well, it's good at the time, but it's like nothing compared to what you get today because you get GPT 5 today.
Uh, and so the like, you know, raising the floor across the world is kind of you know something that we're really we're trying to do.
And then we view it as 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 or whatever.
I I know people love saying like you're using the same iPhone that like you know, Steve or sorry, uh like Mark Zuckerberg's 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.
Uh for like $200 a month, uh, you get the same pro model that you know all the billionaires are using, but they're probably not using pro for everything.
They're probably just using the the plus tier ones uh for their day in and day out.
And so yeah, this kind of like democratization and just like spreading of this this benefit like across all of the world is and that's really meaningful to us and something that um uh drives a lot of of what we do.
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 open AS 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.
Uh and and uh and these developer inverts basically let you sample from our models.
The most popular one that we have right now is one called responses API.
Uh, 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 use you can you know, at a very you know uh uh low level, you're basically just giving the model text.
The model will work for a while.
You can kind of you know pull it to see see what it'll do, and then you'll get the model response back at at some point.
That's like the lowest level primitive that we have uh for people.
And that's actually what a lot of people use.
That's the most popular way of building on top of our API.
With that, it is like 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 responses API or some other API endpoints that we have to build what you might more traditionally think of as an agent, like uh, you know, 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.
You know, we'll see where this all goes.
Um, but it makes it a lot easier for you to build these these kind of agents, giving it guardrails, allowing it to like farm out subtasks to other agents and kind of like orchestrate a swarm of agents.
Uh the agents SCK uh kind of allows you to do that.
And then above that, uh, we've now started building tools to help uh also with kind of like the meta level of deploying an agent.
Uh so we have this product called uh um agent kit uh uh uh and widgets, uh, which are basically a bunch of UI components that you can use to very easily um build a very beautiful UI on top of uh uh either our API or agents SDK, um, because you know a lot of times these agents kind of look very similar from a UI perspective.
Uh and so there's Asian kit.
We also have a smattering of like uh eval's products, like eval's API, where if you want to test and like you know, see if your models were your your agent or your workflow is working, uh, you can test it in a very quantitative way using our EDALS product.
And so yeah, that I view it as like these various layers, they're all kind of helping you build um what you want um with our uh AI uh with our models.
Um and with increasing levels of abstraction and and and uh you know how opinionated it is.
And so um you can start you can do the you can use a whole stack and and it it very quickly allows you to build an agent, um, or you can go down the stack as low as you want to basically responsive API and build um whatever you want uh 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 I'd leave folks with is yeah, I I think um I think the next like two to three years are gonna be some of the most fun uh in tech and in the startup world uh that that we'll have in a very long time.
And uh I would just encourage people to not uh not take it for granted.
Like I I entered the workforce in 2014, it was great for like a couple of years.
I felt like there was like a period of like five to six years where it wasn't very exciting in tech.
Uh and then in the last three years, it's just been the most insanely exciting, energizing period uh of my career.
Uh and I think the next two to three years gonna be a continuation of that.
And so uh would encourage people to not take it for granted.
I'm trying to not take it for granted.
At some point, you know, this wave's gonna play out and it's gonna be a lot more, you know, incremental.
Uh, but in the meantime, we're gonna 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 uh that's the main thing I'd I'd leave folks with.
I love this message.
I want to spend a little more time on it.
Um, when you say don't miss it, is 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.
Yeah, I would just say engage with it.
So it's basically like what you said.
Um, lean in, um, building uh tools on top of this is is part of the, you know, it's part of the story.
Um, just using the tools, like you don't, you know, you don't need to be a software engineer to lean into this.
Um, all I think a lot of jobs are gonna gonna gonna 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 um as the models improve.
And yeah, and so it's basically like getting used and get getting used to the technology and getting familiar with it instead of kind of like laying back and uh uh uh 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 gotta learn a cloud bot this week.
Oh god.
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 stay on top of news?
What are some things you've done learned?
Yeah, so I think I'm personally a bad example of this because I am I'm basically chronically online uh on X and uh our company Slack.
So I I I actually try and absorb, I end up absorbing a lot of it.
What I will say though, it's just like from observing other folks who are less you know addicted to this stuff like I am.
Um 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 like going to 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 um uh uh yeah, this insane like pace of of news, which is honestly very overwhelming.
Uh 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 cli and play around with it.
Install Chat GPT and connect it to a couple of your uh you know internal uh uh data sources, Notion, Slack, GitHub, and see what it can and cannot do.
Um, all of that I think is uh uh a part of it.
Amazing.
Sherwin, with that, we 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 in one fiction book.
Uh the fiction book was I just finished reading it.
I I it was really, I really recommend it.
It it's uh uh There is no anti mimetics division by QNTM.
Uh he's a uh I think he's like an online author, but I saw it being shared on X.
Uh, this this uh it's like a science fiction y kind of book.
Um and it was I basically devoured it in like two days.
Um 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.
Um and so it's just a very like smart, like creative book that that and fresh, uh honestly, in terms of like source material uh that that I really like.
So I'd recommend that one.
Uh the book is also unintentionally hilarious.
