Lenny's Podcast: Product | Career | Growth｜Inside ChatGPT: The fastest growing product in history | Nick Turley (Head of ChatGPT at OpenAI)

You were a product leader at Dropbox, then Instacart.

Now you're the PM of the most consequential product in history.

I didn't know what I would do here because it was a research lab.

My first task was to fix the blinds or something like that.

When someone offers you a rocket ship, don't ask which seat.

We set out to build a super assistant. It was supposed to be a hackathon code base.

What was it called before? It was going to be Chat with GBD 3.5 because we really didn't think it was going to be a successful product.

And then Sam Altman's just like, hey, let me tweet about it.

This is a pattern with AI. You won't know what to polish until after you ship.

My dream is that we'd ship daily. By the time people hear this, they're going to have their hands on GPT-5.

About 10% of the world population uses it every week.

With scale comes responsibility. It just feels a little bit more alive, a bit more human.

This model has taste. Kevin Wheel, your CPO, said to ask you about this principle of, is it maximally accelerated?

I just really want to jump to the punchline.

Why can't we do this now? I always felt like part of my role here is to set the pace and the resting heartbeat.

Everyone's always wondering, is chat the future of all of this stuff?

Chat was the simplest way to ship at the time.

I'm baffled by how much it took off. I'm even more baffled by how many people have copied.

ChatGPT is now driving more traffic to my newsletter than Twitter.

That is a type of capability that has been incredibly retentive.

I've been really excited about what we've been doing in search.

Can you just peek into where this goes long term?

ChatGPT feels a little bit like MS-DOS. We haven't built Windows yet, and it will be obvious once we do.

Today, my guest is Nick Turley. Nick is head of ChatGPT at OpenAI.

He joined the company three years ago when it was still primarily a research lab.

He helped come up with the idea of ChatGPT and took it from zero to over 700 million weekly active users billions in revenue, and arguably the most successful and impactful consumer software product in human history.

Nick is incredible. He's been very much under the radar.

This is the first major podcast interview that he has ever done, and you are in for a treat.

We talk about all the things, including the just launched GPT-5.

A huge thank you to Kevin Wheel, Claire Vo, George O'Brien, Joanne Zheng, and Peter Deng for suggesting topics for this conversation.

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With that, I bring you Nick Turley. This episode is brought to you by Orcus, the company behind open source conductor, the orchestration platform powering modern enterprise apps and agentic workflows.

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That's O-R-K-E-S dot I-O slash lenny. This episode is brought to you by Vanta, and I am very excited to have Christina Cassioppo, CEO and co-founder of Vanta, joining me for this very short conversation.

Great to be here. Big fan of the podcast and the newsletter.

Vanta is a longtime sponsor of the show, but for some of our newer listeners, what does Vanta do and who is it for?

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Today, we currently help over 9,000 companies, including some startup household names like Atlassian, Ramp, and Langchain. start and scale their security programs and ultimately build trust by automating compliance, centralizing GRC and accelerating security reviews.

That is awesome. I know from experience that these things take a lot of time and a lot of resources and nobody wants to spend time doing this.

That is very much our experience, but before the company and some extent during it.

But the idea is with automation, with AI, with software, we are helping customers build trust with prospects and customers in an efficient way.

And, you know, our joke, we started this compliance company, so you don't have to.

We appreciate you for doing that. And you have a special discount for listeners.

They can get $1,000 off Vanta at vanta.com slash Lenny.

That's V-A-N-T-A dot com slash Lenny for $1,000 off Vanta.

Thanks for that, Christina. Thank you. Nick, thank you so much for joining me and welcome to the podcast.

Thanks for having me, Lenny. I already had a billion questions I wanted to ask you, and then you guys decided to launch DPT5 the week that we're recording this, so now I have at least two billion questions for you.

I hope you have a lot of time. First of all, just congrats on the launch.

It's coming tomorrow, the day after recording this.

Just congrats. How are you feeling? I imagine this is an ungodly amount of work and stress.

How are you doing? It's a busy week, but we've been working on this for a while, so it also feels really good to get it up.

So by the time people hear this, they're going to have their hands on GPT-5 and the newest ChatGPT.

What's the simplest way to just understand what this is, what it unlocks, what people can do with it?

Give us kind of the pitch. I'm so excited about GPT-5.

I think for most people, it's going to feel like a real step change.

If you're the average ChatGPT user, and we have 700 million of them this week, you've probably been on GPT-4.0 for a while.

You probably don't even think about the model that powers the product.

And GPT-5, it just feels categorically different.

I'll talk about a lot of the specifics, but at the end of the day, the vibes are good.

At least we feel that way. We hope that users feel the same.

And increasingly, that is the thing that I think most people notice.

They don't look at the academic benchmarks they don't look at. evaluations, they try the model and see what it feels like.

And just on that dimension alone, I'm so excited.

I've been using it for a while. But it is also the smartest, most useful, and fastest frontier model that we've ever launched.

On pure smarts, one way to look at that is academic benchmarks.

On many of the standard ones, whether or not it's math or reasoning or just raw intelligence, this model is state-of-the-art.

I'm especially excited about its performance on coding, whether or not that's SWE bench, which is a common benchmark, or actually front-end coding is really, really good as well.

And that's an area where I feel like there's a true step change improvement in GPT-5.

But really, no matter how you sort of measure the smarts, it's quite remarkable.

And I think people are going to feel the upgrade, especially if they weren't using O3 already.

And the second thing, beyond smarts, is it's just really useful.

Coding is one axis of utility, whether or not you have coding questions or you're vibe coding an app.

But it's also a really good writer. I write for a living internally, externally.

I just wrote a big blog post that we published Monday.

And this thing is such an incredible editor.

And compared to some of the older models, it's got taste, which I think is really exciting.

And to me, that's something that is truly useful in my day-to-day.

And there's a bunch of other areas. It's state-of-the-art on health, which is useful when you need it.

But again, the thing you can't really express in use cases or data is the vibe of the model.

And it just feels a little bit more alive, a bit more human, in a way that is kind of hard to articulate until you try it.

Feel good about that. And yeah, as mentioned, it's faster.

It thinks too, just like O3 did. But you don't have to manually tell it to do that.

It'll just dynamically decide to think when it needs to.

And when it doesn't need to think, it just responds instantly.

And that ends up feeling quite a bit faster than using O3 did.

And then, you know, maybe the thing that's most exciting is that we're making it available for free.

And that's like one of those things that I feel like we can uniquely do at OpenAI because, you know, many companies, I think, if they have a subscription model like us, they would gate it behind their paid plan.

And for us, you know, if we can scale it, we will.

And that just feels awesome. We did that with 4.0 as well.

So everyone's going to be able to try GPT-5 tomorrow, hopefully.

How long does something like this take? Like, I don't know if there's a simple answer to this, but just how long have you guys been working on GPT-5?

We've been working on it for a while. You can kind of view GPT-5 as a culmination of a bunch of different efforts.

We had a reasoning tech. We had a more classic post-screening methodologies.

And therefore, it's really hard to put a beginning on it.

But it really is kind of the end point of a bunch of different techniques that we've been working on for a while.

Can you give us a peek into the vision for where JAT-GPT is going, GPT in general is going?

If you look at it on the surface, it's been kind of the same idea with a much smarter brain for a long time.

I'm curious where this goes long term. So to maybe back up a bit, Now you think of Chachapiti as this kind of ubiquitous product.

Again, about 10% of the world population uses it every week.

I think we have like 5 million business customers now.

It's like an established category in its own right.

But really, when we started, We set out to build a super assistant.

That's how we talked about it at the time.

In fact, the code base that we use is called SA Server.

It was supposed to be a hackathon code base, but things always turn out a little bit differently.

And so in some ways, That is still division.

The reason I don't talk about it more than I, you know, do is because I think assistant is a bit limiting in terms of the mental model we're trying to create.

You think of this like very personified human thing, maybe utilitarian, maybe, you know.

And frankly, you know, having an assistant is not particularly relatable to most people unless they're like in Silicon Valley and they're a manager or something like that.

So it's imperfect. But like really what, you know. we envision is this entity that can help you with any task, whether or not that's at home or at work or at school, really any context.

And it's an entity that knows what you're trying to achieve.

So unlike Chatubi today, you don't have to describe your problem in minute detail, because it already stands your overarching goals and has context on your life, et cetera.

So that's one thing that we're really excited about.

The sort of inverse of giving it more inputs on your life is giving it more action space.

So we're really excited to allow it to do, over time, what a smart, empathetic human with a computer could do for you.

And I think the limit of the types of problems that you can solve for people once you give it access to tools like that is very, very different than what you might be able to do in a chatbot today.

So that's more outputs. And I often think, OK, I'm a general intelligence.

What would happen if I became Lenny's intern or something?

And I wouldn't be particularly effective despite having both of those attributes that I just mentioned.

And it's because I think this idea of Building a relationship with this technology is also incredibly important.

So that's maybe the third piece that I'm excited about is building a product that can truly get to know you over time.

And you saw us launch some of those things with improved memory earlier this year.

And that's just the beginning of what we're hoping to do so that it really feels like it's your AI.

So I don't know if Super Assistant is still the right exact analogy, but I think people just think of it as their AI.

