ChatGPT——超级助理时代｜BG2 嘉宾访谈

ChatGPT originally was entirely free, and the reason for that was that it was intended to be a demo. We were going to wind it down after a month. We then realized that the demo had gone viral, people loved it, and it was actually a product. But to be a product, you can’t take it down every time you’re at capacity, so we shipped subscriptions simply because they could shape the demand. It was a way of gracefully turning users away when we had to turn someone away.

Brad Gerstner

You guys are at 900 million weekly active users now, and that growth has been incredible. The next billion users—where are they going to come from?

We’ve got about 10% of the world coming to us now. There’s 90% left to go, right? There’s so much more opportunity.

Brad Gerstner

Well, Nick, I’m so excited to have you here.

Thank you for having me, Brad.

Brad Gerstner

You’ve had quite the journey, from Germany to the U.S. for Brown.

That’s true.

1. Nick Turley’s Journey to OpenAI

Brad Gerstner

Most recently at Instacart, delivering groceries in 30 minutes, to now delivering AGI to billions. I’m sure that was the plan all along.

Yeah, clearly. Total master plan.

Brad Gerstner

Tell us about your journey. How did you get to OpenAI? I know it’s a fun story. And your three and a half years or so at OpenAI—how have they gone?

The only throughline in how I’ve made any sort of employment decision has been entirely people-based. I don’t claim any credit for joining OpenAI or predicting ChatGPT or anything like it.

I hit up someone I admire a lot, who I got to know at Dropbox—Joanne, who worked here at the time—and I asked her to get me off the DALL-E 2 waitlist. She told me I had an interview if I wanted to get off the waitlist, so I took the bait, got totally nerd-sniped in the process, and here I am.

Brad Gerstner

There you go. The DALL-E 2 waitlist will get you.

It’s a great recruiting tool.

Brad Gerstner

Nice, nice, nice. We should do more waitlists, probably.

Yeah, yeah, yeah.

2. ChatGPT’s North Star: Long-Term Retention

Brad Gerstner

The big supercycle we’re in is ChatGPT. I assume over a billion users on the monthly side, 900 million weekly active users, recently reported, up from zero three and a half years ago. If I imagine what the dashboard of Nick Turley looks like, it could have users, paying subscribers, daily active users, retention, engagement. There are 15 things, maybe all of them. What’s your north star? How do you optimize for? What is Nick looking at in his daily dashboard?

It’s funny, right? It’s such a young product. It’s been, to your point, three and a half years, and this kind of question changes as you evolve and grow up and ask yourself, “What are we really building here?” To this day, we want to build a super-assistant that can actually help people achieve their goals.

3. What Drove ChatGPT’s Consumer Breakout

Ultimately, the thing we care about is whether our product is doing that. Is it actually helping you do the thing that you’re coming to the product to do? It’s so different for different people, right? Some people are trying to get healthy, other people are trying to start a company, learn a new topic, or do their taxes. There are all these different things that you might be doing, and the true measure of success is whether or not we’re helping you do that.

Obviously, we look at WAU in particular because we want to know if you’re coming back to the product. We look at retention, but we look at all kinds of stuff in aggregate because there isn’t one single thing that you can optimize for.

Brad Gerstner

If you were to allocate 100 units of points to these metrics, which metric could you distribute the 100 units across in order of importance for you right this second?

4. OpenAI’s Future Interface, Rapid Fire, AI Jobs, and Nick’s AGI Moments

It’s a good question. I care a lot about long-term retention, and I would put all my points there because I’m really proud of the retention stats we have. Ultimately, the sign of durable value is whether or not people are coming back in three months, because that means you’re really solving their problems.

5. Why ChatGPT’s Retention Curve “Smiles”

I think things like revenue follow from that, versus trying to go after those things directly. We’ve had a lot of success making very principled decisions on this stuff. One good example is that GPT-4 used to be behind a paywall because we couldn’t serve it to everyone, and then we had GPT-4, which was a total breakthrough in our ability to run inference on it.

So we just gave it away for free, and that ended up being totally revenue-positive and retention-positive because it provided access to the technology. I think when you make your decisions that way and focus on the customer, you end up with a great product, and revenue obviously follows too.

Brad Gerstner

Phenomenal.

Yeah, it shows up in the numbers. I posted this chart yesterday on the data that we have from a third party. The retention curves for ChatGPT are smiling. Look at that. Just like that. That is a rare, very rare occurrence, as we know.

Why do you think that is? If you were to give us a narrative on that smile curve, what is the why? Why do these smile curves exist? What are you seeing in ChatGPT that has people who have maybe turned it off for a couple of weeks or months coming back? Why are they coming back?

Look, there isn’t one single thing. The way you build a retentive product is through lots and lots of little things and really trying to make it better systematically.

I will say that with AI, and ChatGPT in particular, I’ve found that it takes people some time to really understand all the parts of their life they can delegate, right? For many users, it’s a multimonth process for them to understand how this thing can help them and what all the different ways are that they could plug ChatGPT into their lives.

When I think about some of the breakthroughs and levers we’ve had, things like search and personalization have helped solve those user problems because search provides way more daily value to you. It used to be that ChatGPT was a pretty work-y product. We’d see usage go down on the weekend and during the summer months, when a lot of people were off from work.

Today, we’re mobile-first. The vast majority of usage is mobile, and we see all these personal use cases. I think search was a big investment that got us there, and personalization makes ChatGPT so much more relevant for you because it gets to know you over time. You get to know it. Those are 2 things that have materially moved the way people come back to the product, but there’s lots more to do.

Brad Gerstner

Yeah.

As mentioned, I’m not resting on our retention stats, even though we’re obviously very proud.

Brad Gerstner

Nice, nice, nice.

The other thing that I got wrong about ChatGPT was 2 to 2 and a half years ago. I was like, “Well, let’s look at who’s going to win this consumer AI race.” Typically, these consumer markets are winner-take-most or winner-take-all. Look at search: Google has near 90% or more market share and a $3 to $4 trillion market cap.

