ChatGPT – The Super Assistant Era | BG2 Guest Interview

Key Takeaway

ChatGPT started as a demo meant to run for one month but became a billion-user product through principled decisions focused on long-term retention over short-term revenue. The key insight: when building AI products, focus on solving real user problems first—revenue follows naturally. OpenAI prioritizes retention by giving away better models (like GPT-4o) for free, knowing that providing access to superior technology creates lasting value and loyal users who return consistently.

Episode Overview

Nick Turley, VP of Product at OpenAI, discusses ChatGPT's explosive growth to 900 million weekly active users and the principles behind building consumer AI products. The conversation covers retention strategies, the evolution from chatbots to proactive AI assistants, challenges in product discovery with empirical technology, and the future of AI agents that can take actions on users' behalf. Turley emphasizes learning from power users, progressive complexity disclosure, and prioritizing user value over immediate monetization.

Full Transcript

Transcript of ChatGPT – The Super Assistant Era | BG2 Guest Interview from Bg2. Auto-generated from episode audio; may contain minor errors.

Chat GPT 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 and but we realized to be a product you can't take the product down every time you're at capacity. So we you ship subscriptions simply because it could shape the demand. It was a way of gracefully turning users away when we had to turn away someone.

You know, you guys are at 900 million VT active users now, and that growth has been incredible. The next billion users, where they where are they going to come from? We've got about 10% of the world coming to us now. 90% left to go, right? There's so much more opportunity. Well, Nick, so excited to have you here. Thank you for having me, Peru. You've had uh quite the journey from Germany to the US for Brown. That's true. That's true. That's true. Most recently at Instacart delivering uh groceries in 30 minutes to now delivering AGI to billions.

I'm sure that was the plan all along. Yeah, clearly total master plan. Well, well, tell us about your journey. What how did you get to OpenAI? I know it's a fun fun story. And your your three and a half years or so at OpenI, how have they gone? The only through light in how any sort of employment decision has been entirely people based. So I don't claim any credit for for uh joining OpenAI um or predicting chat GPT or anything like it. But um I uh um hit up someone who I admire a lot who I got got to know at Dropbox.

um Joanne who uh worked worked here at the time and she uh I asked her to get off the Dolly 2 wait list and she told me I had an interview if I wanted to get off the wait list. So um I took the bait and got totally nerd sniped in the process and and here I am. There you go. The Dolly 2 weight list will get you. It's a great recruiting tool. Nice, nice, nice. We should do more weight lists probably. Yeah. Yeah. Yeah. Well, you know, the big uh the big super cycle we're in is is is Chad GPT.

Now, I assume over a billion users on the monthly side, 900 million weekly active users that recently reported up from zero three and a half years ago. Uh you could have, if I imagine what the dashboard of Nick Turley looks like, it could have users, it could have paying subscribers, it could have daily active users, it could have retention, engagement. I mean, there's like 15 things, maybe all of them. What what is your northstar? How do you how do you op what are you optimizing for?

Uh what is Nick looking at in his daily dashboard? It's it's funny, right? Because it's such a young product. It's been to your point three and a half years. And um this kind of question, it kind of changes as as as you evolve and you and you grow up and you ask yourself, you know, what are we really building here? And uh to this day, right, then want to build a super assistant that can actually help people achieve their goals. goals. goals. And ultimately the thing we care about is like is our product doing that?

Is it actually helping you do the thing that you're, you know, um coming to the product to do? And it's so different for different people, right? Some people um are are trying to get healthy, other people are trying to start a company, um um learn a new topic, um do their taxes. There's all these different things that you might be doing. Um, and the true measure of success is whether or not that we're helping you do that. And um, obviously we look at WAU in particular because, you know, we want to know if you're coming back to the product.

We look at retention. Um, but we, you know, we we look at, um, all kinds of stuff in aggregate because really there isn't like this one single thing that you can optimize for. optimize for. optimize for. If you were to allocate a 100 units of points points points to these metrics, which metric can you like distribute the 100 units across these metrics in order of importance for you right this second? It's a good question. I I care a lot about long-term retention and I would put all my points there.

Um because um I'm really proud of the retention stats we have. Huge, Huge, Huge, but ultimately the sign of durable values, whether or not people are coming back in three months because that means you're really solving their problems. And And And yeah, yeah, yeah, I think things like revenue, they follow from that. Um yeah. yeah. yeah. Um versus like, you know, trying to go on those things directly. And we've we've had a lot of success making very principled decisions u on this stuff. Like one good example is you GPD4 used to be behind a pay pay wall because we couldn't serve it to everyone and then we had GPD4 which was a total breakthrough breakthrough breakthrough in um in in our ability to inference it and um and um and um so we just gave it away for free and that ended up being totally revenue positive and retention positive because it just provided access to the tech and I think when you make your decisions that way and you focus on the customer you end up with a great product and revenue obviously follows too.

Phenomenal. Phenomenal. Phenomenal. Yeah. Well, it uh it shows up in the numbers. You know, I I posted this chart uh yesterday on the data that we have, you know, from a third party. The retention curves for chat GPD are smiling. Look at that. Just like that. And that is a rare that is a very rare occurrence, you know, as as we know. And um why why do you think like if you were to give us a narrative on that smile curve? What is the why do these smile curves exist?

What are you seeing in Chad GPD that has people who have who have maybe turned off for a couple of weeks or months coming back and why are they coming back? Look, there isn't one single thing. You know, the way you build a retentive product is lots and lots of little things and really trying to make it better systematically. I will say that you know with AI and in particular chat GBT I found that it takes people some time to really understand all the parts of their life they can delegate right and I think many users for that it's a multi-month process for them to understand how can this thing help me and what are all the different ways that um I could plug chatbt into my life um and um but you know when I think about some of the breakthroughs and levers we've had um things like search and personalization they they have helped solve those user problems because um you know search provides way more daily value to you.