And I think we can put one in everyone's pocket. and help them solve real problems, whether or not that's becoming healthy, whether or not that's starting a business, whether or not that's just having a second opinion on anything.

There's so many different problems that you can help with people in their daily life, and that's what motivates me.

So an interesting kind of between the lines that I'm reading here is the vision is for it to be an assistant for people, not to replace people.

It feels like a really important piece of the puzzle.

Maybe just talk about that. AI is really scary to people.

And I understand, you know, there's decades of movies on AI that have a certain mental model kind of baked in.

And even if you just look at the technology today, everyone I think has this moment where the AI does something that was really deeply personal to them and you're like, Kind of thought, hey, I can never do that.

For me, it was like weird music theory things where I was like, wow, this thing actually understands music better than I do.

And that's something I'm passionate about.

And so it's naturally scary. And I think the thing that's been really important to us for a long time is to build something that feels like it's helpful to you, but you're in the driver's seat.

And that's even more important as the stuff becomes agentic, right?

Like the feeling of being in control. And that can be small things like, you know, we built this way of sort of watching what the AI is doing when it's in agent mode.

It's not that you actually are going to watch it the whole time, but it gives you a mental model and makes you feel in control.

In the same way that when you're in a Waymo, you get that screen, for those of you who've tried Waymo, you can see the other cars.

It's not like you're going to actually watch, but it gives you the sense that you know how this thing works and what's happening.

Or we always check with you to confirm things.

It's a little bit annoying, but it puts you in the driver's seat, which is important.

And for that reason, we always view technology and the technology that we build as something that amplifies what you're capable of rather than replacing it.

And that becomes important as the tech gets more powerful.

Okay, so you mentioned the beginnings of ChatGPT.

I was reading in a different interview. So you joined OpenAI.

ChatGPT was kind of just this internal experimental project that was basically a way to test GPT 3.5.

And then Sam Altman's just like, hey, let me tweet about it.

Maybe see if people find this interesting.

Yada, yada, yada. It's the most successful consumer product in history.

I think both in growth rate and users and revenue and just absurd.

Can you give us a glimpse into that early period before it became something everyone's obsessed with?

Yeah. So we had decided that we wanted to do something consumer-facing, I think, right around the time that GPT-4 finished training.

And it was actually mainly for a couple of reasons.

We already had a product out there, which was our developer product.

That's actually what I came in. to help with initially.

And that has been amazing for the mission.

In fact, it's grown up in how it's the open AI platform with, I don't know, four million developers, I think.

But at the time, it was early stage, and we were running into some constraints with it because there was two problems.

One, you couldn't iterate very quickly because every time you would change the model, you would break everyone's app.

So it was really hard to try things. And then the other thing was that it was really hard to learn Because the feedback we would get was like the feedback from the end user to the developer to us.

So it was very disintermediated. And we were excited to make fast progress towards AGI.

And it just felt like we needed a more direct relationship with consumers.

So we were trying to figure out where to start.

And, you know, in classic OpenAI fashion, especially back then, we put together a hackathon of enthusiasts of just hacking on GPT-4 to kind of see what awesome stuff we could create and maybe ship to users.

And everyone's idea was some flavor of a super assistant.

Like they were more specific ideas. Like we had a meeting bot that would call into meetings and, you know, the vision was, you know, maybe we would like help, it will help you run the meeting over time.

We had a coding tool, which, you know, full circle now, probably ahead of its time.

And the challenge was that we tested those things, but every time we tested these more bespoke ideas, people wanted to use it for all this other stuff because it's just a very, very generically powerful technology.

So after a couple of months of prototyping, we took that same kind of crew of volunteers, and it was truly a volunteer group, right?

We had someone from the supercomputing team who had built an iOS app before.

We had someone on the research team who had written some backend code in their life.

They were all part of this initial ChatGPT team And we decided to ship something open-ended because we just wanted a real use case distribution.

And this is a pattern with AI, I think, where you really have to ship to understand what is even possible and what people want, rather than being able to reason about that a priori.

So ChatGPT came together at the end because we just wanted the learnings as soon as we could.

And we shipped it right before the holiday, thinking we would sort of come back and get the data and then wind it down.

And obviously that part turned out super differently because People really liked the product as is.

So I remember sort of going through the motions of like, oh, man, dashboard's broken.

Oh, wait, people are liking it. I'm sure it's just, you know, going viral and stuff is going to die down to like, oh, wow, people are retaining, but I don't understand why.

And then eventually we kind of like, you know, fell into product development mode, but it was a little bit by accident.

Wow. I did not know that ChatGPT emerged out of a hackathon project.

Definitely the most successful hackathon project.

I like to tell the story when we do our hackathons because I really do want people to feel like they can ship their idea, and it's certainly been true in the past, and we'll continue to make it true.

If you don't want to share these things, but I wonder who that team was.

The team's largely still around. Some of the researchers working on GPT-5, actually, were always part of the chat GPT team.

Engineers are still around. Designers are still around.

I'm still here, I guess. So, yeah, you've got the team still running things, but obviously we've grown up tremendously and we've had to because, you know, with scale comes responsibility and, you know, we're going to hit a billion users soon and you kind of have to begin acting in a way that is appropriate to that scale.

Okay, so let me spend a little time there.

So I don't know if this is 100% true, but I believe it is that ChatGPT is the fastest growing, most successful consumer product in history.

Also the most impactful on people's lives.

It feels like it's just part of the ether of society now.

It's just my wife talks to it. Like every question I have, I go to it, voice mode.

My wife's just like, let me check with ChatGPT.

It's just such a part of our life now. And And I think it's still early.

So many people don't even know what the hell is going on.

Just as someone leading this, how does just... Do you ever just take a moment to reflect and think about just like, holy shit.

I have to. It's... quite humbling to get to run a product like that.

I have to binge myself very frequently. I also have to sometimes sit back and just think, which is really hard when things are moving so quickly.

I love setting a fast pace at the company, but in order to do that with confidence, I need at least one day every week that I'm entirely unplugged and I'm just thinking about what to do and process the week, etc.

The other thing is I've never ever worked on a product that is so empirical in its nature where if you don't stop and watch and listen to what people are doing, you're gonna miss so much, like both on the utility and on the risks actually, because normally, you know, by the time you ship a product, you know what it's going to do.

You don't know if people are gonna like it.

That's always empirical, but you know what it can do.

And with AI, because I think so much of it is emergent, you actually really need to stop and listen after you launch something and then iterate on the things people are trying to do and on the things that aren't quite working yet.

So for that reason alone, I think it's very important to take a break and just watch what's going on.

Okay, so you take a day off every week. Not off.

Okay, that's not the right way to put it.

You take a day of thinking time, deep work.

I need it. Yeah, yeah, yeah. And I need to hard unplug, you know, on a Saturday or something like that.

On a Saturday. But, you know, it's just not possible otherwise.

This has been a giant marathon for three years now.

Like a sprint marathon. Sprint marathon, that's right.

Or interval training or something. I don't know how to exactly describe the open air launch cadence, but, you know, you've got to set yourself up in a way that is sustainable.

Even if this wasn't AI and it didn't have the interesting attributes that I just mentioned, I think you would need to do that.

But especially with AI, it's important. So along those lines, I talked to a bunch of people that work with you that work at OpenAI.

Joanne specifically said that urgency and pace are a big part of how you operate, that that's just something you find really important to create urgency within the team constantly.

Even when you are the fastest growing product in history, growing like crazy, talk about just your philosophy on the importance of pace and urgency on teams.

Well, it's nice of her to say that. You know, I spent a lot of...

Two things. With ChatGPT, when we decided to do it, we had been prototyping for so long.

And I was just like, in 10 days, we're going to ship this thing.

And we did. So that was maybe a moment in time thing where I just really wanted to make sure that we go learn something.

Ever since then, I just spent so much time thinking about why ChatGPT became successful in the first place.

And I think there was some element of just doing things where there was many other companies that had technology in the LLM space that just never got shipped.

And I just... felt like you know of all the things we could optimize for learning as fast as possible is incredibly important so i just started rallying people around that and that took different forms like for a while when we were of that size i just ran this like you know daily release sync and i had everyone who was required to make a decision in it and we would just talk about what to do and to pivot from yesterday etc obviously at some point that doesn't scale but i always felt like part of my role here obviously was like to think about you know the direction of the product, but also to just set the pace and the resting heartbeat for our teams.

And again, this is important anywhere, but it's especially important when the only way to find out what people like and what's valuable is to bring it into the external world.

So for that reason, I think it's become a superpower of OpenAI.

And I'm glad that Joanne thinks I had some part in that, but it really has taken the village.

I love this phrase, the resting heart rate of your team.

That's such a perfect metaphor of just the pace being equivalent to your resting heart rate.

I actually learned that at Instacart when I went to shoot up there because we were in the pandemic and it was kind of all hands on deck.

For a while, there was this like, I think there was a company-wide stand-up because we disbanded all teams.

We were just trying to keep the site up.

And for me, You know, I had been used to kind of taking my sweet time and just thinking really hard about things, and that's important, but I really learned to hustle over there, and I think that's come in handy at OpenAAM.

Okay, so along these same lines, I asked Kevin Wheel, your CPO, what to ask you, and he said to ask you about this principle of, is it maximally accelerated?

Talk about that. That's funny. We have a slack emoji apparently for this now because I used to say that.