Mobile is the same thing with Apple. Social is the same thing with Meta. I was like, “Well, AI—Meta has all the distribution. Google’s got all the distribution. They’ve got 3 to 4 billion users. It would be a flick of a switch for them to roll out their AI.” But I was wrong. That’s not what happened. ChatGPT turns out—you guys are at 900 million weekly active users now, and that growth has been incredible. Clearly, distribution was not enough, right?

So the same question for distribution: What are the levers that have gotten us to this scale? Is it model quality? Is it product quality? Is it features? Is it the experience or product improvements like memory, personalization, or search? What would you say drove historical growth and success?

We’ve got about 10% of the world coming to us now. There’s 90% left to go, right? So there’s so much more opportunity to reach more people and introduce them to the way that AI can benefit them.

I only say that because the next billion users might be very different in terms of how you engage, reach, and provide value. But when I look backward, it’s been roughly one-third, one-third, one-third between classic friction-removal work and other areas.

One of our biggest moments, when you look at pure impact, was removing the authentication wall. Sam will say, “I told you so,” because I think that was his feedback from day 1: “Can’t you—you shouldn’t have to log into ChatGPT.” It’s stuff like that that you do for any product, and it does matter. Some things never change.

Another third or so is what I would call core product investments. They’re typically things that we’ve done together between research and product. Search and personalization are really good examples of that, where we came together and figured out not just UI/UX evolution, but also how to post-train these changes into the model. It was really the moments when we came together.

Another recent example is that we have these writing blocks that render when you ask about queries where you're trying to write with the model. Putting really good craft into those experiences really matters. Our users love it.

And then another 1/3 of the growth has been model improvements—step changes, like going from GPT-3.5 back then to GPT-4, then going from GPT-4 behind a paywall to GPT-4o suddenly available to everyone, right? But a lot of it is also the iteration that isn't splashy, that doesn't warrant a named release. I'm really excited about the updates we just made with GPT-5.3, GPT-5.4, etc. Because that's when we take a lot of user feedback and methodically address it, and obviously that shows up in our retention as well.

So, 1/3, 1/3, 1/3 between classic friction removal and access, core product investments, and then pure model improvements.

6. How OpenAI Gets the Next Billion Users

Bill Gurley

And so the question that I've really been waiting to ask you is: How do we get the next billion? Let's talk about that a little bit. There's a lot of it—it seems like, at least from the outside, in the fog of war, if I was a consumer today in the market to pick my super assistant, you would have a couple of great options. Claude is out there; they're having some great traction over the last couple of weeks. Gemini, mega distribution, Uber distribution, and us, by far the leading product today, at least in user numbers. Where are the next billion users going to come from?

First of all, just to contextualize that goal: We care about 2 things at the end of the day. Obviously, reaching more people is really important. It's the direct manifestation of our mission to the world: The more people we can introduce to the benefits of AI, the better that is. But we're also really excited to go deeper. And that means taking the same 1 billion users that find value in ChatGPT today and actually providing more meaningful value in their world, like actually helping them achieve their goals, not just answering questions, right?

So I'll talk about how we get to more scale, but I think it's important to remember that the way this technology is evolving is we're going to go beyond pure chatbots pretty fast.

Bill Gurley

Exciting.

I think, on scale, it's shocked me how many people have found value in ChatGPT as it works today, because I don't think delegation is a natural skill for most people. And ChatGPT is a power tool, right? You come to it; it doesn't tell you what it's for. You kind of have to discover it on your own, and you have to use it. Then you'll learn about this prompt that was really cool, and then maybe you're on Twitter and you learn about another one, or you're on Instagram and you learn about another.

But the product is like a raw appliance. And I think one thing we really need to nail as we reach the next set of users is a product that has a bit more of an affordance. Because I think for most people, they're very, very busy. And everyone, I think, in the world has intelligence-constrained problems—problems that more intelligence could help with—but you need to frame that to people.

Bill Gurley

Yeah. Yeah.

ChatGPT

I still feel like we're a little bit too much like a computer terminal, and it needs to feel more like software or, you know, an operating system of software, right? So that's one thing. Another thing that gets at the same constraint is beginning to be proactive.

In a world where a lot of folks are too busy to delegate their problems to AI or don't quite know where to start, I think being able to help you proactively is really, really important as well. But I think all these are product evolutions that we could make on top of the current tech. And the thing that gets me particularly excited is productizing our next-generation tech, or reasoning models, because the truth is, when you look at reasoning in ChatGPT today, it's relevant to a very small group of people. It's relevant for the people who are trying to get the most out of ChatGPT.

But I fundamentally believe that reasoning is transformative. And if you can figure out how to productize reasoning in a way that works on people's behalf without them even knowing, and that looks very much like the model doing long-horizon tasks on your behalf, it doesn't mean you encounter the concept. It just means it's benefiting you, right? So there's so much work to do, and the product certainly has to evolve to be relevant for this kind of scale.

Bill Gurley

Yeah. One of the things that I've been hoping for a while—and Brad made a bet 2 years ago—is: When can ChatGPT help me take actions? Can ChatGPT help me be more proactive? I think his bet expired at the end of last year, so we're very curious: When is that coming?

7. When ChatGPT Starts Taking Actions

I'll frame that for you, because with search engines and Google, 2 decades ago you'd have gotten the 10 blue links. You could have spent an hour getting the answer; you can now get the answer instantly with ChatGPT. And it feels like the next step is actions.

100%.

Bill Gurley

And it feels like the next step is, you know, with Pulse—Pulse is a great proactive product. I have a Pulse that runs weekly, but what I would really like is, like, “Hey, Nick spoke about something. Just find me—make sure I know that Nick spoke about this,” or, “Hey, this XYZ thing happened that I cared about a lot.” When is one of those going to get proactive? What is the modality going to look like?

Yeah. So there are 2 concepts, I think. ChatGPT should be doing stuff rather than just answering, and ChatGPT should be proactive. When you put them together, it starts feeling like a super assistant, because I think these things compound.

