OpenAI CFO 莎拉·弗里亚尔：IPO、AI 竞争、新设备与千亿美元算力支出

Jason Calacanis (00:00:00): OpenAI's CFO, Sarah Fryer. Chamath Palihapitiya (00:00:02): We gotta get right to it. You have just completed what I regard as the most successful fundraising round in history. We're gonna raise actually north of a $120,000,000,000. Sarah Friar (00:00:13): We think AI is the biggest era that we've seen to date. We're just starting to understand what it's gonna mean for global productivity, and with that, you know, hopefully more affluence, better lives for everyone. Luck is whatever the preparation meets opportunity, but you gotta grab it. Long time listener, first time caller. Quite exciting to get to hang out with all the bros here. Hello. David Sacks (00:00:39): We weren't sure how to start this off, but I thought the best thing was to allow our erstwhile cryptozar to maybe Sarah Friar (00:00:45): Of course. David Sacks (00:00:46): Save comments and David Friedberg (00:00:47): I saw an article today. I think it might have been in the Wall Street Journal that the perception is that there's an advantage to IPO ing earlier if you're an AI company. So now we know SpaceX is going, and then the question is when's when are OpenAI and Anthropic gonna go? And I'm curious, how do you think about that? Do you think there is a little bit of a race on or, you know, you haven't made a decision about that yet? Sarah Friar (00:01:12): Like, in the end, an IPO, I say this to the team all the time, it's a milestone. It is not a destination. Do not run your company as if that's some sort of destination. It's just another way to fundraise. We just did you heard me on the the sizzle reel, raise a $122,000,000,000 in March, and that was to give ourselves maximum flexibility. I feel like my job as a CFO is create optionality for this, not just this company, but just this era that we're living in. David Sacks (00:01:44): So, Sarah, was that that point in fundraising, is that the biggest private republic up until the SpaceX IPO? Sarah Friar (00:01:51): It is. Right. It is by orders of magnitude. I think the largest IPO to date was the Earth, Saudi Aramco, which was about $30,000,000,000. Jason Calacanis (00:01:59): Right. So it Sarah Friar (00:01:59): is actually incredible that you're going to have potentially three IPOs at a scale that will be bigger even than 2001, 2000 that that time frame, there was a lot that went on in the market too. But the market has grown. And by the way, the other thing going on in the market is like, if you look at buybacks, m and a, and so on, it's actually a lot of capital keeps being returned back to shareholders in cash. So there is a lot of money sitting on the sidelines. But in the spirit of, like, the question, David, I think in the end, you wanna you'll be measured. Right? It's the in the end, the market is a weighing machine, not a popularity machine. No one remembers who went first, Google or Yahoo, Lyft or Uber. And I say that not because whether I wanna be first or second, but I just think it, you know, the the press loves a bit of drama, but in the end, we're gonna have to build big, sustainable, durable companies. And fundraising will be a key component of doing exactly that. David Sacks (00:02:53): Sarah, breaking news. Oh my god. Sarah Friar (00:02:56): So many people coming out Jason Calacanis (00:02:57): with Breaking news. I know. Speaker 6 (00:02:58): It is it is hard balancing balancing four four interviewers interviewers at at the the same same time. Time. It's It's okay. This is my Sarah Friar (00:03:03): world by the So I'm Jason Calacanis (00:03:04): good with this. Jason. Anthropic just confidentially filed their s one. So does that mean you're third place in terms of the filing? Sarah Friar (00:03:13): It does not mean anything yet because you have to run now the gauntlet of the SEC and who knows how long that takes for anyone. Jason Calacanis (00:03:21): Yeah. Is there though a benefit to them going forward? And I think unpacking the rivalry with Anthropic is on everybody's minds. So, just, I guess you can't talk too much about IPOs. So, I'll just pivot to Anthropic was far behind and now they've really, I think everybody would agree in the industry now blown past OpenAI in terms of developers and corporations and it seems revenue. So did how did that happen at OpenAI when you had such a tremendous lead? How did Anthropic blow past you guys? Sarah Friar (00:03:57): So let's talk a little bit about a strategy. Our strategy is different. Right? So we are building the AI layer, the infrastructure, and it's really important that there's a single foundation, but then with many interfaces out into the world. So ChatGPT is one to the consumer. Over 900,000,000 people use ChatGPT weekly, and it's become the noun and the verb. It's how most people experience AI for the first time. Kind of fun fact, our economic research team just showed me the fastest growing continents now are Africa, probably not totally surprising since it started a small base. Fastest growing languages are Azerbaijani Kazakhstani. What is it's Kazakh. Which is kind of incredible to talk about where it's going. So multiple interfaces, ChatGPT. Of course, there's Codex just hit 5,000,000 over the weekend. We're really proud of that coming