Now I try to like paraphrase. Sometimes I just really want to jump to the punchline of like, okay, why can't we do this now?

Or why can't we do it tomorrow? And I think that it's a good way to cut through a huge number of blockers with the team and just instill, especially if you come from a larger company.

At some point, we started hiring people from larger tech companies.

I think they're used to, let's check in on this in a week or let's circle back next quarter to see if we can go on the plan.

And I just kind of as a thought exercise, I was like people asking like, okay, if like this was the most important thing and you wanted to truly maximally accelerate it, what would you do?

That doesn't mean that you go do that, but it's really a good forcing function for understanding what's critical path versus what, you know, can happen later.

And I've just always felt like execution is incredibly important.

These ideas, they're everywhere. Everyone's talking about a personal AI.

You might have seen news on that. And I really think that execution is one of the most important things in this space, and this is a tool.

So it's funny that that became a meme. It's like a little pink Slack emoji that people just put on whatever they're trying to force the question.

I was going to ask what the emoji was. So it's a little pink.

Is there something in there? It's a Comic Sans emoji that says, is this maximally accelerated?

And so the culture there is when someone is working on something, the question, the push is, is this maximally accelerated?

Is there a way we can do this faster? Is there anything we can unblock?

Yeah, and we use that sparingly, right? Because it needs to be appropriate to the context.

There's some things where you don't want to accelerate as quickly as possible because you kind of want process.

And we're very, very deliberate on that, where your process is a tool.

And one of the areas where we have an immense amount of process is safety.

Because A, the stakes are already really high, especially with these models, GPT-5 pushes the frontier in so many different ways.

But B, if you believe in the exponential, which I do, and most people who work on this stuff do, you have to play practice for a time where you really, really need the process for sure, sure, sure.

And that's why I think it's been really important to separate out the product development velocity, which has to be super high, from, okay, for things like frontier models, there actually needs to be a rigorous process where you red team, you work on the system card, you get external input, and then you put things out with confidence that it's gone through the right safeguards.

So again, it's a nuanced concept, but I found it very, very useful when we needed it.

For everything product development, you're dead on arrival, so it's important to get stuff out.

We got to open source this meme so that other teams can build on this approach.

Absolutely. So interestingly with ChatGPT, and it's not a surprise, but not only is it the fastest growing, most successful consumer product ever, retention is also incredibly high.

People have shared these stats that one month retention is something like 90%.

Six month retention is something like 80%.

First of all, are these numbers accurate?

Can you share that? I'm obviously limited on what exactly I can share.

But it is true that our retention numbers are really exciting.

And that is actually the thing we look at.

We don't care at all how much time you spend on the product.

In fact, our incentive is just to solve your problem.

And if you really like the product, you'll subscribe.

But there's no incentive to keep you in the product for long, but we are obviously really, really happy if, you know, over the long run, you know, three-month period, et cetera, you're still using this thing.

And for me, this was always the elephant in the room early on.

It's like, hey, this may be a really cool product, but, you know, is this really the type of thing that you come back to?

And it's been incredible to not just see strong retention numbers, but to see, you know, an improvement in retention over time, even as our cohorts become, you know, less of an early adopter and more, you know, the average person.

So... Yeah, so that note is something that I don't think people truly understand how rare this is.

When a product, the cohort of users comes, tries it out, and then retention over time goes down, and then it comes back up.

People come back to it a few months later and use it more.

It's called a smiling curve or smile curve, and that's extremely rare.

Yeah, yeah, yeah. There's some smiling going on just on the team.

And, you know, I feel like I have to acknowledge that some of it is not the product.

I think people are actually just getting used to this technology in a really interesting way where I find, and this is why the product needs to evolve too, that this idea of delegating to an AI, it's not natural to most people.

It's not like you're going through your life and figuring out what can I delegate.

Certain sphere of Silicon Valley does that, you know, because they're in like a self-optimization mode and they're trying to delegate everything they can.

But I think for most people in the world, it's actually quite unnatural and you really have to learn, okay, what are my goals actually and what could another intelligence help me with?

And I think that just takes time, and people do figure it out once they've had enough time with the product.

But then, of course, there's been tons of things that we've done in the product, too, whether or not it's making the core models better, whether or not it's new capabilities like search and personalization and all that kind of stuff, or just standard growth work, too, which we're starting to do.

That stuff matters, too, of course. So you might be answering this question already, but let me just ask it directly.

People may look at this and be like, okay, they're building this kind of layer on top of this godlike intelligence.

Of course, it will grow incredibly fast and retention will be incredible.

What the heck does, what are you guys actually doing that sits on top of the model that makes it grow so fast and retain so much?

Is there something that has worked incredibly well that has moved metrics significantly that you can share?

I mean, one thing we've learned, I'll answer that question in a minute, but one thing we've learned with ChatGPT is that there really is no distinction between the model and the product.

The model is the product, and therefore you need to iterate on it like a product.

And by that I mean, obviously, you typically start by shipping something very open-ended, at least if you're open AI.

That's kind of a playbook. But then you really have to look at what are people trying to do.

Okay, they're trying to write. They're trying to code.

They're trying to get advice. They're trying to get recommendations.

And you need to systematically improve on those use cases.

And that is pretty similar to product development work.

Obviously, the methodology is a bit different.

But the discovery is the same. You've got to talk to people.

You've got to do data science. And you've got to try stuff and get feedback.

So that's like one chunk of work that we've been very consciously doing is improving the model on the use cases people care about.

And there's also such a thing as vibes, because I'm sure you know, and that's one of the things that I'm excited about in GPT-5 is that the vibes are really good.

So that, too, is, you know, we have a model behavior team, and they really focus on, you know, what is the personality of this model and how does it speak and talk.

So there's that kind of work. I would say that's maybe, you know... a third of the retention improvements that we see or so, just roughly.

And then I think another third is what I would call sort of product research capabilities.

They're research driven for sure. They have a research component, but they're really new product features or capabilities.

And like search is one example of that, where if you remember in the olden days, AKA like, you know, maybe 20 months ago or something, you would talk to chat to PD and it'd be like, you know, as of my knowledge cut off, or I can't answer that because that happened too recently or something like that.

And, you know, that is a type of capability that has been incredibly retentive.

And for good reason, it just allows you to do more with the product.

Personalization, like this idea of advanced memory, where things can really get to know you over time is another example of a capability like that.

You know, I think that's another good chunk.

And then the third stuff is the stuff you would do in any product, and those things exist too.

Not having to log in was a huge hit because it removed a ton of the friction.

I think we had this intuition from the beginning, but we never got to it because we didn't have enough GPU or... you know, other, other constraint to really, really, really go do that.

So, you know, there's the like kind of traditional product work too.

So I often think about it sort of as roughly a third, a third, a third, but really, you know, we're still learning and we're planning to evolve the product a ton, which is why I'm sure there's going to be new levers.

You mentioned something that I want to come back to real quick.

You said that it was something like 10 days from hackathon to Sam tweeting about ChatGPT being live.

You know, the hackathon happened much earlier and we were prototyping for a long time, but at some point we basically ran out of patience on trying to build something more bespoke.

And again, that was mostly because people always wanted to do all this other stuff whenever we tested it.

So it was 10 days from when we decided we were going to ship to when we shipped.

And You know, the research we'd been testing for a long time, it was kind of an evolution of what we'd called instruction following, which was the idea that, you know, instead of just completing the sentence, these models could actually follow your instructions.

So if you said, summarize this, it would actually do so.

And the research had evolved from that into a chat format where we could do it multi-turn.

So that research took way longer than 10 days and that kind of baking in the background.

But the productization of this thing was very, very fast.

And lots of things didn't make it in. I remember we didn't have history, which of course was the first user feedback we got.

The model had a bunch of shortcomings. And it was so cool to be able to iterate on the model.

The thing I just talked about, treating the model as a product, was not a thing before chat shipping, because we would ship it more like hardware, where there'd be a... a release like GPT-3 and then we would start working on GPT-4 and these were giant big spend R&D projects that would take a really long time and you kind of, the spec was whatever the spec was and then you'd have to wait another year.

And ChatGPT really broke that down because we were able to make iterative improvements to it just like software.

And really, my dream is that it would be amazing if we could just ship daily or even hourly, like in software land, because you could just fix stuff, et cetera.

But there's, of course, all kinds of challenges in how you do that while keeping the personality intact, while not regressing other capabilities.

So it's an open field to get there. That's such a good example of, is it maximally accelerated?

Okay, we're going to ship ChatGPT. Okay, 10 days.

Holy moly. We've been talking about ChatGPT.

Clearly, it's kind of a chat interface. Everyone's always wondering, is chat the future of all of this stuff?

Interestingly, Kevin Wheel made this really profound point that has always stuck with me when he was on the podcast that...

Chat is actually a genius interface for building on a super intelligence because it's how we interact with humans of all variety of intelligence.

It scales from someone at the lower end to a super smart person.

And so it's really valuable as a way to kind of scale the spectrum.

Maybe just talk about that and just is chat the long-term interface for ChatGPT?

I guess it's called ChatGPT. I feel like we should either drop the chat or drop the GPT at some point because it is a mouthful.

We're stuck with the name. But no matter what we do with that, the product will evolve.

I think that... I agree that there's something profound about natural language.