On the action-taking piece, strictly speaking, ChatGPT can do stuff today. The action space is just very limited, right? It can search the web, which means it can use a search tool or browser in the same way that a human would. It can make images. It can do all these things, right?

But it clearly doesn't have the same action space that a human with a computer would have. And that is what we aim to build. Timing is everything on these bets, right? And I don't pretend to be great at timing either. You look at past attempts that we've made, like the ChatGPT agent, for example, which kind of has capabilities like this. It was just slightly too early. The models weren't quite good enough to hit real escape velocity.

And the problem is, if you don't have escape velocity, users don't learn to trust it. They don't even try. So when you look at a lot of the things people were doing in the original version of ChatGPT agent, it was the things that happened to work, like migrating your file server into the cloud or something like that. Useful stuff, but very niche.

Bill Gurley

Yeah.

As this stuff gets better, we just have to get it to a point where people try to use it for real, meaningful problems in their life, because then we can start hill-climbing. And this has been the magic of ChatGPT: When ChatGPT launched, it was good enough to get real attempts at use cases, even if they didn't initially work. ChatGPT was a pretty bad writer originally; it was a bad software engineer, but people tried and got enough value out of it that we could take those use cases and make them great.

And I do think we're about to get to that point with general-purpose agents, where it works well enough that you get at least partial credit. And because you're getting partial credit, you get really good tasks back, and then the magic begins, because once you have a set of use cases that you can climb the hill on, we can make them awesome.

So on task, I think we're close, but I think even people inside of OpenAI would have had a hard time predicting exactly when this gets good. We've been excited about it for a while.

On proactivity, Pulse was a really great first step, because what we wanted to build was a form factor where you're not prompting the model; the model's prompting you. That's for the reasons I described earlier: It's so hard for people to delegate and to figure out what their problems are. What if the AI understood your goals and the things you're interested in and just could start being proactive on your behalf? Pulse is limited in the value it can provide for you because it's not connected to your life and it can't take action.

So it's producing information for you, and people love that. I love that. I've got mine running, too. But I think the magic begins when you have actions and proactivity, because then it can begin speculatively actually detecting, “Hey, you just landed where you were supposed to go. I'm going to call a cab for you.”

Or, if you're at work, it's like, “Hey, I proactively ran this analysis because I saw your metrics dropped.” So I think these things really compound, and we need to nail multiple of the building blocks to really achieve the transformation and the form factor that we hope for.

8. Why Coding Agents Came First

Bill Gurley

As you were answering those questions, I now have 15 more questions for you [laughter], so I hope you have 15 more minutes. But okay, one by one. We'll start with what you said on actions and tasks.

Got it.

Brad Gerstner

But is there a shape or ordinality of tasks and/or agents that you think, “Hey, this is the kind of thing that’s likely to come first,” whenever it does?

The thing that’s already come first is the domain-specific agents, right? If you look at what’s happening in code, we’re fully there. It’s mind-bending, but we’ve got so many engineers who don’t open their IDE ever.

For me, as someone who used to code and then unfortunately got very, very busy, it’s brought me back in the game. Codex and products like it are clearly products that have escape velocity, where people are absolutely using them for all kinds of agentic work. If you just take what people are doing and make it work even better, you kind of get all the way there.

I won’t be surprised if you see this happen for other forms of quantitative knowledge work, just because it happens to have the properties that code has. It’s testable—you know if it worked or not—and it’s very RL-friendly. The domain-specific ones already work. I think the thing everyone’s working toward is general-purpose agents that just kind of work for anything.

Brad Gerstner

Yeah, and that’s why I think you need to win in consumer, because it’s very hard to train people into, “Okay, it can work.”

Deep Research was a consumer product, and it really was our first agentic thing out there. But I think what consumers want is, “I can just ask it anything, and it’ll do what needs to be done” without any sort of retraining. We’ll get there; it’s just a matter of time.

Brad Gerstner

At least a psychological goal is flight bookings, restaurant bookings, shopping—all this stuff. There are so many consumer problems, and those are just the type of things that you would kick off, right?

Yeah. The minute you have proactivity, there are things you don’t even think of as agentic tasks. You’re trying to get in shape; you don’t think of that as a task you would delegate unless you have a trainer, in which case you do, but most people don’t, right?

But if the AI knew that, it could totally start working in the background for you over very long periods of time and then say, “Here’s your fitness plan.” “Okay, I actually signed you up for this thing.” You could imagine it being quite helpful if it’s aligned with your long-term interests.

Brad Gerstner

You’re going to give Ozempic a run for its money. [laughter]

We’ve got to be careful what businesses we get into, but hopefully we can help.

9. Beyond Chatbots: The Super Assistant Vision

Brad Gerstner

That’ll be great. I cannot wait. Cannot wait. The second thing you said was proactive users, and that might require us to go beyond chatbots. What’s an example of a modality that might take ChatGPT beyond a chatbot?

Chat will always be close to my heart. It’s the way we grew up, and it’s an important modality to keep. I think it’s less about chat and more about natural language to me, where the fact that you can express yourself to the machine in ways that are very natural to you—whether or not that’s text, whether or not that’s voice, whether or not that is structured UI that is rendered by the model—is just very, very powerful, and that’s here to stay.

Brad Gerstner

SAS server?

That’s right. For those who don’t know, that’s the name of our codebase, short for Super Assistant Server, because it’s proof that this was always the vision and is always the vision.

The thing that’ll change, I think, is that chat is a great way of expressing your intent. It’s a good way of communicating with the machine, but it’s not a great output. In many cases, what you want back is an artifact, like, “Here’s your plan for your trip. Here is the analysis. Here is an outcome that I delivered for you. I just made you 5 bucks.” This is what I want my AI doing for me, right?

Brad Gerstner

Yeah, totally. This is what people care about, right?