Um it used to be that chat was a pretty worky product. You know we'd see usage go down on the weekend. We usage you know go down during the summer months when a lot of people were off from work and um and um and um today you know we're we're mobile first. The vast majority is mobile and we see all these personal use cases and I think search was a big investment that got us there there there and personalization makes chat so much more relevant for you right because it gets to know you over time.

you get to know it. Um and um those are two things that have materially moved um the way that you know people come back to the product. But um there's lots more to do. Yeah. Yeah. Yeah. Um and you know, as mentioned, I don't I'm not resting on our um retention stats even though, um we're obviously very proud. very proud. very proud. Nice, nice, nice. Um and you know, the other thing that I I got wrong about Chad GPT was this is two two and a half years ago.

I was like, well, you know, let's look at who's going to win this consumer AI race. You typically these consumer markets are winner or take most winner or take all. Look at search. Google has near 90% plus market share. Three and a half, four trillion market cap. cap. cap. Mobile, same thing with Apple. Social, same thing with Meta. I was like, well, AI, Meta has all the distribution. Google's got all the distribution. They've got 34 billion users. Well, it would be a flick of a switch for them to roll out their AI.

But I was wrong. That's not what happened. Chad GPT turns out uh you know you guys are at 900 million VT active users now and um that growth has been incredible clearly distribution was not enough right so the same question for distribution what are the levers for us that have gotten us to the scale is it model quality is it you know product quality is it you know features is it the experience or product improvements like memory and personalization or search like like like same question like what would you say drove historical drove historical drove historical growth and and success We've got about 10% of the world coming to us now.

90% left to go, right? So there's so much more opportunity to to to re reach more people and introduce them to the way that AI can can can benefit them, right? But when I when I look backwards and I only say that because like the next billion users might be very different in terms of like how you um engage and reach and provide value, value, value, but when I look backward, it's been roughly sort of oneird one third one third between sort of classic friction removal type of work.

Like one of our biggest um um moments when you look at pure impact um was you know removing the authentication wall authentication wall authentication wall and Sam will say I told you so because I think that was his feedback from like day one day one day one u like can't you shouldn't have to log into the chatbt but it's like stuff like that that you do for any product and it does matter some things never change right right right um and but you know and then you know another third or so isort you know are what I would sort of sort core product investments and they're really typically things that we've done together between research and product.

So So So search and personalization are really good examples of that where we came together and we figured out not just UIUX evolution but also how to postrain um these changes into the model and it was really the moments when we came together. Um um another recent example is like we have these writing blocks that render when you you know ask about queries um where you're um you know trying to write with the model and like putting really good craft into those experiences really matters and um um you our users love it and then another third of of the growth has been just model improvements like step both step changes like going from um GBD 3.5 back then to GPD4 then going from GPD4 behind a payw wall to O suddenly available to everyone, right?

But a lot of it is also the iteration that isn't splashy, that doesn't warrant like, you know, a named release. You we um I'm really excited about the updates we just made um with 5.3, 5.4, etc. Because um that is when we like take a lot of user feedback and we you know, methodically address it and obviously that shows up um in our retention as well. So sort of one third one third one third between classic um friction removal um and access access access um core product investments and then pure model improvements.

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

First of all, just to contextualize that goal. You we care about about about um two things at the end of the day. Obviously, reaching more people is really important. It's the direct manifestation of our mission um to the world where you the more people we can introduce the benefits of AI uh the better that is. Um but we're also really excited to go deeper. Um and that means taking the same billion users that find value in chat GPD today. Um and actually providing more meaningful value in their world like actually helping them achieve their goals.

not just answering questions, right? questions, right? questions, right? Um, so I'll talk about how we get to more scale, but I think it's important to remember that, you know, the way this technology is evolving is, you know, we're going to go beyond pure chat bots um pretty fast. Exciting. Um I think on on scale you know it's shocked me how many people have found value in chatbt as it works today because I don't think delegation is a natural skill for most and chatbt is a pretty it's a it's it's a power tool right you come to 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 and then you'll learn about this prompt that was really cool and then maybe you you're on Twitter and you learn about another one or you're on Instagram and you learn another But the product it's it's like a raw appliance.

And I think to you know on one thing we really need to nail as we you know reach the next set of users is a product that has a bit more of an affordance. affordance. affordance. U because I think for most people they're very very busy. Um and they everyone I think in the world has intelligence constrained problems like problems that more intelligence could help with but you need to frame that to people. Yeah. Yeah. Yeah. And I still feel like we're a little bit too much like a computer terminal and it needs to feel more like software like or um you know an operating system of software, software, software, right?

right? right? Um so that's one thing. Um another thing um that gets at the same constraint is is beginning to be proactive. Um in a world where a lot of folks are, you know, 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. as well. as well. Um, but you know, I think all these are are product evolutions that we could make on 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 and chatb 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 chat GPT. GPT. GPT. But I fundamentally believe that reasoning it's transformative. And if you can figure out how to productize reasoning in a way that works on people's behalf without them even knowing knowing knowing um and that looks very much like you know know know 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. Um so there's so much work to do and and you know you know you know the the the product certainly has to evolve to to be relevant for for for this kind of scale. Yeah. One of the one of the things that I've been hoping for a while and you know Brad made a bet two years ago um when can Chad GBD help me take actions. Can Chad Guby help me be more proactive and um I think his bet expired end of last year.

So he's he's we're very curious. curious. curious. When is that coming? And I'll frame that for you because you know with with with with search engines in Google you know two 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 chat GPT. Uh and it feels like the next step is is actions actions actions 100%. 100%. 100%. And it feels like the next step is you know with you know pulse is a is a great proactive product.

proactive product. proactive product. Um that that that feels you know it's I have a pulse that runs weekly but what I would really like is like hey Nick spoke about something and just just find me make sure I know that Nick spoke about this or or like hey this XYZ thing happened that I cared about a lot. Um when is when is one of that going to get proactive? um what is the modality going to look like? Yeah. Yeah. So there's there's two concepts I think.