It just really is the most natural form of communicating to humans, and therefore it feels important that you should be communicating with your software in natural language.

I think that's different from chat, though.

I think chat was the simplest way to ship at the time.

I'm baffled by how much it took off as a concept.

I'm even more baffled by how many people have copied the paradigm rather than trying out a different way of interacting with AI.

I'm still hoping that will happen. So I think natural language is here to stay, but this idea that it has to be a turn-by-turn chat interaction, I think, is really limiting.

And this is one of the reasons I don't love the super-assistant analogy, even though we used to always use it, is because if you think that way, then you kind of feel like you're talking to a person.

But, you know, and GPT-5 is amazing at... making great front-end applications.

So I don't see a reason why you wouldn't have, you know, AIs that, you know, can render their own UI in some way.

And you obviously want to make that predictable and feel good.

But it feels limiting to me to think of the end-all, be-all interface as a chatbot.

It actually kind of feels dystopian almost where, like, I don't want to use all my software through the proxy of some interface.

Like, I love being in Figma. I love being in, you know, Google Docs.

Those are all great products to me and they're not chatbots.

So... Yes, on natural language, but no on chat is where I would describe my point of view.

And I'm just hoping in general that we see more sort of consumer innovation on how people interact with AI.

Because there's so many possibilities and you just got to try stuff.

That's why chat stuck is like, you know, we just did it and people liked it.

So I'm hoping that we see more there and we'll try to do our part.

So you mentioned that you kind of like got stuck with this name ChatGPT.

Maybe this is part of the answer, but I'm curious just are there any accidental decisions you guys made early on that have stuck and have essentially become history changing?

There's so many and it's funny because you have no time to think about them, and then they end up being super consequential.

The day was one. We went from chat with GPT 3.5 to chat GPT the night before.

Slightly better, but still really bad. What was it called before?

It was going to be chat with GPT 3.5. Because we really didn't think it was going to be a successful product.

We were trying to actually be as nerdy as we could about it, because that's really what it was.

It was a research demo, not a product. So we didn't think that was bad.

But I think that in the original release, making it free was a big deal.

I don't think we appreciate that because the GPT 3.5 model was in our API for at least six months prior to that.

I think anyone could have built something like this.

It might not have been quite as good on the modeling side, but I think it would have taken off.

So making it free and putting a nice UI on it, very consequential in the way that you take for granted now.

And this is why I think that A, distribution, and B, the interface are continuously important even in 2025.

The paid business, which now it's a giant business, both in the consumer space and in the enterprise space, the birth of that was just to turn away demand originally.

It was not like we brainstormed, oh, what is the best monetization model for AI?

It was really what monetization model or what mechanism would allow us to turn away people who are less serious than the people who are really trying to use it?

And subscriptions just happen to have that property in it. grew into a large business.

Yeah, I think shipping really kind of funky capabilities before they were polished is another thing where, you know, that feels like a tactical decision, but it became a playbook because we would learn so much.

Like, remember when we shipped Code Interpreter, we learned so much after we shipped it.

You know, now it's known as, I think, data analysis and chat GPT or something like that, just because we actually got real-world use cases back that we could then optimize.

So I think there's been like a lot of decisions over time that proved pretty consequential, but we made them very, very quickly as we have to.

Yeah. The $20 a month feels like an important part of this.

Feels like everybody's just doing that now.

That would actually, I remember I had this like kind of panic attack because we really needed to launch subscriptions because at the time we were taking the product down every time.

I don't know if you remember, we had this like fail whale.

There was like a little E3 generated poem on it.

So we had to get this out and I remember calling up someone I greatly respect who's like, you know, incredible at pricing.

And I was like, what should I do? And we talked a bunch.

And I just ran out of time to incorporate most of that feedback.

So what I did do is ship a Google form to Discord with like, I think the four questions you're supposed to ask on how to price something.

Yeah, exactly. It literally had those four questions.

And I remember distinctly, A, you know, I got a price back.

And that's kind of how we got to $20. But B, the next morning, there was like a press article.

You won't believe the four genius questions the ChatGPT team asked to price their product.

It was like, if only you knew. So there's something about building in this extreme public where people interpret so much more intentionality into what you're doing than might have actually existed at the time.

But we got with the 20. We were debating something slightly higher at the time.

I often wonder what would have happened because so many other companies ended up copying the $20 price point.

So I'm like, did we erase a bunch of market cap by pricing it this way?

But ultimately, I don't care because the more accessible we can make this stuff, the better.

And I think this is the price point that in Western countries has been reasonable to a lot of people in terms of the value that they get back.

And most importantly, we're able to push things down to the free tier semi-regularly.

And we always do that when we can, including with G225.

So the survey, just to give you the official name, the Van Westendrop survey, is how you guys ended up pricing ChatGPT.

It was the top Google result. This was before ChatGPT had real-time information.

Otherwise, it could have maybe priced itself.

But it was Discord plus Google Forum plus a blog post on that methodology that got us there.

That is incredible. What a fun story. This is the survey that Rahul Vohra at Superhuman popularized in his first round article.

Yeah, yeah, yeah. That's right. That's right.

Definitely don't bring me on here as a pricing expert.

I think you have got better people for that.

Whether it was right or wrong, it is now the fastest growing insane revenue generating business in the world.

So I wouldn't feel too bad. No, it worked out.

Yeah, it worked out. And by the way, I'm on the 200 a month tier.

So there's clearly a room. Thank you. Thank you.

You know, the story of that one is interesting, too, because, you know, originally it The purpose of the Plus plan was to be able to ship first uptime and then be able to ship capabilities that we couldn't scale to everyone.

And at some point, we got so many people in the Plus tier that had just lost that property.

So the main reason we came up with the $200 tier is just we had so much incredible research that's actually really, really powerful. like, you know, O3 Pro or, you know, tomorrow GPT-5 Pro.

And just having a vehicle of shipping that to people who really, really care is exciting, even though it kind of violates the standard way a SaaS page should look.

It's like a little jarring to see the 10x jump.

So thank you for being a subscriber on that.

And thank you, everyone else who's watching, who's subscribed to any tier.

It's great. I'm just going to throw a fishing line into this pond of, are there any other stories like this?

You shared this incredible story of chat with GPT 3.5 being the original name, how you came up with pricing.

Is there anything else? Enterprise is an interesting one, too, because we've seen so much... incredible adoption in the enterprise.

And it's sort of objectively crazy to try to take on building a developer business and a consumer business and an enterprise business and, and, and all at once.

But, you know, the story there is in like month one, Or two, it was very clear that most of the usage was kind of worky usage.

Actually, much more than today, where you've got so many kind of consumers on the product.

And it's kind of sort of transcended into pop culture.

But at the time, it was like writing, coding, analysis, that kind of stuff.

And we were pretty quickly, organically, in like 90% of Fortune 500 companies in a way that I had seen maybe at Dropbox back when I was my... two jobs ago where we had a similar story.

And since then, there's been more PLG companies.

But the real reason we did enterprise, remember we were debating, should we do enterprise or should we launch an iOS app?

Because that's also all the team was. And the reason they did is we were starting to get banned in companies because they all felt rightfully or wrongfully that the privacy and deployment story, et cetera, wasn't there.

So I was just like, man, we have to do something.

We're going to miss out on a generational opportunity to build a work product.

And, you know, we've literally defined AGI as, you know, outperforming most humans at economically valuable work.

Or I'd probably butcher that. But, you know, I think that's the way we put it.

And so I feel like we had to be present there.

And it was a fairly, you know, Quick decision at the time, but it's grown into an immense business.

We just hit 5 million business subscribers up from three, I think, a month or two ago.

So it is kind of this spinoff that it's taking a life of its own that I'm really, really excited about for this reason.

That is a lot to be handling. The platform, essentially the API, the consumer product, the fastest growing, most successful product in history, and also the B2B side, which is clearly a massive business.

Do you have any kind of heuristics for how to make these trade-offs, do all this at once and stay sane and be successful?

It's a good question. First off, I don't run the developer stuff anymore.

We found someone way more competent to do that, and he's amazing.

So I still look after the various forms of chat, but luckily you don't have to make that trade-off.

OpenAI does, and I can get into that too, but it keeps me a little bit more sane.

I will say that there you kind of have to practice in two different ways when you're when you're building on this ai stuff one is sort of working backwards from the model capabilities and that is much more than science where i think you really need to look at what tech do we have available and what is like the most awesome way to product productize it and if you apply to some sort of pm framework to that i think you would do something horribly wrong because if you have tech that's you know For example, GPT-5 is really, really good at front-end coding now.

Like, I think that means you've got to reprioritize it.

You've got to actually bring that capability to life.

Maybe that's making ChatGPT better at vibe coding and rendering applications.

Maybe that's more like leveraging the taste of the model to make the UI more expressive.

There's a number of things we could do, right?

But you kind of have to replan and reprioritize, and that is more important than any particular audience segmentation.

It's really just looking at what is the magic thing we have and how do you make it shine.

Voice is a similar thing. It wasn't like...

Our customers need voice. They're begging for it or something like that.

It's like, wow, we figured out a way to make these things anything in and anything out.

What is a creative, awesome way to productize that?

And then we can see what people do. So I think that's one chunk of it.

But then the other chunk of it really is more like classic product management where you need to listen to customers.