Chat will always be there as the way that you disambiguate your intent and kick off the task, but I don’t think it’s necessarily the final deliverable. I think that’s the way in which we can evolve.

Hopefully that’s a very graceful transition, because I’m very lucky—and it’s hard-earned—to have a billion people coming to you weekly for a thing that they love.

Brad Gerstner

Yeah.

But I think it’s a great jumping-off point because we have so much unsatisfied intent from people, where they’re clearly trying to do something and chat is helpful enough, but it could be so much more helpful. I think that’s where we evolve.

Brad Gerstner

And you must be sitting on so much of this data, where people are showing up to chat and attempting—as you said, 3 years ago they were at least making the attempt.

Yeah. We do. We have really awesome classifiers that run automatically. It’s fully privacy-preserving but gives us a sense of what use cases people have, and it’s important, right? When you make a new model, you make a model update, you want to know what use cases just got better and what use cases got worse.

Brad Gerstner

Yeah.

That’s not always trivial to figure out unless you have really good analytics on the system. But so much of my learning is actually qualitative, where I have a habit of reaching out to a fairly random set of users to figure out what they’re doing.

I’ve never worked on a product where 3.5 years later you’re still learning every time, because usually by that time you know what the use cases are that your product can deliver on. But our tech is so unusual in the fact that I keep learning about something crazy I didn’t know was possible.

10. Power Users vs. Casual Users

Brad Gerstner

Wow, that’s awesome. But basically, a billion users—I suspect a small fraction of them are power users who are getting maybe thousands, maybe tens of thousands, in value from their $200 subscription.

The vast majority is middle of the pack, and then a few—call it casual users—who are using ChatGPT as search, maybe, or “Teach me about AI,” or “Help me with my homework.” What is our focus for each of those 3 factions: power users, casual users, and early users, or however you frame it?

Yeah. First of all, I feel accountable to our entire user base—in fact, our non-users too, because products like ChatGPT can have real externalities on all humans.

But when I think about the way we build, it’s really useful to imagine the extremes. One extreme is a user who doesn’t care about AI at all, who has a busy life and needs to be convinced of the value that we can provide, because that forces you to really nail the interface and expose the capabilities that are hidden in the model in a way that people can actually grok.

The other useful extreme is our power-user base, because power users are the users who teach us what’s possible. It’s actually impossible for us to do all the product discovery on our own simply because of how empirical this technology is and how much you actually learn post-launch.

So building for each of those extremes can be valuable. But our user base is incredibly diverse, and people have so many different use cases. This is why I like to look at all kinds of different segmentations—not just frequency, but also what use cases people are coming to us for. There’s definitely huge variety in the ChatGPT user base.

Brad Gerstner

Yeah, I look to macOS, for example, as an example where it really works for people who don’t understand technology at all. It’s entirely magical, but if you’re a power user, you’ve got Terminal and Settings; you can configure almost anything in macOS.

It’s really beautifully done, where the complexity is progressively disclosed. You can interact with it and love the simplicity of it all, but you’ve also got all the knobs, and developers love it, right? I think this is kind of the inspiration for how we want to be in ChatGPT.

That doesn’t mean we always live up to it, but it means that building for power users is extremely important. That’s not just a property that I think is aesthetically exciting. It’s also really important in AI, because power users show you what’s possible.

They’re actually doing the product discovery, because it would be impossible for us, with such empirical tech, to do all the product discovery on our own.

The type of user who subscribes to ChatGPT Pro, who used Codex before it quite worked, who is now the strongest advocate of cool tools, tokens, and teaching us what’s possible—that is an incredibly valuable member of the community.

It might not show up in your weekly active users as just one number, right? This is exactly why there isn’t a single North Star, and you really need to take these different segments very seriously.

I love building for power users. You asked about token consumption, et cetera; it’s so fascinating to see that there are people who get incredible value out of these products, and watching what they do is very informative.

Okay. So we're very focused on the entire user base. We learn a lot from the power users. The other thing I might say is that the power users right now are getting a lot of value—almost too much value.

Brad Gerstner

No such thing.

No such thing. The analogy that is most common is Uber and Lyft in the 2015 era, right?

11. Why ChatGPT Pricing Has to Change

Brad Gerstner

Right. And you know, it took a while, but I know you were thinking about it a lot. I know you guys are thinking about pricing quite a bit.

Yeah.

Brad Gerstner

Maybe tell us a little bit about pricing. Right now, pricing is pretty simple. Is there a path for folks who are getting a lot of great value to price that product differently and meet them where they are—and, on the other side?

I mean, pricing—there's no world in which pricing doesn't significantly evolve when the technology is changing this quickly. ChatGPT originally was entirely free, and the reason for that was that it was intended to be a demo, and we were going to wind it down after a month. We then realized that the demo went viral, and people loved the demo, and it was actually a product. But we realized that, to be a product, you can't take the product down every time you're at capacity.

So we shipped subscriptions simply because they could shape the demand. It was a way of gracefully turning users away when we had to turn someone away, and it felt like the fairest and most equitable way of doing so was saying, “Hey, if you really need this product, pay a subscription fee and you've got it.” Then we figured out how to make the product stable, and we had the choice: Do we keep the subscription thing, or do we go back to free? We realized we consistently had more technology that we couldn't scale.

GPT-4 was the first example, because we had way too many free users to serve GPT-4, and we put it behind the Plus plan. The way we stumbled into subscriptions was sort of accidental, by trying to just solve for the user. It felt like the right way at the time to provide maximal access to our technology.

Since then, we've had so many other breakthroughs, including test-time compute, where you can scale up intelligence as much as you want, more or less. It took us, and the entire industry, a little bit of time to turn that into product value, but we're here now, where our power users want to use more and more and more intelligence.

It's possible that, in the current era, having an unlimited plan is like having an unlimited electricity plan. It just doesn't make sense, because people may need a lot of electricity and they're getting a lot of value out of that. There's a reason you can't buy that, right? Obviously, I want to be really thoughtful about the way that we evolve our plans, SKUs, and subscriptions, but you would be incredibly surprised if they didn't change, given the magnitude and profoundness of the technical breakthroughs that we've had and the product breakthroughs that follow.