Um there there's chat should be doing stuff rather than just answering answering answering and then there's chat should be being proactive and I think when you put them together you start feeling like it feels like a super assistant. Um because I think these things compound um um um on the action taking piece you strictly speaking in chat can do stuff today. Um the action space is just very limited, right? It can search the web, which means it can use you search tool or browser in the same way that a human would.

Um it can make images. It can do all the all these things, right? But it doesn't have clearly doesn't have the same action space that a human with a computer would have. And that is what we aim to build. Um and if you timing is everything on these bets, right? And I don't pretend to be great at timing either. U you look at past attempts that we've made like the chatbt agent for example, which kind of has capabilities like this. um 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 is that users don't learn to trust it. They don't even try. So when you look at a lot of things people were doing in the original version of chatb agent, it was the things that happened to work like migrating your uh uh file server into the cloud or something like that. Useful stuff but very niche. Yeah. Um and um as this stuff gets better um 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 chatbt where chatbt upon launch was good enough to get real attempts at use cases even if they didn't initially work like chat 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 where 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 um you know once you have a a set of use cases that you can um climb the hill on we can make them awesome.

So on task I think we're close but I think even people inside of open would have had a hard time predicting exactly when this gets good. Um we've been excited about it for a while on um proactivity. Um Pulse was a really great first step because because because what we wanted to build was a form factor where you're not prompting the model like the model's prompting you. Um for the reasons that I described earlier, which is, you know, it's so hard for people to delegate and to figure out what their problems are.

What if the eye understood your your goals and the things you're interested in and just could start being proactive on your behalf? behalf? behalf? um 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 for you for you and people love that. You know, 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, you know, um detecting, hey, you just landed where you were supposed to go.

Um you know, I'm I'm I'm going to call a cab for you. or um you know if you're at work it's like hey I proactively ran this analysis because I saw your metrics dropped um so I think these things really compound and we need to nail multiple of the building blocks to really achieve the transformation um and the form factor that we hope for as you were answering those questions I now have uh 15 more questions for you so so so I hope you have 15 more minutes but um but um but um okay one by one we'll start with what you said on actions and tasks got it on timing tough to Okay.

But is there a shape or ordinality of tasks and or or agents that that you think, hey, this is the kind of thing that's likely to come first whenever it does? I mean, the thing that's already come first is the uh domain specific agents, right? If you look at what's happening in in code, we're we're we're fully there. You know, it's it's mindbending, but we've got so many engineers um who who don't open their IDE like ever. And right right right for me as someone who you know used to code and then unfortunately got very very busy it's brought me back in the game.

game. game. So um codeex and you know products like it is clearly a product that has escape velocity where people are absolutely using it for all kinds of agentic work and if you just take what people are doing doing doing and make it work even better you kind of get all the way there. Um, you know, I won't be surprised if you see this happen for other forms of sort 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, um, it's, you know, very RL friendly. Um, Um, Um, but um, uh, the the domain specific ones already work. I think the thing everyone's working for is, you know, general purpose agents that just kind of work for anything. Yeah. And I you know that's why I think you need to to win a consumer because it's very hard to train people into like okay okay okay um it can work like deep research was a consumer product and it's really was our first agentic first agentic first agentic uh thing out there um but I think what consumers want is I can just ask it anything and we'll do what what what um what needs to be done without any sort of retraining and um we'll get there just a matter of time at least a psychological goal is uh flight bookings bookings restaurant bookings, shopping, bookings, shopping, bookings, shopping, all this stuff.

Um there are so many consumer problems and those are just the type of things that you would kick off, right? right? right? Yeah. Yeah. Yeah. The minute you have productivity, there's there's things you don't even think of as agentic tasks. Um like you're trying to get in shape. You don't think of that as a task you would delegate. would delegate. would delegate. Um unless you have a trainer, in which case you do, but most people don't, right? But if the EI knew that, it could totally start working in the background for you over very long periods of time and and getting you, you know, um, 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 with your long-term interest. long-term interest. long-term interest. You're going to give Ozanic a run for the money. the money. the money. We got to be careful what businesses we get into, but uh, hopefully we can help. That'll be great. Cannot wait. Cannot wait. Uh the second thing you said was um you know pro proactive users and that might require us to go beyond chat bots. What's an example of a modality that might take chat GPD beyond a chatbot?

So chat will always be close to my heart. It's the way we grew up. Um, and it's an important modality to stay like I I I think it's less about chat and more about natural language to me where you know 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 you know um structured UI that is rendered by the model that is just very very powerful and that's here to stay but u I think the thing SA server SA server SA server that's right uh uh for those that don't know that's the name of our codebase um short for super assistant server Um because you know it's proof that this was always the vision and is always the vision.

Um but uh you know the the the thing that 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. Um where in many cases what you want back is an artifact like here's your your your you know plan for your trip. Here is the analysis. Here is um you know an outcome that I delivered for you. you know, I just made you five bucks. Like, this is what I want my AI doing for me, right?

Yeah, totally. I mean, this is what people care about, right? And and and and I think chat will always be there as the way that you sort of disambiguate your intent and you kick off the task, but I don't think it's necessarily the the the final deliverable. And I think that's that's that's the way in which we can evolve. So hopefully that's a very graceful transition because I'm very lucky and it's hard earned, you know, to to have a billion people coming to you weekly for a thing that they love.

they love. they love. Yeah. But I think it's a great jumping off point because we have so much unsatisfied intent from people where they're trying clearly trying to do something and chat is helpful enough but it could be so much more helpful. Um and I think that's where we evolve. Yeah. And you must be sitting on so much of this data where people are showing up to chat and attempting as you said uh three years ago they were at least making the attempt. Yeah. Yeah. Yeah.