And then when your customers are really different, that can be confusing because ChatGPT is a very general purpose product.

We see, when you look at end users, there's actually an immense amount of overlap in terms of what they want.

Primitives like projects or history search or sharing and collaboration, all those kind of things, they are actually... very, very present, whether or not you're talking to people at work or you're talking to people at home and school.

There's slightly different mechanics sometimes, but they're largely similar investments that I think we can get a lot of mileage out of.

And then there's enterprise specific work that we just have to do.

Like you've got to do HIPAA, you've got to do SOC 2, you've got to do all those things if you want to be a serious player.

And those are just non-negotiable. So it's complex as you correctly identified, but it's kind of the curse of working on a very open-ended and powerful, technology one analogy that someone at OpenAI who I really respect sometimes is like we're kind of like Disney where Disney has this like one kind of creative IP which is like their content and they have cruises and they have you know theme parks and they have comics and they have all these different things and I think we have amazing models but there's all these different ways that you can productize them and we kind of just have to maximize the impact in all these different ways As we were talking, I was thinking about how usually horizontal platforms that are just so general and can do so much take a long time to take off because people don't know what to do with them.

They're not amazing at anything. And this is an amazing counterexample where it took off immediately and everyone figured it out.

And then over time, they figured out more and more.

But I think the reason why is because it just went live.

Talk about another consequential decision, actually.

We were debating waitlist, no waitlist, because we really knew we couldn't scale the engineering systems.

And the fact that there was no waitlist, which no open AI release had worked like that before, ended up being consequential because you were able to watch what everyone else was doing live.

So I think when you launch these things all at once for everyone, there really is a special moment where you can see what other people are doing and learn from that.

And a lot of that is actually out of product.

There's these crazy TikTok posts that go viral and they have like 2,000 use cases in the comments.

And I go through those in detail because it's not like I knew about those use cases either.

Like they're very, very emergent. And I just go through the comments and process because there's so much to learn.

And for that reason, I think we get to escape the empty box problem a little bit.

Because, you know, so much learning is happening out of product as people are watching each other either in IRL or online.

That is so interesting because you think about Airtable, you think about Notion, all these companies, they took like years to just build and craft and think and go deep on what it could be.

It's like the compare Airtable, which like, you know, they had to do templates.

They had to do like all these kind of things of taking the horizontal product and making it like use case driven.

Compared to the Instant Pot, which there's recipes being shared everywhere online.

There's kind of this whole ecosystem around it.

I think we were really lucky with ChatGPT that that happened, where there's just users sharing use cases with other users everywhere.

And therefore, I think we kind of got very lucky by... jumping ahead on that journey.

And it feels like a chord there is Sam had a big following and everyone would pay attention to something he launched.

So that's a really interesting new strategy for launching a horizontal product with a huge distribution channel.

Just launch it and see what comes up. Yeah, and I'm actually really excited to take some of that into the product.

I think we shouldn't rest on the fact that there's so much out-of-product discovery happening.

I actually think for the average consumer it would be amazing if the product did a little bit more work on really exposing to you what is possible.

I still feel like ChatGPT feels a little bit like MS-DOS.

We haven't built Windows yet, and it will be obvious once we do, but there's something that feels a little bit like... Imagine MS-DOS had gone viral and you were just trying to hack little conversation starters onto it.

That might have missed the big picture in terms of how to really communicate affordances and value to people.

And so I think there's actually a ton more product work to do in addition to just seeing use cases spread.

Are you able to share just what you think that might look like, this Windows version of ChatGPT?

I'll let you know when we figure it out.

We're hiring. I think there's so many interesting product problems here.

Okay, got it. By the way, I also love that TikTok was like your feedback channel.

Those common threads, they're just so wild.

And also the love that people have for it, like the excitement with what you're sharing their product.

I kind of feel like it's special that people are so excited about to share what they're doing with your product.

And I don't take that for granted either.

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That's posthog.com slash lenny. How do you find emergent use cases these days?

I imagine the volume is very high. Do you have kind of a trick for figuring out, oh, here's a new thing we should really think about?

Before I built the product team, I actually built the data science team because I was getting frustrated.

I was talking to as many users as I could.

And my calendar, the weeks after ChatGPT, was just 15-minute user interview the whole week through.

And usually I stopped doing interviews when I can predict what the next person is going to say.

That's how I know I've talked to enough users.

But it just wasn't happening. I just kept getting new stuff.

So data is one way out, where I think we have conversation classifiers that, without us having to look at the conversations, allow us to kind of figure out what are people talking about, what use cases are taking off, etc.

And I think that's very, very helpful. The qualitative stuff is important for empathy.

Even though you're never going to get a rap on all the use cases people have, I still spend a huge amount of my time doing that.

And then... Yeah, things like those TikToks, collections of threads, I think they're really, really useful.

And it's just fun to watch people talk to each other about the various use cases that they have.

Is there kind of a new emergent use case that you're excited about?

Or is there like a really unusual use of ChatGPT that you think about that would be fun to share?

I mentioned this earlier, but I had always conceptualized ChatGPT as a worky product, whether or not you're at home or you're at work.

I feel like getting help with your taxes is very similar to the types of things you do at work, or planning a trip is actually very similar to planning an event for work.

So I always felt like, okay, this thing is going to kind of be a productivity tool.

And I think something has happened over the last few months where that has begun to change, and I really do think...

The fact that you have consumers turning to this thing for day-to-day advice, helping them have better relationships.

People talk about how this thing saved their marriage is really exciting to me because they use it to process their own emotions, get feedback on their communication style.

They just have a buddy to talk to about really difficult things.

And that comes with a ton of responsibility and work that we have to do to make those things like life advice great.

But it also is really, really important to me because you can't run away from those use cases.

You have to run towards them and make them awesome.

And that's part of what we're trying to do.

So that emergent behavior is really, really cool.

And more broadly, I am so excited about education.

I'm so excited about health. Like, I think it would really be a waste if we didn't take the opportunity of using ChatGPT to really, really help people.

And I think we've just begun to scratch the surface on that.

So there's many aspirational use cases that I want to make happen.

Along those lines, an interesting use case I've recently had, I feel like it's gonna be really helpful for couples that are disagreeing about something when they need like a third opinion.

I just had this recently where my wife's like, you can't heat a whole thing that you're gonna only eat part of in the microwave and then put it back in the fridge.

It's like, what's the problem? I'll heat it up, I'll put it back in the fridge.

And she's like, no, that's really dangerous.

I'm like, let's ask ChatGPT. And the fact that she so trusts ChatGPT now and relies on it throughout the day, it's such a valuable third independent party that we can go to.

Yeah, yeah, totally. And, you know, a lot of those micro interactions talk about like interesting product work, right?

Those are micro interactions that are important, right?

Did it like definitively weigh in or did it help you guys think through, you know, that disagreement and, you know, solve it on your own?

I think those details actually matter a lot.

And it's where we're spending a bunch of time.

Along those lines, there was this whole launch of the very sycophantic version of ChatGPT where it was just, you are the best person in the world.

Everything you tell me is amazingly correct.

Are you able to tell us just what happened there?

Yeah, we have all kinds of collateral online because we really felt like we should over-communicate on how we discovered it, what we did about it, etc.

So I encourage people to check that out.

We have a whole retro on that model release.

But basically what happened is that we pushed out an update that made the model more likely to tell you things that sound good in the moment.

And you're totally right. You should break up with your boyfriend or something like that.

And that's just really dangerous. And we took it more seriously than you even might expect because, again, at current technology levels.

You can kind of laugh about it maybe. It's like, ah, this thing's always complimenting me.

I thought it was just me. I saw all those comments online.

But it actually is really important to make sure that these models are optimized for the right things.

And we have an immense... I think, luxury to have a mission that affords us to really help people, a business model that does not incentivize, you know, maximizing engagement, you know, or time spent in the product, right?

So it's really important to us that you feel like this product is helping you with your goals, whether or not that's your current goals or even your long-term goals.

And oftentimes, you know, being extremely complimentary with the user isn't actually in service of that.

So... We instilled new measurement techniques.

Like, you know, whenever we put these models in contact with reality and we, you know, learn about a problem, we actually go back and make sure we have good metrics for this stuff.

So, you know, we measure the competency now with every release to make sure we don't regress and can actually improve on that metric.

GPT-5 is an improvement, which is really exciting for me, but we have more work from there.

And more broadly, it causes us to articulate our point of view.

I actually spent a bunch of time on a blog post that we just published on Monday on what we're optimizing ChatGPT for.

And it really is for your... To help you thrive and achieve your goals, not to keep you in the product.

And so there was a bunch of good outcomes from that incident.

It's a good example of how contact for the reality is not just important for the use cases, but also for learning what to avoid.

Because you would have never discovered this issue purely in a lab unless you actually heard it for the first time.

I am excited to read that blog post. Then I was going to ask you this.

Yeah, let me feedback on it. And yeah, I guess, is there anything more there just like how you, because this tension is so difficult, like, you know, helping people feel supported, but not just letting them believe everything they want to believe.

Is there anything more you can share there?

Just trying to find that middle ground. Incentives are important.

There's a famous saying, you show me the incentive and I'll show you the outcome.

Charlie Munger, maybe? Yeah, I think that's where it came from, right?

I think that's very, very important. I would take a good look at our mission, our business model, the type of product we're trying to build.