Brad Gerstner

Yeah. And, relatedly, I imagine you're going to have something for the power users.

Mm-hmm.

Brad Gerstner

What about the other side? How do we get the casual users into the funnel and still monetize them?

As mentioned, our business model will evolve, and the North Star is access. We'd like to provide an offering that maximizes the number of people who can access our most powerful tools. I think for the longest time, that has been subscriptions.

Subscriptions have the downside that, in many markets, people don't have credit cards or don't use credit cards to subscribe to software. We're interested in other ways that can maximize access to the technology. Our ads pilots are in that spirit. We really view it as a tool for bringing ChatGPT and our intelligence, most broadly, to anyone around the world. It is an example of how we constantly need to evolve and figure out the best way to bring the demand in line with what we're able to offer.

Brad Gerstner

Makes sense. The ads piece has been a tricky one because Sam has historically expressed reluctance about ads, and we've got to maintain a lot of trust while delivering that. So I guess what changed?

I think we've talked about this several times in my history at OpenAI, and every time it came up, we said, if we were to do ads, we'd have to be really thoughtful about the way we do it. The first thing we did, starting at the end of last year, was to really engage the company on this question: If we put ads in ChatGPT, how should we approach it? What should the principles be? How do you preserve the things that are magical about ChatGPT while getting the benefits of ads, which is our ability to bring our most advanced technology to anyone, regardless of their ability to pay?

I really love where we ended up on the principle side. On the experience side, we're very, very early. But on the principle side, I feel really proud, because it's very important that ChatGPT's answers be independent, as an example. Respecting user privacy is very important, and there's a lot to learn from the way that technology has evolved over the last few years—or really the last decade. I like that the principles are out there before we've even really gotten started. We're very early with our pilots.

It's kind of interesting. I was obviously very anxiously and eagerly looking at our support inbound data, and the most common inquiry about ads is not, “How do I disable ads or turn off ads?” but, “How do I run an ad?” The entire ecosystem is really excited to be part of the story and to figure out a way to talk to ChatGPT users. There's a lot more to come, but I'm very eager to get this right.

12. Partnerships, Distribution, and Product Tradeoffs

Brad Gerstner

Yeah. I'm sure you guys will. Switching gears, Nick, something you and I have spoken about a little bit is distribution and partnerships. There were a couple of big partnerships last year: Apple and Reliance with Gemini. Those are 2 big user bases, right? A lot of India, a lot of iOS users. Tell us a little bit about how you think about partnerships for ChatGPT to meet the user base, and maybe specifically on those 2 as well.

Look, I think partnerships are a great way to bring 2 products together and expose something like ChatGPT to people who might not otherwise have encountered it.

The thing I care about most when considering something like a partnership is: What is the user experience, and can we make it amazing? At the end of the day, when you look at what's going on in the market, you can get users to click on things, you can get them to tap any sort of product, especially if it looks like a product they recognize. But if the experience isn't truly awesome, people will churn, or at least not retain in the way that we've been lucky to retain them on ChatGPT.

For that reason, I'm super interested in paths like that. But it needs to be great. It needs to accrue to the user. We are very lucky to have a great brand and a recognizable product for many, many folks, and I want to make sure that anything we do is accretive to all that.

Brad Gerstner

Nick, you're a master of trade-offs. You must be making a lot of trade-offs right now. Tell us about some of the trade-offs you're making. Tell us about a trade-off that you might be making that people don't appreciate from the outside.

There are a lot of trade-offs indeed, and for different reasons. What I encounter a lot is trading off delivering on the use cases that exist in the product today and making them better versus productizing step-change technology that's going to generate a whole other set of use cases.

Because when you think about how ChatGPT came to be, it was a totally open-ended product. It was basically a user experience around a technical breakthrough. We couldn't have told you all the ways that people find it valuable, but putting it out there was really important because it allowed us to discover, and the world to discover, what we can do. Then, post-launch, we can obviously very systematically go and improve on the things that people actually want to use it for.

When you're at a company in this moment where you both have such amazing traction with what exists today and the most mind-bending breakthroughs on the research side, the balance you have to strike is making the core product you have better today with all the things that matter—latency, reliability, and making the use cases really great that people come to it with—while providing access to the step-change technology.

We try to get the balance right, but we're a small team and we don't always get it right. For that reason, it's one of the most difficult trade-offs that I have to deal with.

13. GPUs, Scarcity, and the Cost of Scaling AI

Brad Gerstner

Nick, I imagine one of the hardest trade-offs you guys make here is those GPUs that are melting between ChatGPT, Codex, and research.

How do you guys allocate the GPUs?

That is a very good question, and I'll let you know when I figure it out. Just kidding. We've gotten a lot better at this. I really hope, by the way, to be at a point one day—and I've yet to reach that point—where we don't have to face this trade-off, because it's really painful to have real user demand for products that you can't serve.

If you only ever worked in software, that's an entirely unusual dynamic, right, where you just weren't limited by this zero-sum resource out there. The marketplaces have it, but pure software doesn't really have that dynamic.

One thing we try to do, obviously, is prioritize our existing users first. We want to provide a fast, reliable product, and that is critical and table stakes. Then, when you look at new capabilities, the naïve business-school thing to do would probably be to look at incremental revenue per GPU or something like that.

But this is where it's more an art than a science, because we often have new breakthrough capabilities that are entirely zero-to-one. Deep Research was one of those. We couldn't have told you whether there was going to be consumer demand for a research product, but if you don't productize it to find out, you will never know.

This is where we have to be a little bit thoughtful about how we balance things that are no-brainers, that people are really going to love, with things that are brand-new ideas. Obviously, on the research side, there's a reason that Mark has the job he has, because a big part of his job is figuring out what research to fund, and obviously, GPUs are a big part of that. It's a very nuanced topic that we're continuously getting better at, but for me, the priority is always on our users.