So you might have at least the frequency histogram of like hey here are all the things that people want to achieve with us. We do uh we have like really awesome you know um classifiers that run automatically. It's fully privacy preserving but gives us a sense of you know what use cases people have and it's important right because when you make a new model you make a model update you want to know what use cases just got better what use cases got worse. worse. worse. Yeah.

Yeah. Yeah. Um and that's not always 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 will just you know I have a habit of reaching out to a fairly random set of users to just figure out what they're doing and I've never worked on a product where three and a half years later you're still learning every time because usually by that time you know what the use cases are that your product can you know deliver on but our tech is so unusual in the fact that I keep learning about something crazy I didn't know was possible.

Wow that's awesome. But basically a billion users, I suspect a small fraction of them are power users who are uh getting maybe thousands maybe maybe maybe tens of thousands of of of value on on their $200 subscription. Yeah. Yeah. Yeah. Um the vast majority is is you know middle of the pack and then and and a few call it casual users who are you know start using Chad GPD as search maybe or or or teach me about AI or or help me with my homework. Um what is your focus like maybe in those constituents power users, casual users and and early users or however you frame it, what is our focus on for each of those three factions?

Yeah. Yeah. Well, first of all, I feel accountable to our entire user base. In fact, our non-users too because you products like like Chachet can have real externalities on on all humans. Yeah. But when I think about sort of the way we build, it's really useful to imagine the extremes. Um, one extreme being a user who doesn't care about AI at all. Um, who has a busy life. Um, and um, um, needs to be convinced of, you know, the value that we can provide because that forces you to really nail the interface and to expose the capabilities uh, that are hidden in the model in a way that people can actually gro.

And then the other useful extreme is you know um our our power user base because power users are the users who teach us what's possible. Um possible. Um possible. Um um it's actually impossible for us to do all the product discovery um on our own simply by because of how empirical this technology is um and how much you actually learn post launch. So building you know um for each of those extremes can be valuable. Uh but our user base is incredibly diverse and um people have so many different use cases and this is why you know I like to look look at all kinds of different segmentations not just frequency but also you know what use cases are you coming to us for.

Um but um definitely huge variety in the HP user base. user base. user base. Yeah Yeah Yeah I look up to Mac OS 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 are a power user you've got terminal you got settings you configure almost anything in Mac OS and it's really beautifully done where the complexity is progressively disclosed disclosed disclosed so um you can interact with it and love love the simplicity of it all but you can also got all the knobs and developers love it right and so I think this is kind of the inspiration for how we want to be in chatbt that doesn't mean we always live up to it but um it means that building for powers is extremely important and you know that's not just a property that I'm think is sort of aesthetically exciting.

It's also really important in AI because it's the power users who show you what's possible. They are actually doing the product discovery discovery discovery because it would be impossible for us with such an empirical tech to do all the product discovery on our own. So the type of user who subscribes to Chad GPD Pro who used codeex before it quite worked quite worked quite worked who is now the strongest advocate of of cool tools tokens and teaching us what's what's possible. um that is an incredible valuable incredibly valuable member of the community and it might not show up in your you know um weekly active users as just one number right um but this is exactly why there isn't like a single north star and you really need to need need to take these different segments very seriously so um I love building for power users um um um and uh you know you you asked on you know token consumption etc it's so fascinating to see there's people who get incredible value um out of these products and u watching what they do is very informative.

very informative. very informative. Okay. So, so we're very focused on the entire user base. Um learn a lot from the power users. Um you know the other thing I might say is the power users right now are getting um a lot of value almost too much value and a lot of no such thing. No such thing. the the the analog that is most common is the Uber and Lyft of the 2015 era, right? right? right? And you know, it took 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. Yeah. Yeah. Um maybe tell us a little bit about pricing. Um pricing. Um pricing. Um you know, right now pricing is pretty simple. uh is there a path simple. uh is there a path for for for folks who are getting a lot of great value to price that product differently and and and you know meet them where they are and and and and and the other way on the other side I mean pricing is there's no world in which pricing doesn't significantly evolve when evolve when evolve when the technology is changing this quickly right chat GPT 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 a month a month we then realized that the demo went viral And people loved the demo and it was actually a product and but we realized to be a product you can't take the product down every time you're at capacity.

So we you know ship subscriptions simply because it could shape the demand. It was a way of gracefully turning users away when we had to turn away someone and it felt like the fairest and most equitable way of doing so is saying hey you know if you really need this product pay a subscription fee and you got it. Then we figured out how to make the product stable and we had the choice of do we keep the subscription thing or do we go back to free and we realized we had consistently more tech that we couldn't scale.

GPD4 being the first example because we had way too many free users to serve GPD4 and we put it behind the plus plan. And so, you know, the way we stumbled into subscriptions was was sort of accidental by trying to just solve for the user. Um, and it felt like the right way at the time um to to provide maximal access to our to to to our tech. Um, since then we've had so many other breakthroughs including test time compute where you can scale up um um intelligence um um kind of um as much as you want.

Yeah, Yeah, Yeah, more or less. And um you know, it took us, you know, in the entire industry a little bit of time to turn that into product value, but we're here now where, you know, our our our our power users want to use more and more and more intelligence. And you it's possible that, you know, in in the current era having unlimited plan is like having unlimited electricity plan. You know, it just doesn't make sense because like, you know, people may need a lot 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? So, um, obviously I want to be really thoughtful about the way that we evolve our our plans and SKs and subscriptions, but you would be incredibly sub, you know, surprised if it didn't change given the magnitude and profoundness of of the technical breakthroughs that we've had and the product breakthroughs that follow. Yeah. Um, and you know, relatedly, so so I I imagine you're going to have something for the power users. Mhm. Mhm. Mhm. Um, what about the other side?

How do we get um the casual users into the into the wheel and and still uh monetize them? them? them? As mentioned, you know, our our business model is will evolve and the northstar is access, right? We we we would like to pick a um way of of of of you providing an off we want to pro provide an offering that maximizes um the number of people can who can access our most powerful tools. I think for the longest time that has been subscriptions. Um, subscriptions have the downside of the fact that, you know, in many markets people don't people don't people don't have credit cards or they don't use credit cards to subscribe to software.