So like it's like meant to be like this like sci-fi almost like horror style book, but it was it was it was uh it made me laugh a couple times.
So uh that's the that's the um fiction book.
Nonfiction, so I'm gonna cheat and I'm I'm gonna recommend two of them.
So in the last year, I've been reading a lot more about China and kind of like the US China relations.
Uh and I think there are two books that came out in the last year that have been you know really, really eye-opening for me in 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, uh, and their pros and cons to each.
I read it and I was like, hmm, yeah, it does does seem like we're run by lawyers uh in the US.
Uh so I think that's one.
Uh and the other one is the Patrick McGee book on Apple and China was super, super interesting.
I'm a huge Apple fanboy.
Like if you could see my uh 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 um also, you know, very, very timely, uh timely book as well.
The anti mimetics book sounds amazing.
I'm buying it right now as you're talking.
Yeah.
Yeah, yeah.
It's it's like, I think it's only like a couple hundred pages.
I literally finished it in two dreams.
It was just like so, so good.
Okay, great tip.
Okay.
Uh, favorite recent movie or TV show you have really enjoyed.
Yeah, that one's tough because you know, with I have two kids and uh uh a busy job, and so I really haven't had much time um to watch TV shows.
Uh I will say in the last couple of weeks, I watched a couple episodes.
I'm actually a big anime guy.
And so uh I watched a couple episodes.
There's a new season of this anime called Jujutsu Kaisen uh that's out.
Uh so season three of JJK uh was was really good.
Um in general, uh I'm a huge uh fan of uh Japanese anime.
I think they create the most uh novel and unique uh plots uh in universes that uh Western media has shied away from.
Um and so uh generally a big fan of that.
But yeah, it haven't really watched much, but saw a couple episodes with JJK recently.
Extremely understandable in your role.
Yeah.
Favorite product you recently discovered that you really love.
Yeah, okay.
So so um so I recently uh had to set up a Wi-Fi and like home networking, and I went all in on Ubiquity uh routers um and security cameras.
Uh I'd never heard of it before I had had to do this.
I always just had a very simple setup.
Uh and it is just such a well-built product.
Uh, I don't know if you used it before, but it's basically like the Apple of like home networking.
So uh beautiful products.
Uh, but the thing that actually makes it extremely good is its software is good.
Uh and so they have a really great um mobile app to help manage, you know, uh all of the the home networking.
Um and so basically uh ubiquity, you can use it to buy uh wireless routers.
Um you need Ethernet uh wiring throughout your house to use it.
Um, but I actually think what makes it really good are security cameras.
So if you have security cameras that are plugged into the Ubiquity 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 uh they're they're they're a little pricey, but not that pricey.
Uh, but it's been just an incredible product experience.
All right.
I went Eero, so I made a mistake.
Good tip.
Euros are pretty good too, but uh I'm not ubiquity.
Fully converted to ubiquity at this point.
Okay, good tip.
Okay, two more questions.
Do you have a favorite life motto that you find yourself going back to in work or in life?
Yeah, uh the one that I always you know repeat to myself is uh uh never feel sorry for yourself.
There's a lot of things that are gonna happen, you know, uh at work, uh in life, uh, 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 um also something that I repeat to to uh a lot of other folks as well.
Last question.
So in your previous life, you worked at Open Door, where you led work on basically figuring out how much to uh 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?
There's a bunch that were surprising.
I'll I'll maybe list the the couple of most uh uh interesting ones.
Um power lines and like uh high voltage power lines like are super super uh 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 was like buzzing, and most people have families, you don't want your kids kind of near there.
Uh so I think that was one that really really uh kind of surprised me.
That makes sense.
Yeah.
And then the other one, which uh was something that uh was always something really difficult for us to uh quantify uh was floor plans.
Uh 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.
Like we were doing all these things of like how wide is the kitchen and like is it uh 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 uh ops team would go in and be like, yeah, it's 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.
Uh so yeah, those those are ones that were uh surprising.
And then the last one that was more impactful than I thought is um general like curb appeal and like even like the front door.
Uh and so I actually think there's a Zillow book on on this where um the front door replacement tends to be the highest ROI uh for homes.
Um, 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 uh I'd underrated its importance.
That is extremely interesting.
Uh and I love that you had to figure figure out how to do all this uh in code and that while you're gonna do it.
Yeah, yeah, and then floor plans.
I have a bunch of stories around like for floor plans.
There's like there's like uh it's not digitized, so there's like a handful of people who have like paper floor plans uh of like all these homes in like Phoenix and Dallas.
Um yeah, a lot, a lot of fun, fun stories from the open door days.
Okay, Sherwin, uh thank you so much for doing this.
This was incredible.
Uh, where can folks find you online?
And uh and how can listeners be useful to you?
Yeah, so I'm uh online on on Twitter on X.
I'm just at Sherwin Woo.
And uh yeah, I mostly just tweet about uh OpenAI and the API and some of the products that we're launching.
Uh and then how folks can be inter can be useful to me.
Uh 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 uh uh just reach out to me on X.
Um, I would love to hear about uh 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.
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