Brad Gerstner

Yeah. The other takeaway that I had is you don't have line of sight to a time when you won't have that problem.

It's been so fascinating because we've obviously been incredibly lucky to encounter more and more users who want to use our technology, but the value that we're able to provide for each user is going up as well. GPU consumption correlates pretty well with that value.

When you just look at token consumption per user, especially in the enterprise, which is a massive opportunity, you see a lot of very GPU-hungry workflows. Demand keeps going up even as prices go down.

Brad Gerstner

This is a fascinating insight. People used to think that humans were a finite resource: you can't make more humans. Well, it takes 9 months and then 18 years. But you're saying that's actually a less finite resource than GPUs?

Yeah. On the human side, you can hire more humans, and obviously, we've been busy doing that and bringing the best talent across functions to OpenAI. In a world with agents, you can also get more leverage per human. You can make your humans very effective at their jobs and have them do more.

But GPUs are zero-sum, and if you don't have more GPUs, you really have to figure out how to make very, very hard trades. I hate making hard trades for users.

Brad Gerstner

Yeah.

Hence the desire to have more GPUs. But it's useful to start with the most zero-sum trade-off when you do your planning. I think starting by working backward from GPUs is usually a pretty good idea.

Brad Gerstner

Yeah. We have all these external data sources for charts of users, usage, activity, and retention—all those things. What we don't have is tokens per user over time. I bet that chart is like a sweet line going this way.

I think internal usage is pretty good. Our internal employees are a pretty good indicator of what's about to happen, and the charts are mind-boggling.

14. Shopping, ChatGPT as a Thought Partner, and Code Red

Brad Gerstner

Yeah, yeah, yeah. Fascinating. Okay, a couple of quick ones on the present before we go into the landscape, which is shopping. We just moved into a new house. We took some photos and were hoping that all our furniture would magically appear, with ChatGPT helping us place it. There have been a lot of recent updates on ChatGPT shopping. Tell us about it. What are you thinking?

On shopping, as ChatGPT as a shopping assistant, shopping is one of those use cases that exists organically in ChatGPT today, and it works. You can ask ChatGPT about any purchase you might be planning and get pretty excellent advice.

But it's also one of those cases where the experience that exists in ChatGPT today isn't the perfect experience that you would want, because shopping is very visual, for example. You're going to want to actually see products and images and be able to compare and contrast, not just read walls of text.

People care about the sources—where they can learn more about a given product, et cetera. There's a lot of work to do to make this discovery really, really good and allow people to use ChatGPT as an assistant to find the right product to buy.

That's where our focus lies: making that really great, and making it really great in a way that works for our retail partners as well. As I mentioned earlier, there's huge appetite from the ecosystem to be part of the ChatGPT journey. Nailing the discovery piece has been the most promising focus here to date.

Brad Gerstner

Nick, on ChatGPT, you must see a breadth of information. You must see a breadth of use cases that people are pursuing with ChatGPT. Tell us something about what the world underestimates about ChatGPT that has surprised you, or that a listener might be surprised by.

There's been a real change in the way that people think of ChatGPT over the last year or so. It's increasingly like a true thought partner to people. It's not just a thing that answers your question; it's a sparring partner that you can actually think things through with.

That shows up in all kinds of domains, ranging from life advice—if you've got a relationship problem, you can actually get a lot of value from ChatGPT helping you think through how to handle it and how to talk to your partner about it—all the way to a work setting, where you're working on an analysis, trying to figure out how to frame something, or trying to build something, and ChatGPT really shows up as a second brain of sorts.

I think that's qualitatively different in terms of the mental model it occupies with people. You see that in the usage patterns and the use cases that exist. The more we nail things like proactivity, which we talked about earlier, and tasks, et cetera, the more it's going to feel like a teammate in the workplace and like a super-assistant at home.

I think that's going to meaningfully change the use cases that people come for.

Brad Gerstner

Yeah. The most high-stakes thing I do with ChatGPT is that we have a new baby, and when the baby's crying at 3 in the morning, I ask, “ChatGPT, what's going on?”

First of all, congrats. Second of all, I've heard this from all the parents in my life. ChatGPT has become indispensable as a thought partner.

It makes sense, right? If you have a really specific scenario, or you think it's a specific scenario to you, ChatGPT really comes through and can help you build confidence. I think that's such an empowering thing.

New parents aren't always the most confident about what the right thing to do is, and if ChatGPT can make you feel like you have agency and control, I think it's really valuable.

Brad Gerstner

Yeah, it's huge. Well, thank you for making ChatGPT. It's literally getting me an extra hour of sleep every day.

It took a village. But that is a great metric. The North Star metric should be incremental hours of sleep.

Brad Gerstner

That's a great one: incremental hours of sleep, incremental hours of joy.

There you go. I mean, you joke, but we talk about this a lot, because spiritually, that is pretty close to what we hope we can do, right? Help you reach whatever you consider self-actualization—whether that's sleep, joy, or any other goal you might have.

Brad Gerstner

Yeah, yeah, yeah. Well, thank you to the village. We're going to switch gears and talk about the landscape.

Sure.

Brad Gerstner

There's a lot going on in the field. How would you frame ChatGPT's differentiation to people out there? There's a lot of different products out there.

Look, it's the best time in history to be a consumer of technology.

Brad Gerstner

It is indeed.

You've got options, and the competition is intense. I think that's beautiful, and it's actually good for us too, because if you were to premortem why a company like OpenAI does not achieve its mission, it's probably focus, because of the sheer number of opportunities that become possible when you approach AGI, right?

Having competition and options out there forces us to focus on our customers too, and on the things that really matter, which aren't always the most flashy things. Sometimes it's latency, reliability, or the quality of the user experience.

So, I think it’s a really good thing.

Brad Gerstner

Yeah.

I think the biggest differentiation of ChatGPT is the team behind it because we’re not static, right? Anything we build will get copied, sometimes in ways that are high craft and sometimes in ways that are just checkboxes. It’s really important to us that we evolve the category and build the super assistant that we’ve always imagined.