Um, and we're interested uh in other ways that can maximize access of of the tech. Um, tech. Um, tech. Um, our ads pilots are in that spirit. You know, we we really view it as a tool of bringing chatt and our intelligence most broadly broadly broadly to anyone around the world. M um and it is an example of how we constantly need to evolve and figure out the best way to to you know bring the demand in line with what we are able to offer. offer.

offer. Makes sense. Makes sense. You know the ads the ads piece is um is is has been a tricky one because you know Sam has historically expressed reluctance about ads and um you know we've got to maintain a lot of trust um with while delivering that. So, um, I guess what changed? I think we've talked about this several times in my my history at OpenAI, and every time it came up, we said if if we were to do ads, we'd have to be really thoughtful about the way we do it.

Um, so the first thing we did, you know, starting, you know, end of last year was to really engage the company on if we put ads in chat GPT, how should we approach it? What should the principles weigh? what be how do you preserve the things that are magical about chat GBT while getting the benefits of ads which you know is is is our ability to bring um our most advanced tech to to to anyone um regardless of their ability to pay pay pay and um I really love where we ended up on the principal side on the experience side um we're very very early but on the principal side I feel really proud because you know it's it's very important that the answer of chatbt be independent as an example respecting user privacy is very important and there's a lot to learn um from you know the way that tech has evolved over the last few years um or really last decade um um and I I like that the principles are out there before we've even really gotten started um like we're very early with our pilots um you know it it's kind of interesting I was obviously very anxiously and eagerly looking at um our support um inbounds and data and um the most common inquiry about ads is not you know how do disable ads or turn off ads, but it's like how do I run an ad?

Um because the entire ecosystem is really excited to be part of the story and to figure out a way to talk to chatb users. So, users. So, users. So, um there's a lot more to come. Um but I I I'm I'm very eager to get this right. Yeah. Yeah. I'm sure you guys will. Um switching gears, Nick, um something you and I have spoken about a little bit is, you know, distribution and partnerships. Uh there's a couple of big partnerships last year. Um Apple Reliance with with Gemini, those are two big user bases, right?

A lot of India, a lot of the iOS users. Um talk tell us a little bit how you think about partnerships for Chad GPT to to to meet the meet the user base. Um and maybe specifically on those two as well. Look, um I think partnerships are a great way to to bring two two two two products together and to you know um expose um expose um expose um something like chatt to um people who might not other otherwise have encountered it. encountered it. encountered it.

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

So for that reason you know I'm super interested in in paths like that. Um but it needs to be great. Uh it needs to acrue to the user. Um we are very lucky to have um a great brand and a recognizable product um for many many folks and I want to make sure that anything we do is accretive to um um to all that. Nick, you are uh you're a master of trade-offs. You must be making a lot of trade-offs right now. Uh um tell us about some Uh um tell us about some of the trade-offs you're making.

tell us about, you know, an a trade-off that you might be making that people don't appreciate uh from the outside. There are a lot of trade-offs um indeed and um for different reasons, right? Um um the what I encounter a lot is trading off off off delivering on the people on on the use cases that exist in the product today and making them better versus productizing you know step change technology that's going to generate a whole another set of use cases because when you think about how chatb came to be it was a totally open-ended product.

It was basically a user experience around a technical breakthrough. breakthrough. breakthrough. And 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 us to discover and the world to discover what we can do. And then postg um we can obviously very systematically go and improve on the things that people actually want to use it for. And 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, um reliability, um making the use cases really great that people come to with um you know providing access to to the step change um And um uh you we try to get the balance right, but we're a small team and we don't always get it right.

And for that reason, it's one of the most difficult um trade-offs that I have to deal with. Nick, I imagine one of the hardest trade-offs you guys make here is those uh GPUs that are melting between chat GPT, between codecs research. Uh how do you guys uh allocate the GPUs? That is a very good question. Um and I'll let you know when I figure it out. Just kidding. you know, we we've gotten we've gotten a lot better at this. Um, 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 user demand user demand uh for products that you can't serve.

Um, like you know, if if you only ever worked in software, that's entirely unusual dynamic, right? Where you just, you know, or you were limited by this zero sum resource out there. Um, Um, Um, the marketplaces have it. Um um but you know I think pure software doesn't doesn't really have the dynamic right. Yeah. Yeah. Yeah. So um you one thing we try to do obviously we prioritize our existing users um first we want to provide a fast reliable product um and that is critical and table stakes.

Then when you look at new capabilities the sort of na naive u business school thing to do would be to probably you know look at revenue incremental revenue per GPU or something like that. Yeah, 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 you know we couldn't have told you is there going to be demand for you know consumer demand for a research product but if you don't productize it uh um to find out um you you will never know.

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

Yeah. The other takeaway that I had is you can you don't have line of sight to a time when you won't have that problem. It's it's been so fascinating. Um because because because you know we obviously have been incredibly lucky to uh encounter more and more users who want to use our technology but then the value that we're able to provide for each user is going up as well and um you know GP or GPU consumption you know it correlates pretty well with that value that value that value um and uh when you just look at token consumption per user um especially in the enterprise too which is you know um a a massive opportunity um you see um a lot of very GPU GPU hungry hungry workflows and um um yes demand keeps going up even as prices go down.

down. down. This is a fascinating insight. People used to think that humans were you know you can't kind of make more humans well on a takes nine months and then 19 years but uh but you're saying well that's actually more more less finite resource than GPUs. Yeah, I mean on the human side um you can hire uh more humans and obviously we've been doing busy doing that and bring the best talent um across functions to OpenAI. Um in the world with agents you can also get more leverage per human um um you can make your humans very effective um at their job to do more but GPUs are zero sum um and if you don't have more GPUs you really have to figure out how do you make very very hard trades and hate making hard trades for users.

for users. for users. Yeah. Yeah. Yeah. um hence the desire to um um have more GPUs. But uh it's it's useful to start with the most zero sum trade-off when you do your planning. So I think starting working backwards from GPUs um is usually pretty pretty good idea. Yeah. You know, one of the we have all these external data sources for charts of users and usage and act activity and retention all those things. What we don't have is tokens per user over time. And I bet that chart is like a sweet line going this way.