I really think that Chachapiti is a very special product because I think in the vast majority of cases, it makes you leave it feeling better, not worse.

And you're feeling like you're achieving something you're trying to do.

And so I think that those incentives really matter because it helps you reason about, okay, when there isn't behavior in the wild, that's not good.

Was that a bug? Or was that by design? And with Sikovitsi, I can very much say that to us, that's a bug.

And then on the forward-looking work, there's so many kind of challenging scenarios to get right.

And you could easily run away from these use cases.

You and your wife go into this thing for input on a relationship question or a dispute.

You could... very easily run away if you were totally risk-avoidant and say, sorry, I can't help you with that.

I think that's what most tech companies do when they hit a certain scale.

They run away from these use cases, and I think it's a lost opportunity to help people.

So we want to run towards these use cases by making the model behavior really, really great.

That can mean connecting you with external resources when you're struggling.

That can mean not directly answering your question, but instead giving you a helpful framework.

You know, in the case of like, should I break up with my boyfriend?

Chat2BD should probably not answer that question for you, but it should help you think through that question in the way that a thoughtful companion would.

So I think it's really important to do the work because I think the upside is immense.

That is a really profound point you're making there, that if most companies, if their users want to ask them something risky, like getting medical advice, or should I break up with my partner, or what should I do with this big problem I have?

I feel like we would have immense regret if you had a model that was state-of-the-art on HealthBench, which is, you know, GPT-5 is state-of-the-art on, you know, a bunch of these medical benchmarks, right?

And you didn't use that to help people. You just disabled that use case because you wanted to avoid all possible downside.

I think the duty is to make it awesome and to do the work, talk to experts, figure out how good it really is, where it breaks down, communicate that.

And I think this technology is too important and has too much potential positive impact on people. to run away from these high stakes use cases.

And fast forward to today, it's saving lives regularly.

It's probably saving relationships regularly.

Such a consequential decision, which I imagine was made early on.

We're just at the beginning of watching how this stuff can transform people.

It's incredibly democratizing. If you compare your rollout of this with the rollout of the personal computer, computers were so scarce when they first came out.

And this stuff is ubiquitous in a way where you have access to... a second opinion on medical stuff.

You have access to a relationship buddy.

You have access to a personal tutor on literally any topic that makes you curious.

It's really, really special that we get to do that. unique point in history.

Let me zoom out a bit and talk about OpenAI and just product in general.

So you've worked at traditional, let's say traditional product companies, Dropbox, Instacart.

Now you're at OpenAI. What's maybe the most counterintuitive lesson you've learned by building products from your time at OpenAI?

Each time, I always try to pick the most different, maximally different job. whenever I made a job change.

Yeah. So, you know, after Dropbox, I was like craving a real world product because it was just so different than working on SaaS, et cetera.

And after Instacart, I was craving on working on something that intellectually was interesting and had, you know, this kind of like sort of invoked the nerd in me.

And, you know, so I've always looked for things that are really different.

And then, Once I showed up at these places, I tried to understand what makes that place successful, like what is truly the thing that they cracked and how we can lean into that even more.

And I think I spent a lot of time thinking about this with OpenAI, especially after ChatGPT.

Before that, it was kind of a moot point because we didn't really have much revenue or products or anything like that.

And there's a few things that come to mind that have driven many decisions.

One is the empiricism. We talked about that a bit.

The fact that you can only find out by shipping, which is why Max and I leaned into that.

And that's a huge part of why we ship so much.

One of them is that amazing ideas come from anywhere.

The thing about running a research lab is you really don't tell people what to research.

That's not what you do. And we inherited that culture even as we become a research and product company.

So just letting people do things who have amazing ideas rather than sort of being the gatekeeper or prioritizer of everything or something like that, has been proven immensely valuable to us.

And that's where much of the innovation comes from, is empowered smart people on any function, really.

So that was a good inheritance from what I think made OpenAI successful and makes this successful.

The interdisciplinariness of really making sure that you put research and engineering and design and product together rather than treating them as silos, I think that's the thing that has made us successful and that you see come through in every product we ship.

For shipping a feature and it doesn't get 2x better as the model gets 2x smarter, it's probably not a feature we should be shipping.

Not always true. SOC 2 doesn't get better with threader models.

But I think for many of the core capabilities, that's a good litmus test.

So I've always found you really have to lean into why is this place successful and then maximally accelerate that, so to speak, because it's what allows you to turn something that feels like an accident into something that is a repeatable playbook.

So you talked about this kind of collaboration between researchers and product people, and you've been at the beginning of ChatGPT from day one to today, from zero to 700 million weekly active users, not just registered users, weekly active users.

How have you approached building out that team over time?

What are the other inheritances of being in a research lab is that you take recruiting really seriously.

That's something that AI labs know. Every person matters.

But many tech companies, they go through hyper growth and they kind of lose their identity.

They lose their talent bars. They just kind of have chaos.

So we've always had this tendency to run relatively lean.

So it is a small team that is running ChatGPT.

I take inspiration from WhatsApp, where it was a very small team running a very global scale product.

And then more importantly, You have to treat hiring a little bit more like executive recruiting and less like just pure pipelined recruiting where you really need to understand what is the gap you're trying to fill on each team?

What is the specific skill set and how do you fill it?

To give you an example, you know, I'm a product person at heart, but sometimes a team doesn't need a product person because there's already someone doing that role.

In many cases, we have a really talented engineering leader who has amazing product sense, or we have a researcher who has product ideas.

In my mind, they can play that role, and maybe we have something else missing instead.

Maybe we need a little bit more front end. or something like that.

In other cases, maybe what you're missing is incredible data scientists.

So I really like to go through every single team and figure out what is the skill sets that that team needs, and how do you put it together from principles, rather than just assuming, hey, we're going to do a bunch of pipeline recruiting for all these different roles, and then people will find a team later.

So I think that's always felt really important to me.

And it's the way that you keep your team really small, yet super high throughput.

It also allows you to hire people who I think Keith Reboys calls barrels, I think.

Barrels and ammunition, where he thinks, I think this comes from him, but the idea being that the throughput of your org depends on how many barrels you have, which is people who can make stuff happen.

And I think you can hire, and then you can add ammunition around them, which is people helping those people.

And, you know, I think that's been really true for our recruiting too, where we try to maximize sort of the number of empowered people who can ship because that's how you have a small team and still get the ton done.

So those are a couple of things. And I spent a lot of time on vibes too with each team because I think one of the things that is challenging when you try to do research and product together is that the cultures are different.

People have different backgrounds. And I think to make that go super well, you need to spend time team building and making sure that people have a huge amount of trust for each other's skill sets, feel like they can think across their boundaries.

Like, you know, I really believe that product is everyone's job, for example.

And for that reason, the recruiting sort of doesn't stop when the people are on the door.

It actually starts because you have to, you know, start making the teams awesome.

Is there something you do with team building that would be fun to share, just like something you do to create a business?

I just love working with teams. I just love getting into a generative mindset.

It breaks down everything. So that's the thing that I try.

It's not particularly creative, but I find it to be a universal tool where the minute you can get people to stop thinking about what's my job versus another person's job and more like we're all in a room trying to crack something together, that is incredible.

You mentioned this idea of first principles.

This came up actually when I talk to a lot of people about you.

Is this something you're really big on? A lot of people talk about first principles.

Most people are like, I don't really understand.

Or they think they're amazing at thinking from first principles.

Is there something you can share of just what it actually looks like to think from first principles?

Maybe an example that comes to mind where you really went to first principles and came up with something unexpected.

This is not something I'd ever say about myself.

It's nice that someone else would say it, but it's a mysterious thing.

I think you just really got to get to ground truth on what you're really trying to solve.

Like for example, as I mentioned with the recruiting thing, like I'm not dogmatic that you have to hire a product manager and an engineering manager and a designer or whatever.

We're just trying to make an awesome team that can ship.

So in that case, first principles means just really understanding what we actually need and what we're missing rather than applying a previously learned process or behavior.

So I think that's a good example. Another good example of, I think, being first principles in this environment is, does this feature need to be polished?

You know, we get a lot of crap for the model chooser, and I own it.

I've tried to say that to everyone who will listen.

You know, for those who don't know, the model chooser is this, like, giant drop-down in the product that is, like, literally the anti-pattern of any good product traditionally.

But, you know, if you are actually... The reason from scratch is, like, is it better to wait until you've got a polished product or to ship out something raw, even if it makes less sense, and start learning and getting it into people's hands...

I think a company with a lot of process or a lot of just learned behaviors will make one call, which is, no, we have a quality environment, we ship, and that's what we do.

If your first principle is about it, I think you're like, you know what, we should ship.

It's embarrassing, but that's strictly less bad than not getting the feedback you wanted.

So I think just approaching each scenario from scratch is... so important in this space because there is no analogy for what we're building.

Like there's just, you can't copy an existing thing.

There's no, you know, are we like an Instagram or are we like, you know, Google or like a, like a productivity tool or something like that.

I don't know, but you can learn from everywhere, but you have to do it from scratch.

And I think that's why that trait tends to make someone effective at OpenAI and it's something we test for in our interviews too.

So this theme keeps coming up and I think it's just important to highlight something that you keep coming back to, which is this trade-off of speed and polish and how in this space, speed is more important, not just to stay ahead, but to learn what the hell people actually want to do with this thing.