The reason I have confidence that’s possible at a speed that outpaces the dynamic of being copied is that we have an amazing team. We have an amazing team across research, engineering, design, and all the different functions it takes to make something amazing. I think our unique ability has been to bring those functions together to build something that’s at the intersection of useful and possible.

Brad Gerstner

Right in that moment.

So, my best answer for you is that we keep pushing forward and hope to expand what people think of this product as. Last winter, we had what was called Code Red. Google had a great model, and there was a lot of talk about it. Marc Benioff switched very vocally to Gemini, and we delayed ads, health agents, and shopping—basically hit pause on everything to make ChatGPT better.

Brad Gerstner

Talk to us about that moment, both what led to it and what was happening at the time.

Yeah. First off, Code Reds are a tool we use to create focus. As you can imagine, when you’re in a place like OpenAI—and this is what makes it special to work here—there are so many different things going on. It’s a research lab, and we’re pursuing many different ideas, right?

There have been moments when we’ve wanted the company to come together to solve a problem across boundaries, no matter what your project might have been. At the end of last year, we had one of those moments when we felt like we needed to show up for our users and focus on the basics, such as reliability, performance, the way talking to the model feels, and making personalization really great. All these elements are things our users care about, and I loved it because it was an opportunity to work with a bunch of people I don’t normally get to work with on making the product great.

We just exited the Code Red, which we knew we would, with the launch of GPT-5.3, which is a great model for the everyday user—it’s great to talk to—and GPT-5.4, which is a workhorse if you’re trying to do real knowledge work. Undoubtedly, we’re going to continue to use the tool of a Code Red whenever we want to create focus, but I’m excited because I think ChatGPT is in a great spot.

Brad Gerstner

Yeah, so Code Red is over now?

That’s correct.

Brad Gerstner

It’s not the new normal?

It’s not the new normal. We want it to be a special thing, but it is a tool I suspect we’ll continue to use.

Brad Gerstner

That’s great. Maybe tangibly, if you were to point to how Code Red changed ChatGPT, or how the team operates, what would you say?

The thing I try to foster with the team is focus. We’re certainly more focused than we were 6 months ago on the things we really want to nail, and some of those things are very behind the scenes, like latency and reliability.

Brad Gerstner

Okay. And some of those things are very considered efforts, like evolving ChatGPT into the super assistant.

So, focus is the main lasting artifact. As you can imagine, it’s hard to stay focused sometimes when there’s so much going on in the space, but that’s the hard job. You asked me about trade-offs earlier, and getting the team to focus on the things that really matter to users is certainly one of them. That’s always worth it.

Brad Gerstner

Yeah. In the back of my mind as I ask you that question is all the other founders who are in the arena right now. It’s a reminder that Code Red is a tool for you. Wartime-peacetime balance, as we used to call it, is a tool.

Yeah, I think every company does it differently in terms of how you get stuff done. But I think it’s really valuable to have terminology that means something, that signals to people that it’s okay to drop their other stuff and focus on this thing together, even if that wasn’t your original job.

Brad Gerstner

Yeah.

So, I think it worked really well at a place like OpenAI. But I imagine startups would have an equivalent.

Brad Gerstner

Yeah. One of the things that caught everybody’s imagination on our team was what Peter was doing at OpenClaw.

Mhm.

I’m very excited for Peter to be here. I was excited to have another German speaker in the house. He’s Austrian, I’m German, so we were exchanging Guten Morgens.

OpenClaw is so inspiring because it brought to life, in many ways, a vision that we’d had in different forms—admittedly—around this kind of AI that is fully embodied, exists across different UIs, can do stuff for you, has state, and has an interaction pattern that feels a little bit more like talking to a human. OpenClaw allows you to interact in a very natural way, where you can send many texts back and forth and it’s very curt.

There are a lot of elements of OpenClaw that I think were very clarifying to folks across the industry. But the best thing is that I’m super excited to learn from Peter, bring him into the company, and figure out what we can do together. There’s a lot more to come.

Brad Gerstner

All right. Now on to the most fun section: rapid fire.

All right.

Brad Gerstner

You ready?

Sure.

Brad Gerstner

We’ll start with my favorite game, which is long-short. Pick an idea, a startup, a business, or a product that you love and think you’re very bullish on.

Yeah. If I were starting a company today, I’d be really excited about companies that are going into other companies and getting extremely hands-on, effectively doing professional services with AI, because we’ve saturated all the emails and you need to get proximate to the problems. Those are the companies I’m paying attention to.

Brad Gerstner

Fascinating. This would be an example where you’re either acquiring or going inside an operating firm that has scale and a humming engine.

Exactly.

Brad Gerstner

And making that a more efficient engine.

Yeah. Or you’re doing contracts for customers that have really hard problems, and you’re actually going in and committing to solving the problem.

Brad Gerstner

Outcomes.

Yeah, because there’s a reason I think we made so much progress on math and coding but not on many other domains: those are domains we are proximate to, as people who work in labs. There are all kinds of other domains that we are not as proximate to, and if you get proximate, I think you can build something transformative.

I think this is more important now precisely because the easy problems have been solved. The obvious problems have been solved by the models.

Brad Gerstner

Credit where credit is due: NotebookLM is awesome and differentiated, and it helps me learn new stuff. I think it’s great.

It’s so good.

Brad Gerstner

Yeah, it’s so good. I think this is an example of how you can innovate and build something totally different. It’s awesome.

Yeah, yeah, yeah. It’s so good.

Brad Gerstner

Particularly for some more technical learning, I’ve found it to be a very approachable way to totally learn. It’s really cool. I feel like an underrated capability of AI is to just transform things into a different medium.

Mhm. I think that’s so important for learning. We just launched these dynamic math blocks, which allow you to visually understand math inside ChatGPT. Learning is obviously a big use case for us, too.