I think internal is pretty good. Our internal uh employees is a pretty good indicator for what's about to happen. And um yes um the charts are are are mindboggling. Yeah. Yeah. Yeah. Fascinating. Uh okay, a couple of quick ones on the present before we go into the the landscape, which is, you know, shopping. Uh you know, we we just moved into a new house. We took some photos and we were hoping that all our furniture would magically appear that Chad Gibbly helped us paint. But But But you know a lot of uh recent you know a lot of uh recent updates on chat GPD shopping.

Tell us tell us about it. What are you thinking? Yeah. On on shopping as Chad GP as a shopping assistant. Shopping is one of those use cases that exist organically in chat GBT today and they work. Um you can ask chat GPT about any purchase you might be planning and get pretty excellent advice. Uh but it's also one of those cases where the experience that exists in chat um today is it's not it's not the perfect experience that you would want um because shopping is very visual for example.

So you're going to want to actually see products and images and be able to compare and contrast not just read you know walls of text. Um people care about the sources of you know um um um you know where can I learn more about you know a given product etc. Mhm. Mhm. Mhm. Um and um so there's a lot of work to do to make this discovery really really good and allowing people to use chat as a as a um as an assistant to find the right product to buy.

Um and that's where our focus lies is making that really great and making that really great in a way that works for our retail partners as well. Um because as I mentioned earlier, there's huge appetite from the ecosystem to be part of the the ChachiBT journey. Um and um nailing the discovery piece is has has been um the most promising um focus here to date. Nick on chat GPT you must see a breath of information. You must see a breath of uh use cases that people are doing with Chad GPT and and tell us something about you know what what does the world underestimate about Chad GPT that you have maybe been surprised by or or or listener might be sub surprised by?

there's been a real change in the way that people think of chat GPT um over the last year or so where um it's increasingly like a true thought partner to people um it's not just a thing that you know answers your question um but it's a thing that you can it's a sparring partner that you can actually think things through with and u that shows up in all kinds of domains ranging from life advice where you know if you got a relationship problem you can actually get a lot of value chat is helping you think through how to handle it and how to talk to you your partner about it.

um all the way to a work setting where you know you're you're working on um um an analysis or you're like trying to figure out how to frame something or you're you know trying to build something and Chad GPT really shows up as um as a um second brain um of sorts and uh I think that's qualitatively different in terms of how the mental model it occupies with people and you see in the usage patterns and the use cases that exist and I think the more we nail things like you know proactivity which we talked about earlier in task etc.

I think the more it's going to feel like a teammate in the workplace and like a super assistant at home at home at home and uh I think that's going to um meaningfully change the the use cases that people come for. Yeah. You know, I've been the the most highstakes thing I do with Chad GPT is uh we have a new baby uh we have a new baby and when the baby's uh crying at 3 in the morning, the morning, the morning, Chad GPT, Chad GPT, Chad GPT, what you know what's what's going on?

First of all, congrats. Second of all, I've heard this from all parents in my life. the chat has become indispensable indispensable indispensable indispensable as a thought partner and it makes sense right if you have a really specific scenario or you think it's a specific scenario to you chat really comes through and can um you know help you you know um um build confidence and I think that's such an empowering thing right like and I imagine new parents aren't always the most confident about what is the right thing to do and if chaty can make you um you know feel like you you you are you have agency and control and you know you know uh I think I think it's really valuable.

Yeah, it's huge. Well, thank you for making Chad GPT. It's literally getting me an extra hour of sleep every day. It took a village. But that is a great metric. You know, that should be the northstar metric is like incremental hours of sleep. That's a great one. Incremental hours of sleep, incremental hours of joy. There you go. I mean, you joke, but like we talk about this a lot and um because spiritually that that that is pretty close to what we we we hope we can do, right?

is is is help you re reach whatever you consider self-actualization. self-actualization. self-actualization. Yeah. Yeah. Yeah. Whether that that's sleep or joy or any other goal you might have. Yeah. Yeah. Yeah. Well, thank you for thank you to the village. We're going to switch gears and talk about the landscape. Sure. Sure. Sure. There's a lot going on on the field. Um you know, how would you frame Chad GPT's differentiation differentiation differentiation uh to to to people out there? Um there's a lot of different products out there.

Look, it's the best time in history to be a consumer of technology. It is indeed. Um because uh you got options and the competition is intense. And 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? and having competition and and options out there, I think um it it forces us to focus on our customers too um and on the things that really matter which aren't always the most flashy things, right?

Sometimes it's latency, reliability, um the quality of the user experience. experience. experience. So, uh I think it's a really good thing. Yeah. Yeah. Yeah. I think the biggest differentiation of Jet Beauty is the team behind it because, you know, we're not static, right? anything we build will get copied and you know sometimes in in in in ways that are high craft sometimes in ways that um you know are sort of um you know um check boxes and um it's really important to us that we evolve the category um and um build the super assistant that we've always imagined and I think the way the reason that I have confidence that that's possible at a speed that outpaces you know um um the the dynamic of being copied is that we have an amazing team.

Um, and that we have an amazing team across research and engineering and design and all the different functions that it takes to make something amazing. And I think our unique ability has been to bring those functions together to build something that is sort of at the intersection of useful and possible right in that moment. Um, so you know, my best answer for you is we keep pushing forward and we hope to be um um expanding what people think of this product as. you know, last uh winter we had obviously uh you know, what was called Code Red.