from almost zero in January. David Sacks (00:04:54): 5,000,000 users. Speaker 6 (00:04:55): Go Sarah Friar (00:04:56): Codex. Help me prepare for this little special up here too. There's of course Frontier, our enterprise offering, and everything every other way that we can get out there to reach businesses of all sizes. That is a very different strategy. We think that because it's served up on one model, there's a compounding element of advantage that comes from that. More users, more data, more ability to personalize, ChatGeePTS as a front door. As models get bigger, there's more efficiency that should lower the overall cost to give you a token in the world. That should compound to higher gross margins, ultimately more ways to pay for compute, and then access to compute is one of the really big competitive advantages at the moment. So, you know, we have to all run our own races, but we all have to recognize we're part of an ecosystem that also needs to bring people along collectively. Jason Calacanis (00:05:48): Did you spread a little bit too then too many projects? People were talking about this new gadget, Sora, and and then maybe not enough focus on enterprise. Is that a fair assessment of if there was a mistake in the last year? David Sacks (00:06:00): That was it? Sarah Friar (00:06:01): No. I I think that the world loves to go to binary isms. Like, are you a consumer company, Sarah? Are you an enterprise company? The reality is we're very much both. We're not one or the other. Right now, our revenue is getting pretty balanced about fifty fifty. We are incredibly focused on the enterprise. Like, I spend so much of my time with I mean, just even in the last week, I could tell you I've been to see Thermo Fisher in Boston, I was with a bunch of banks in New York, I was on the phone with Travelers on Friday, I spent this morning on the phone with a tech company. It doesn't matter the vertical, people are really moving on AI right now. Our new head of revenue, Denise Dresser, in seats since December. She is a force of nature. And so I think the enterprise broadly speaking is really firing on all cylinders. But we don't want to leave the consumer behind. Remember, our mission at OpenAI is AGI for the benefit of humanity. Not for the benefit of humanity who can pay or for the benefit of humanity who live in an enterprise, but very broad based. It's why we offer so much free because we want people to get a taste. Once they get a taste of intelligence, the ability to come up a commitment curve is incredible. Our free users do about seven turns, seven questions a day. Our first paid tier do double that, about 15. Our our real paid tier, the plus, $20, hopefully you're all on it or higher, about three x, and pro, about 11 x over a free user. So remember when you got your flip phone and you're like, yeah, I don't know, it does make some calls. Now, that same phone, think of all the things it does for you. That's the path we're on with intelligence right now. Sorry, David Sacks (00:07:42): You said something very influential. I think it was about eighteen months ago for a lot of us in the industry where you framed a very simple economic trade off, which was gigawatts to cash. And I think you said one gigawatt is roughly equivalent to about $10,000,000,000 a year of revenue to OpenAI. So comment number one was this one gigawatt equals $10,000,000,000 a year of revenue for you. But it's not just you because you can probably extrapolate that to Anthropic and other folks, Gemini. Yep. But then you were really at the forefront of getting access to power and data centers and powered land. It seemed a little crazy, but now it looks like, hold on, there's a huge deficit of supply. Can you just unpack all of that and explain Yeah. Both the spectrum of where we are and then those specific economics and if that's So Sarah Friar (00:08:30): first of all, yes, compute is a very scarce resource at the moment. Mean, I what we see in our business, we're going up that kind of vertical wall of demand right now, and there's just not enough tokens available. So we I'm very grateful that I got to work alongside Greg and Sam. I think we're very pressing on this. And last year, we were definitely taking some, you know, arrows in the back about why are they out there buying all this compute, and I think, thank God we did, because in '26, we still won't have enough compute. Where are we on the compute continuum? There's kind of choke points everywhere, and and I think they will continue to move back and forth. I mean, you all talk about this and know this as well as anyone here, whether it's energy, first and foremost, land power, how we get regulatory environments such that we can build quickly. When you get into the racks and chips themselves, clearly, do we have enough in that supply chain? Memory spike is is on at the moment. Access to great talent. Do we have enough people coming through our education system? I really worry about this right now. I'm a trustee at Stanford, and, you know, I see just that, you know, we need to keep the focus on education and science. And then trust. I mean, I actually put that as part of the supply chain. Sam right now is in Celine, Michigan. He's gonna be cutting the ribbon in about two hours, so