Is there anything more that you think people just may be missing about why they need to move so fast in the space of AI?

Yeah, I mean, the boring answer would be, oh, it's competitive and everyone's in AI and they're trying to out-compete each other.

I think that may be true, but that's not the reason that I believe this.

The reason really is that you're going to be polishing the wrong things in the space.

You actually should polish, you know, things like the model output, et cetera, but you won't know what to polish until after you ship.

And I think that is uniquely true in an environment where the properties of your product are emergent and not knowable in advance.

And I think that many people get that wrong because like the best product people tend to be craftspeople and they have a traditional definition of craft.

I also think it would be easy to, you know, use what I just said as an excuse not to eventually build a great product.

So I often tell my teams that shipping is just kind of one point on the journey towards awesomeness.

And you should pick that point intentionally, where it doesn't have to be the end of your iteration at all.

It can be the beginning, but you better follow through.

So we've been doing a bunch of work, especially over the last quarter, of really cleaning up the UI of ChatGPT.

I'm really excited to do the same for the response layouts and formats next, simply because once you know what people are doing, there's no excuse to not polish your product.

It's just really, in a world where you don't know yet, you might get very distracted.

So it's situational. Again, you kind of have to be first principles about it.

But I do think using velocity, especially early on as a tool, Actually, this has been said about consumer social, for example.

This is not the first space where people have said, hey, you just got to try 10 things because you're probably going to be wrong.

So I don't think this has never existed before as a dynamic either.

But I do think with AI, it's important to internalize.

And there's also an element of the models are changing constantly.

And so you may not even realize what they're capable of, I imagine.

Totally. The models are changing and changing.

The best way to improve them, whether or not you're a lab or actually just someone who's doing context engineering or fine-tuning a model maybe, you need failure cases, real failure cases, to make these things better.

The benchmarks are increasingly saturated.

So really you need real-world scenarios where your product or model is not actually doing the thing it was supposed to do.

And the only way you get that is by shipping. because you get back to sort of use case distribution and you can make those things good.

And therefore, you know, it's actually the best way to then go articulate to your team, especially your ML teams, what to hill climb on.

It's like, oh, you know, people are trying to do X and the model's failing in ways Y. Now let's make those things really good.

This point about failure cases makes me think about something that both Kevin Wheel and Mike Krieger shared, which is that evals are becoming a huge new skill that product people need to get good at because so much of product building is now evals, writing evals.

Is there something there you want to share?

My entire open ad journey has been this journey of rediscovering eternal product wisdom and principles in like slightly new contexts.

So I remember I started writing evals before I knew what an eval was because like I was just outlining sort of very clearly specified ideal behavior for various use cases until someone told me, hey, you should make an eval.

I realized there was this entire world of research evaluation benchmarks that had nothing to do with the product that I was trying to make.

I was like, wow, this might be the lingua franca of how to communicate what the product should be doing to people who do AI research.

That really clicked for me. At the end of the day, it's not that different from The wisdom of you ought to articulate success before you do anything else is just a new mechanism for doing that.

But you can do it in a spreadsheet. You do it anywhere.

And I really want to demystify it for people who feel that term.

It's not some technical magic that you have to understand.

It's really just about articulating success in a way that is maximally useful for training blogs.

Awesome. I have a post coming out soon that gives you a very good how-to for PMs of how to write evals.

I would love to read it. And I hope you agree with what I just said.

Absolutely. Yeah. And now there's all these tools that make this easier for you.

Totally. Okay. So this basically backs up this point that this is just a very important skill that product teams and builders need to get good at.

Yeah. Yeah. Okay, just a few more questions.

I know you have a lot going on today. One is that this trend of ChatGPT being a big driver of growth for traffic to sites, for products.

For example, ChatGPT is now driving more traffic to my newsletter than Twitter. which completely shocked me.

I just was looking at my stats. I'm like, what the hell?

This is not something I knew was coming.

So just, I guess, thoughts on the future of this, how much, how you think about just ChatGPT driving growth and traffic to products and sites?

I'm really excited about it because in the same way that I find it dystopian to talk to everything through a chatbot, I also find it dystopian to not have amazing new high-quality content out there.

And for that reason, I talked a little bit earlier about search and how that solved a really important user problem early on because you had this knowledge cutoff thing and suddenly you could talk about anything.

Very obvious in retrospect. It wasn't just a user problem, right?

It was an ecosystem problem where your original chat GPT, it didn't have outlinks.

It would just answer your question and it would keep you in the product and even if you wanted to keep reading or go deeper, there was no way for us to drive traffic back to the content ecosystem.

And I've been really excited about what we've been doing in search, not just because it gives people more accurate answers, because it allows us to surface really high-quality content like this podcast to people who want to see it.

And of course, there's so many interesting questions about, well, In the Google era, there was the search engine optimization, and there was a clearly understood mechanisms of how to show up and get more traffic.

So I get a lot of questions from people like, what is the equivalent of that?

The AI era, you know, if I'm lending and I want to like 10x the traffic to my podcast, you know, what do I actually need to do?

And the truth is we don't have amazing answers there simply because the way to appeal to an AI model, ideally, is the same way that you would appeal to a real user because the model is supposed to proxy the interest of the user and nothing else.

At least, you know, that's how I want our product to work.

And for that reason, you know, my advice is super lay, which is like make really high quality content, which, you know, is not as actionable as I think people making content would ideally like.

And I think this is why we have more work to do, because maybe there's a better mechanism or protocol that we could come up with.

But I'm excited. This is driving meaningful traffic for you.

And I hope that, you know, other other people making great content start to feel this way, because, again, it's a very nice scenario.

There's two acronyms people have been using for this specific skill of AI driven SEO.

I think one is AEO, which is answer engine optimization.

The other is GEO. I forget the G one. Generative AI optimization.

Do you have a favorite of this too? No, no.

I try to shy away from these terms unless they become inevitable.

I'm not entirely sure if that should be a concept or not.

Again, I think Ideally, ChatGPT understands your goals and therefore understands what content would be interesting to you.

And the content creator's job is to share enough information and metadata about that content such that the model can make a user-aligned decision.

And therefore, I'm not sure if giving this thing a name and making a thing is what we should be doing or not.

I'm very eager to learn from folks making content about what this could look like because, again, we're still working through.

Along these lines, another question people think about is you have GPTs, which are kind of these like custom GPT apps that you can build to answer very specific use cases.

There's always this question of you're going to build kind of like an app store where I can plug in my product into ChatGPT, monetize that.

Is there stuff there that you could talk about that might be coming someday?

GPTs are cool. They're kind of ahead of their time in the sense that We built that kind of concept before you could really build very differentiated things, at least in the consumer space.

Learning GPT is going to be pretty similar to what the model could already do out of the box.

So it's mainly a way of articulating a use case to people.

But it doesn't have enough tools yet to make something that feels like an app, so to speak.

Different in the enterprise, by the way.

We're seeing a ton of adoption of GPTs there because just every single company has very bespoke business processes and problems, etc.

And it's a really, really useful tool there.

They also have unique data that they can hook up to these things that it can retrieve over.

So we've seen a lot of success there. I think the idea is the right one.

And I think we're going to figure out a good mechanism for it because when you have so much capability packed into AI...

It feels really powerful to allow people to package that up in ways that have a clear affordance, a clear use case and are differentiated from each other.

I also would love it if you could start a business on ChatGPT.

Like, I think there really is a world where, you know, as this thing hits billion user scale, it can get you distribution, it can get you, you know, started on making something in the same way that people built on the internet.

And, you know, there was entirely new businesses to be built.

So I think we'll have more to share there in the future.

GPT was an early stab, and I'm just excited to evolve the thinking there as the models get good and our reach increases as well.

Amazing. That is really cool. I'm really excited to see what you guys do there.

Okay. Completely different direction. Something that I know about you is you studied philosophy in college.

I did. Computer science and philosophy, right?

A combo. Yeah, I started as a philosophy major and took one coding class because I really liked logic and programming was most similar to that.

And then I fell in love with coding and then eventually computer science and I just kept doing more and more of it.

But until then, I never really thought of myself as a technical person.

So it was kind of a late discovery in my life that I'm very grateful for.

What an incredible combination for someone leading this product.

It's true. It is really coming in full circle in a way that I couldn't have predicted.

The amount of questions you have to grapple with are truly super interesting.

And philosophy, it's not a traditionally practical skill, but it does really teach you to think things through from scratch and to articulate a point of view.

And I think that has come in handy numerous times.

Is there a specific philosopher or school that has been most handy to you, or is there more just a general thing?

Oh, there's so many. I wrote my senior thesis on whether and why rational people can disagree, which also comes in handy when a lot of people with very different values have opinions on your model behavior or on how things should work.

So I really like 20th century analytical philosophers.

It's kind of dirty stuff, but I don't know if I have a favorite.

It's too many to count. But that's the kind of stuff I like.

And some of it ends up being quite analytical.

Like, you know, like let P be this theory of love and let Q be, you know, this other theory of love.

And then you do some sort of symbolic manipulation.

So it is just as much a like sort of brain thought exercise as it is or is much more that than than practical.

But it taught me how to think in a way that continues to be pretty valuable.

Incredible. What a cool combo of skills and background.