Google had a a great model. Uh there was a lot of, you know, talk about it. Mark Beni off switching very vocally to to to to Gemini and us delaying ads and health agents and shopping. Basically hit pause on everything, making tragedy better. Talk talk to us about that moment. Um both both about what led to that um and what was happening in that moment. Yeah. Um so first off code reds are a tool we use um to create focus and as you can imagine when you're in a place like open and this is what makes us special to um special to work here is is is is is there are so many different things going on it's a research lab we are pursuing many different ideas right and there's been these moments where u we've wanted the company to come together um um to solve a problem across boundaries you know no matter what your project might have And end of last year, we had one of those moments where we felt like we we need to show up for our users.

We need to focus the things focus on the basics. Um like reliability, performance, um the way that talking to the model feels um making personalization really great. um all these elements that you know our our users care about and um I loved it um because it was really an opportunity to work with a bunch of folks who I don't normally get to work with on making the product great product great product great and we just exited the code red um which we knew we would um with the launch of 5.3 which you know is is a great model for the everyday user um it's great to talk to and um 5.4 four which is um workhorse if you're trying to do real knowledge work and um you know undoubtedly we're going to continue to use the tool um um of a code red whenever we want to create focus um but u I'm excited because I think um chat GBT um is is is in a great spot spot spot yeah so code red is over now that's correct that's correct that's correct it's not the new normal um it's not the new normal uh we want it to be a special thing but it is a tool I I suspect we will continue to use that's great that's great and Um maybe tangibly if you were to point at how did code red change chat GPT uh or maybe the ops or or how the team operates.

The thing I try to get like you know foster with the team is focus. Um so we certainly more focused than we were six months ago months ago months ago on um you know the uh the things we really want to nail and some of those things are very behind the scenes like scenes like scenes like latency reliability latency reliability latency reliability those kind of things. Okay. And some of those things are like very considered efforts like you involving chatbt into the super assistant and um so focus is the main lasting artifact.

Um and as you imagine um um it's hard to stay focused sometimes when there's so much going on in the space but um that's the hard job and you asked me about trade-offs earlier. Um um getting getting the team to focus on the things that really matter to users is certainly one of them. Um that's always worth it. Yeah. you know, in the back of my mind as I ask you that question is all the other founders that are that are in the arena right now.

Uh and and just a reminder that hey, Code Red is a tool for for for you. Wartime at balance as we used to call it is is a tool. Yeah, I think you know every company does it differently um in terms of how you how you get stuff done. Um, but I think it it's really valuable to have terminology that um, you know, means something something something um, that, you know, signals to people it's okay to drop your other stuff and it's okay to, you know, focus on this thing together even if that wasn't your original job.

original job. original job. Yeah. Yeah. Yeah. Um, so I think it worked really well um, at a place like OpenAI. Um, but I imagine startups would have an equivalent. equivalent. equivalent. Yeah. You know, one of the things that caught everybody's imagination uh, on our team was uh, what Peter was doing at OpenClaw. OpenClaw. OpenClaw. Mhm. Mhm. Mhm. Incredibly potent to put all the tools together. Obviously, Peter's a great builder. Congrats on on on bringing on Peter to the team. Um, tell us a little bit about what what what Peter's working on and when when might the uh uh the billions on Chad GPT have something to to to see there.

Well, first of all, I'm very excited for for Peter to be here. Um, I uh um um was excited to have another German speaker in the house. He's he's Austrian. I'm German. So, we were we were um exchanging Guten Morgans. Um but the uh um yeah the the open claw is so inspiring because um it brought to life in in many ways um a vision that that that you we'd had um um in different forms know admittedly um around this kind of AI that is fully embodied that you know exists across different UIs that can do stuff for you that has state that has an interaction pattern that feels a little bit more like talking to a human, you know, because you um you open cloud allows you to you interact in a very very natural way where you can you send many texts back and forth and it's very curt and so there's a lot of elements of of of of OpenCloud that I think were very clarifying to to folks across the industry and um but the best you know I I'm I'm super excited to just like learn learn from Peter and bring in uh into the company and figure out what we can do together.

So there there's there's a lot more to come. All right. So, now on to the most fun section. Rapid fire. All right. All right. All right. You ready? You ready? You ready? Sure. Sure. Sure. Well, we we'll start with my most my my my favorite game, which is long short. Uh, pick an idea, a startup, a business, a product that you love, you think you you're very bullish on. Yeah, I'm I'm if I were starting a company today, I I I'm really excited about these companies that are going into companies and getting extremely hands-on and doing effect effectively professional services um with AI because we've saturated all the emails the emails the emails um and you need to get proximate to the problems.

So um that's it's it's those companies that that um I'm I'm paying attention to. attention to. attention to. Fascinating. So this is, you know, this is an example. This would be like, hey, you're going and either acquiring or or or going inside an operating firm that has scale and and and and and a humming engine. engine. engine. Exactly. Exactly. Exactly. And making that a more efficient engine. Yeah. Or or or just like, you know, you're doing contracts for for for for customers that have really hard problems and you're actually going in and committing to solving the problem.

Um outcomes. Um outcomes. Um outcomes. Yeah. because like you know there there's a reason I think that we made so much progress on math and coding um but not on many other domains because those are domains we are approximate to we as people who work in labs and there's all kinds of other domains that we are not as proximate to and if you get proximate I think you can you know build something transformative and I think this is more important now um precisely because um you know the easy problems have been solved um the obvious problems have been solved by the models models models credit where credit is due I think not notebook spoke LM is awesome and differentiated and helps me learn new stuff.