you are getting a sneak preview, but they told me it was okay to say it in the room. That will be, you know, sticking shovels in the ground on a one gigawatt data center, which is part of our Oracle complex. Really important there on the trust side that we don't leave communities behind. I spent seven years of my life working at Nextdoor, doing the hard work of what it means to be local. And you cannot tell people from top down what they need, because they will tell you thank you, but no thank you. I will tell you what I need. And so in a data center like that, we're actually spending a lot of time in the community saying, number one, we're not going to raise your electricity bills. We're going to pay for our infrastructure and our power. Sarah Friar (00:10:32): It will not be the rate payer that has to pay. Number two, we're going to bring jobs, 2,500 union jobs, good jobs, like electricians, HVAC, and so on. We are going to pay our taxes, $1,000,000,000 in taxes just for that data center into Michigan. And on top of that, we're going to invest $45,000,000 going into education for Codex credits to do what you all talked about this weekend. It's like anyone who's not, like, coming in facile to their new job, I have teenagers using Codex, it would be like I would never hire a finance person who didn't know how to use Excel. And I pretty much probably wouldn't hire a finance person today that doesn't know how to use a tool like Codex. So that you know, so when I think about investment, we're having to invest ahead of demand. That means we need to both be able to find all of the compute and all the pieces and then pay for it. So that goes back to your capital question on IPO. And then on the other side on the economics, look, the economics do continue to get better. They're getting better on multiple fronts. I think we are doing a better job of actually showing true value to our customers. And I think you get beyond kind of a cost plus type pricing into something that feels more akin to the value being created. Now, scarcity of tokens helps because it's causing a bit of a compression in that David Sacks (00:11:50): in just, like, without specific names, where you know the landscape exists today in terms of all the power that's available and all the demand that exists across everybody. Yep. What's going to happen over the next year just at the current course and speed of what is available? Of the data centers that's available, of the tokens that's available, of the infrastructure that's available, for everybody. Because, you know, I told this story last week, but, you know, I'll use Anthropic, one of the frustrating things is at some point it just says, you know, 10:30. It's like, alright, Shmoth, see you at 02:30. Sarah Friar (00:12:22): Yeah. David Sacks (00:12:22): And that's not a viable experience. Right. And in fairness to ChatGPT, actually, I've never had that with. Sarah Friar (00:12:29): Yeah. We we're quite generous with our tokens. And again, on purpose, we're trying to drive access so people understand. If you're on that free tier, not actually getting the latest model, but we're trying to put it in your hands so you get a sense for it, by the way. Because, you know, if you're a kid doing homework, like, I think about when I grew up in the Encyclopedia Britannica's showed up at the front door in Northern Ireland in a tiny little community in the middle of the troubles, it was like the clouds parted. And so we wanna make sure that people get that feeling, by the way. But the landscape right now, in '26, if you wanna buy more compute, good luck to you. Like, tell me because I don't know where else to find it. I mean, as you know Elon has something. Jason Calacanis (00:13:11): What I Sarah Friar (00:13:11): was gonna say, Elon ironically ended up being the one person that had too much compute in a way, but good job on, like, figuring out how to sell that off. In '27, it's pretty limited as well, frankly. Now there's a couple of things shifting around. When we talk about compute, there's training that mostly still all happens here in The United States for USG reasons, for making sure that a national asset, in effect, is happening on US soil. For inference, we want that to be global. And I think particularly in an agentic world, you want much more kind of real time. Even for things like Sora and video, which by the way, yeah, we have you know, we had to make a really tough choice because we didn't have enough compute. Jason Calacanis (00:13:56): And we said a lot. Sarah Friar (00:13:57): Right now, yeah, video does. But video is not over. Like, in particular, when you start to think about where AI is taking us into more multimodality. So remember, we've all been taught by the last generation of technology to talk with our thumbs. It's a disease. You walk around, everyone's looking down, they don't look up anymore. Teenagers sit on my sofa at night and talk to each other with their thumbs. I'm like, who are you talking to? And my son will be like, him. I'm like, okay. Talk. Multimodality is here. Hopefully, I think you all talked about it this weekend. You're talking to your tool. I talk to Codex every day. And so that is changing rapidly, but that is gonna need much more kind of real time compute