I think just being able to transform things from text to visual, and soon from visual to video, across all these different media is amazing because people have such different ways of processing information. Some people are auditory learners, some people are visual learners, and some people like reading. I think that’s really magical and a great angle to take.

Brad Gerstner

Yeah. Amazing, amazing, amazing. One of the things I think about a lot is education, and education for kids who are in school now. The world’s changing so fast. I’m not sure our education system is changing that fast.

Yeah.

Brad Gerstner

What advice would you have for students who are in school now, who might have to adapt faster than the system around them might adapt?

It’s a really good question and something that I’ve thought a lot about myself. I think the most important perma-skill in this era is curiosity, because if the machine can answer all your questions, you better have good questions.

And the only way to have good questions, I think, is to pursue the things you were actually excited about from an early age and throughout your entire life.

Brad Gerstner

Yeah.

And I reflect on this because the only reason I'm here and working on this stuff is because I thought it was neat when I got nerd-sniped in the interview process, right? And it was like—

Brad Gerstner

That's right.

This is so cool. And so, no matter what you're doing, I think that's an important skill: to be curious and learn to stay curious. And I think I'm confident that if you foster that skill, you will know how to adapt to an evolving landscape of tools, AIs, and jobs. So that would be my advice.

Brad Gerstner

Yeah, curiosity has always been the power skill. Our friend Bill Gurley wrote about it in his book Running Down a Dream. I'll have to check that out.

Yeah.

Brad Gerstner

What is a job that gets more valuable, not less, as AI gets better, as AGI arrives?

Well, I think maybe the easy answer is being an entrepreneur, because it's the best time to build ever, in terms of being able to self-actualize your—

Brad Gerstner

Yeah.

—idea. But maybe one that is nonobvious is writing, actually. It's very important, and it's not because AI can't write. AI will become amazing at writing, just like any other domain, but because I think the skill of writing forces you to be very clear about what you have to say.

Even though prompt engineering is obviously going to go away, and has gone away to a large extent, the idea of expressing what you want to a machine requires you to be a pretty good writer and a very precise writer. So I would say that any profession that involves very clear writing and therefore thinking, I think, is well set up.

Brad Gerstner

Yeah, 100%. Honestly, I mean, this is the whole thing about slop, right? There's so much—

That's the other thing. I think there's going to be a permanent need for high-quality, trusted, authoritative content, and tools like ChatGPT can help you discover that content.

Brad Gerstner

Yeah.

But I think the need for amazing content is also here to stay.

Brad Gerstner

And final question: what was your AGI moment? When did you feel it?

I've had so many, honestly, and it's definitely not stopped. A few weeks or so after I joined OpenAI, GPT-4 had finished training, and I remember trying it out. It actually didn't impress me at all, nor anyone else that week, because it kind of didn't work. It's because we hadn't figured out how to post-train it.

I think seeing it go from, “Wait, is this really a thing, or was GPT-3 kind of it?” to, “Wow, actually, this is an entire step change,” with what felt to me at the time, when I didn't understand much about AI at all, like just some tweaks or a little bit of final-stretch work, was profoundly humbling. You can realize that it might not look like we are close to really powerful, useful AI, but we probably are.

And then the moment that really—there were 2 things that GPT-4 did that felt like AGI to me. One is that it could do poetry, and I didn't think it was possible for an AI model to do poetry. Fundamentally and philosophically, it just didn't feel like it was in scope.

The other one was that it could produce code that actually worked and compiled. My next moment where I stared at the ceiling just in awe was when I realized GPT-4 could simulate an entire computer terminal—a full computer with commands, et cetera. And I'm like, “Wait, how would this be imbued in a language model?” There have been so many moments since then, honestly. Reasoning was a moment.

One of the moments was when Mark and I were giving a demo of reasoning in front of the whole company. This was a moment where we were still trying to find use cases that were hard enough for the AI for the reasoning to make a difference. We're way past that point now, we know, but at the time, I think we had to do a puzzle in front of everyone.

One of the moments that made me totally feel the AGI was when we were in the middle of the demo and everyone started laughing. I was like, “Wait, what is funny?” Then I stared at the screen because we were showing the chain of thought as it was streaming out of the model. The model swore and said, “Oh, damn it. Maybe I have to adjust because I realized I had made a mistake in the puzzle.”

The fact that it did that, but in particular the fact that it did that in a way that was entirely emergent from the RL process, completely blew my mind and made me feel quite humble about what else these models might be able to do. So that was one of those moments.

Brad Gerstner

Yeah.

And then, most recently, watching people use Codex—watching people walk around with their computer open because they don't want the task to end. Watching people who have never coded in their life make stuff and bring ideas to life feels like AGI.

Honestly, it just keeps accelerating for me, and it doesn't wear off at all. Everyone has a different thing, obviously, but those were some of mine.

Brad Gerstner

Yeah. You know, 10 years ago there was a product called Kite. I don't know if you remember. It was for software engineers. It was like an AI coding product.

Mhm.

Brad Gerstner

That's when I felt the hunger for personal AI, and nothing happened for 10 years, and then everything happened in the last 10 months.

The timing thing is really hard because it's actually quite possible to predict where things will end up, I think, in terms of the kind of product form factors you're going to have. But to know when it happens, it's really hard for me to make statements on anything between eventually and in 3 months.

Brad Gerstner

Yeah, because of all the ambiguity around—

Well, that's a tight enough window, you know. Now and 3 months is a tight enough window.

Brad Gerstner

3 months is pretty okay. Try to stick to the 3-month plan, more or less.

Though my team would probably tell me we don't, but I try. But yeah, anything in between 3 months and eventually is difficult.

Brad Gerstner

Yeah. Yeah. Yeah. Well, thanks for doing it. You've got a lot going on. This was a total treat. We're so excited to see all the great products you release for us. If we can do anything to be of help, let us know.

Awesome. Thanks very much. Thanks for having me.

Brad Gerstner

Of course, man. This was fun. As a reminder to everybody, these are just our opinions, not investment advice.