I think it's great. It's so good. Yeah, Yeah, Yeah, it's so good. I think this is the example of you can innovate and you can build something um totally different. It's awesome. Yeah. Yeah. Yeah. It's so good. Particularly for uh I found it for some more technical uh uh learning to be a very approachable way to to totally learn. And it's really cool. I feel like AI an underrated capability of AI is to just transform things into a different medium. different medium. different medium. Mhm. And I think that's so important for learning like we just la um um launched these like dynamic um math blocks which allow you to visually understand math inside chatb learning is obviously a big use case for us too and I think just being able to transform things from text to visual you know soon from you know visual to video and like all these different media is amazing because people have such different ways of processing information um and some people are like auditory learners some people are visual some people like reading uh so I think that's really magical Um and um a great great angle to take.

take. take. Yeah. Amazing. Amazing. Amazing. Uh you know, one of the things I think about a lot is education and and and education for for for kids now in school. Um the world's changing so fast. I'm not sure our education system is changing that fast. Yeah. Yeah. Yeah. What advice would you have for for students who are in school now? Um you know, 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 um myself um myself um myself and you know the I think the most important perma skill in this era is uh curiosity I think because if the machine can answer all your questions you better have good questions.

questions. questions. Um, 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. Yeah. Yeah. Yeah. And, um, 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, you know, nerd sniped in um, in the interview process, right? And it was like, that's right. that's right. that's right. This is so cool. And and so no matter what you're doing, I think that's an important skill is to be curious and learn to stay curious.

And I think I'm confident that um if you foster that skill um you will know how to adapt to you know an evolving landscape of tools and AIs and and jobs. Um so that would be my advice. Yeah, curiosity is has always been the poorest scale. Our friend uh Bill Gurley wrote about it in his book Running Down a Dream. Um but curiosity have to check that out. that out. that out. Yeah. Um, what what is a job that uh gets more valuable, not less, as uh AI gets better as as AGI arrives?

Well, I think maybe the easy answer is being an entrepreneur. Um um because it's the best time to build ever build ever build ever in terms of like being able to self-actualize your self-actualize your self-actualize your Yeah. Yeah. Yeah. your idea. Yeah, but maybe maybe one that is maybe nonobvious is I think writing actually um is very important and it's not because the AI can't write you know AI will become amazing at writing just like any other domains but because I think the skill of writing forces you to be very clear of what you have to say and even though prompt engineering is obviously going to go away and has gone away to much extent the idea of expressing what you want to a machine requires you to be a pretty good writer and a very precise um um writer.

So I I would say that that is a you know any profession that involves very clear writing and therefore thinking I think is is is um um well set up. Yeah, 100%. Honestly, I mean there this is the whole thing about slot, 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 CHAP can help you discover that content. Yeah. Yeah. Yeah. But I think the need for amazing um um content is is is also here to stay.

Uh and final question, what has been your AGI feel the AGI moment? When did you feel it? I've had so many honestly. Um, and it's it's definitely not stopped. Um, a few weeks or so after I joined Open AI, GP24 had finished training and I remember trying it out and it actually it didn't impress me at all. Um, nor anyone else that week because it kind of didn't work. And it's because we hadn't figured out how to post train it. And uh I think seeing it go from kind of wait is this really a thing or was GPT3 kind of it to wow actually this is an entire step change with what felt to me at the time who didn't understand much about AI at all as like just some tweaks or some a little bit of final stretch work stretch work stretch work was profoundly humbling because you can realize that you might it might not look like we are close to really powerful useful AI But we probably are.

Um, and then the moment that, you know, really, you know, there was two things that GBD4 did that felt like AGI to me. One is it could do poetry and I didn't think it was possible for AI model to do poetry. It just kind of fundamental philosophically, it just didn't feel like in scope in scope in scope and then the other one was it could produce code that actually worked and compiled. And then my next moment where I stared at the ceiling just in awe was when I realized GPD4 could just simulate an entire computer terminal um um like a full computer with commands etc.

And I'm like, wait, how would this be imbued in a in a in a language model? And there's been so many moments since then, honestly. Like reasoning was a moment. One of the moments was when I think Mark and I were uh uh giving a demo of reasoning uh in front of the um whole company. Um and um this was a moment where we were still trying to kind of find use cases that were hard enough for the AI for the reasoning to, you know, make it make a difference.

We're way past that point, we know. But at the time, you know, I think we were having to do a puzzle in front of everyone and um I think one of the moments that made me totally feel the AGI is like um we were in the middle of the demo and everyone started laughing and I was like, "Wait, what what is funny?" And then I stared at the screen because we were showing this chain of thought as it was streaming out of the model. Um and the model swore and said like, "Oh, damn it.

May I have to adjust because I realized I had made a mistake in the puzzle." And uh the fact that they did that, but in particular the fact that it did that in a way that was entirely emergent from the you know RL process completely blew my mind um and you know made me you know um uh feel quite humble about what else these models might be able to do. So um that was one of those moments. moments. moments. Yeah. Yeah. Yeah. Uh and then most recently watching people use codecs like like watching people have people have people have walk around with their computer open uh because they don't want the task to end.

Um yeah uh watching people who have never coded in their life make stuff and bring ideas uh to life that feel feels like an AGM. So honestly it just it's just accelerating for me and it doesn't wear off at all. Um and uh everyone has a different thing obviously but those are were some of mine. Yeah. You know it's uh 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. product. Mhm. product. Mhm. That's when I that's when I felt the hunger for for for for for personal AI and you know nothing happened for 10 years and then everything happened in the last 10 months. The timing thing is really hard because it's it's actually quite possible to predict where things will end up I think um 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 anything between anything between sort of eventually and in three months.

Yeah. because of all the ambiguity around, you know, um um well, that's a tight enough window, you know, now and three months is a tight enough window. enough window. enough window. Three months is pretty pretty okay. Try to stick to the three-month plan more or less. Um um though, you know, my team would probably tell me we don't, but I I try. But yeah, anything in between three months and eventually is is is difficult. Um difficult. Um difficult. Um yeah. Yeah. Yeah. Well, thanks for doing it.

You've got a lot going on. This was a total treat. We so excited to see all the great products you release for us. Um, Um, Um, we can do anything to be of help, let us know. know. know. Awesome. Thanks very much. Thanks for having me. having me. having me. Of course, man. This was fun. As a reminder to everybody, just our opinions, not investment advice.