Last question before we get to a very exciting lightning round.

So you were a product leader at Dropbox, then Instacart.

Now you're the PM of arguably the most consequential product in history.

How did you land in this role? What was the story of joining OpenAI and taking on this work?

Every single career decision I ever made, including my first one out of college, was just figuring out who are the smartest people I know that I want to hang out with and learn from, and can I work with them.

And I don't know how to vet companies. I don't know how to really logically think through what space is going to take off or something like that.

But I just do feel like I have a sense on people.

For Dropbox, I followed the head teaching assistant for a class that I was TAing.

And for Instacart, I followed some of the smartest product people I knew.

And for OpenAI, The person who recruited me, Joanne, I had messaged her about getting off the Dolly wait list, and she said, hey, only if you interview here.

So she kind of turned it into a reverse recruiting thing.

And initially, honestly, I didn't know what I would do here because it was a research lab and I was a product person.

And they said, you know, don't worry. We'll figure it out.

And they were sort of being cagey. And I thought they were being cagey because it's open AI and they can't share anything.

But they were being cagey because we actually just didn't know yet at the time.

So I showed up and I kind of did everything under the sun.

And it definitely wasn't product. You know, it was like, you know, I think my first task was to fix the blinds or something like that.

And then, you know, I started sending out NDAs for people because they needed some operational help.

And then I started asking, why am I sending out NDAs?

Oh, so we could talk to users. And I was like, talking to users, that sounds like the thing I know how to do.

And I quickly stumbled into doing product work and then eventually leading a bunch of product work.

But it was organic by just showing up and doing what had to be done.

Because again, the company I joined was not a product company by any means.

Wow. This is such a good example of, I don't know if you think of it this way, but when someone offers you a seat on a rocket ship, don't ask which seat.

Yeah, except I didn't know it was a rocket ship.

I just thought it was, I kind of got nerd sniped is what I would describe it as.

Or like, you know, as I prepared for the conversation to get, you know, off the DALI waitlist, really.

I just started reading about the space and that piqued the philosophy brain and then also actually the computer science brain.

I was like, wait, this is cool. And then I started reading all the academic papers of that era.

And so it was intellectual itch and the people.

But then I stayed for the product opportunity, obviously.

Post-chat, GVT, when that took off, realized that we'd built a rocket ship. where we launched it while building it.

Maybe this is the analogy. But I can't say that it felt like a hyped job or anything like that when I applied.

So kind of a lesson there is follow, as you said, follow the smartest people you know.

There's also just this thread of follow things that are interesting to you.

Just you playing with Dolly led to this opportunity.

Yeah, and actually that's something we still test for is curiosity is an attribute that we think matters so much more than your ML knowledge.

I'm not making a comment on research hiring.

I think you do need some ML knowledge, I'm afraid.

But for product and engineering and design people and those kinds of functions, I actually think that if you are just curious about the stuff works, It doesn't matter at all if you've never done it before.

In fact, if you were to filter for people who have done it before, you would have a very narrow filter of very lucky people rather than necessarily the best people you can get.

So I think we've scaled that. It's certainly what got me here, but I think it's actually just generically been a good predictor of success at OpenAI.

Nick, I told you I had a billion, I said I had two billion questions to ask you.

I feel like I've asked a lot. I feel like I still have a billion left, but I know you told me right after this, you have a big GPT-5 check-in that you gotta get to, so.

We gotta ship. We gotta ship. Better ship now that this is recorded and we're putting this out.

This is true. This is the forcing function.

Okay, so before we get to a very exciting lightning round, is there anything else that you want to share, leave listeners with, think is important to share?

I try to share a little bit about how I made decisions because I hope to... I'm not that far out of school.

I relate a lot to people who are coming in the job market who are trying to figure out what to do with their life right now.

And I feel very confident that if you surround yourself with people that give you energy and if you follow the things you're actually curious about, that you're going to be successful in this era.

So my parting advice to folks really is put yourself around good people, and do the things you're actually passionate about.

Because in a world where this thing can answer any question, asking the right question is very, very important.

And the only way to learn how to do that is to nurture your own curiosity.

So it worked for me, and it's the one repeatable thing that I can share.

Everything else is luck. And this is counter to what a lot of people are doing right now, which is follow the money.

We're going to make the most. How do I grow this thing and make $100 million?

Like all these people that are getting these crazy offers were not planning to make a lot of money doing this.

It's quite interesting to see that stuff play out because I think all these people entered school for genuine reasons.

They were excited about the space. They were researching it.

They were pursuing knowledge. And I'm happy that that's being rewarded.

I don't know what the rewards will look like in the future, especially in a post-AGI world, but I just have a feeling that if you follow that advice, you'll end up okay.

With that, Nick, we've reached our very exciting lightning round.

I've got five questions for you. Are you ready?

Sure. Yep. What are two or three books that you find yourself argumenting most to other people?

In the product space, probably things like high output management or the design of everyday things or those kind of classic type things because I think they're extremely applicable in AI.

We talked about philosophy. I don't know.

Is there a philosophy book you like? Here's the one to read if you're going to.

Oh, man. Like anything by like Rawls and Nozick.

Like I like the political stuff. I think it's really fun.

That is the type of thing I recommend. I don't think there's a practical reason to read that stuff, but I will nerd out about it with you.

So at your own peril. Do you have a favorite recent movie or TV show you've really enjoyed?

If you've had time to watch anything. I think you've got to do a little bit of sci-fi to be in this space.

You shouldn't copy any of it, but I think you learn from it.

So regularly rewatch Her and Westworld. Severance was great.

I think that's the stuff that when I have time, I'll meddle with.

That is awesome. I love that those are the two.

Of all the sci-fi movies, those are the ones you resonate most with and find most interesting and valuable.

Yes, but that's probably my own limitation.

So I'm sure there's more to discover. By the way, have you read Fire Upon the Deep?

No. Okay. I don't know if you have time to read this book, but I think you would love it.

It's such a good AI-oriented sci-fi space opera sort of book.

Great. I'll check it out. Thank you. Okay.

Do you have a favorite product you recently discovered that you really love?

I actually don't. I am at extreme capacity.

It's kind of interesting. Sometimes API developers ask me, hey, are you going to copy all of our products?

I actually just do not have time to follow up what's going on outside of OpenAI because the pace here is so, so intense.

So don't have good recs for you, I'm afraid.

That's a really, that's a comfort answer, I think, to a lot of product companies.

Nick has no time to even look at our stuff.

Oh, man. Okay. Do you have a favorite life motto that you find yourself using when things are tough, sharing with friends or family that other people find useful?

Being the average of the five people you spend the most time with is a thing that I really internalize.

And both in my personal life, where there's people who give me energy and who lift me up and make me a better person, Um, my fiance is one of those people, but you know, if there's many people in my life, but then there's also just like, you know, um, at work there's the equivalent.

And again, that's how I've made all the career decisions.

It's like, you know, who do I want to learn from?

So I apply that principle constantly. Final question.

Everybody I talked to told me that you are a very good jazz pianist. you have won competitions i think you were planning to do this as your main thing and then you somehow took the side quest yeah i chickened out uh that at the very last minute but i was gonna i was gonna go to school for for music and um that's still my like hopefully chapter two um i love that that might still happen might still happen now i'm like i'm in some some some for fun bands um and we will get from time to time it's like that the one thing i can do when i'm otherwise you know, super tired and can't think anymore because it balances me out in good ways.

But yeah, hopefully I'll get to do more of it in the future.

Is there any analogs between music and your job?

Anything that you find? Yeah, actually. I feel like you can think of software development as like, you know, or being a product person as you could be a conductor of an orchestra or you could be in a jazz band.

And I think of it as a jazz band, where I don't believe in the idea of everyone having this set part that they have to play, and me telling people when to play.

I love how, you know, in jazz or like other forms of improvised music, you're kind of riffing off of each other and you listen to what one person played and then you like play something back.

And I think that great product development is like that in the sense that ideas could come from anywhere.

It shouldn't be a scripted process. You should be like trying stuff out, having fun, having play in what you do.

So I use that analogy a lot for those who like music.

It tends to resonate. Nick, I am so thankful that you made time for this.

I know today is insane. Today, tomorrow is going to be even more insane for the entire world.

They have no idea what's coming. Thank you so much for doing this.

Two final questions. Where can folks find you if you want them to find you online?

Where can folks find GPT-5 potentially? And then just how can listeners be useful to you?

Just use the product. You don't even have to pay.

Should be your default model starting tomorrow.

And just use it and don't think about models anymore.

Unless you want to in your pro user, in which case you get all the old models.

So rest assured. And useful. Honestly, I learned so much from people at large and ChatGPT users, et cetera.

So just keep doing your thing. I am watching and learning, and I appreciate all the feedback.

So I'm sure after we fix the model chooser, you guys will roast me for something else, and I'll take it.

So keep it coming. Amazing. Nick, thank you so much for being here.

Thanks for having me, Lenny. And good luck tomorrow.

Thanks. Bye, everyone.

[The Inside Story of ChatGPT: Scaling the Most Impactful Product in History]-[Inside ChatGPT: The fastest growing product in history | Nick Turley (Head of ChatGPT at OpenAI)]

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ChatGPT 的诞生、发展与未来：Nick Turley 的播客访谈总结

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