because it's an odd experience that I was talking to Jamat. Jason Calacanis (00:14:43): Johnny, have this puck Totally. These earpieces. So maybe tell us a little bit about that process. You've admitted it now. Sarah Friar (00:14:49): If I if I tell you it's an earpiece, Johnny will come and steal my teenage son. I might give it to him, Jason Calacanis (00:14:53): give him to But you you do believe that should be some Sarah Friar (00:14:57): changing into a consumer substrate that I cannot tell you what it is, but by the end of this year, we will unveil it. I have seen it. I've tried it. I am a hand talker. Right now, I'm sitting on my hands. Jason Calacanis (00:15:10): Did have a did have a paradigm shift? Speaker 6 (00:15:12): When when yeah. When you used it, was it like having an iPhone for the first time? Sarah Friar (00:15:16): It's very what Johnny and team are really good at is bringing humanity to devices. And I don't really know how to explain that well, but when you see it, you feel it. Jason Calacanis (00:15:28): It feels natural in some way? Sarah Friar (00:15:29): It feels very natural, but it feels very lovable. Really? And I can't really explain what that emotion is because Jason Calacanis (00:15:37): In so much some way in terms of because David Sacks (00:15:39): technology Not taking your Jason Calacanis (00:15:40): phone out and it's it's seamless is what I've heard from people who played with it. Sarah Friar (00:15:44): Technology is very can be very mechanistic, but we all know great design just makes everything fade away. Right? It's what at the time, you know, the simple is hard. Speaker 6 (00:15:56): Yeah. I I think this Sarah Friar (00:15:57): is a very Job said. Speaker 6 (00:15:58): This story, just going back to the earlier questions of putting on the CFO hat, help us understand the capital allocation model that you use. Because a lot of businesses over the last decade, two decades that have kind of been these outsized returners have found some unique way to deploy capital at a higher ROC than anyone else. Yeah. And then you end up plowing all your capital into that higher ROC bucket. Yeah. What is that for you guys, and how do you think about that that portfolio approach to having more of these kind of big returner shots, and is there an engine where that gets better over time? Sarah Friar (00:16:32): There has to be because in the end, the durable, high value companies created in this era, I don't think they're not going to be magical. They're going to look like the great companies of prior eras. They're going to create customer value. Starts with the customer and really helps the customer do something different, better, more revenue, more efficiency. Right? Thermo Fisher wants to be able to get patient screening done faster so they get FDA approval faster. That's really important. Like, if you have a form of cancer where you have weeks to live, the difference between a breakthrough in four weeks and two weeks can literally be life or death. They also have I'm going to misquote this but something like 30,000, 38,000 people in the field selling those amazing like, if you walk into any lab in the country, you'll just see Thermo Fisher plastered all over every device. Those people want to be more efficient going to work. Like, the the fastest takeoff of codecs within OpenAI right now is actually in our go to market team. Our devs are there, but like if you look at the pace of growth, kinda month over month, it's all in GTM. So they want more productivity out of their GTM team. And of course, they're doing things in areas like finance, which I get really excited about. So customer value first. From that, now you need to get to a great gross margin. So how do you get to a great gross margin? You're looking at, like, the cost of revenue. The main input is compute. The good news on compute is that there is a massive deflationary curve on cost. Right? From ChatGPT five to five four, I think the deprecation cost was something like 97 percent. It's like a Wow. Kind of an amazing curve. Actually, I'm slightly from four to five four, it was 97%. But that happened in like two years. That's kind of wowing. Right? David Sacks (00:18:21): That's Sarah Friar (00:18:22): incredible. Even our newest model, if you look at five five that we just released, we're trying to now translate that back to the customer. So we actually raised prices on five five two x. But if you look at what the cost of the customer is, they're probably still getting a break of about 20 to 30% cost reduction per token because it's just much more efficient per token. So there's a lot to do in that envelope. David Sacks (00:18:44): Yep. Sarah Friar (00:18:44): And and part of making an alloc a capital allocation decision is having to if you make it on today's cost profile, you actually might misprice Right. The outcomes. You have to lean in a little on the cost profile. And then as we think about, like, the builds, yeah, you are having to make like, really, my focus today on compute is what's the compute I can buy for 28 onwards? Like that Michigan data center in Celine, I don't think we will be getting compute out of it until probably end of twenty seven or early twenty eight. So that's where you're starting to make your bets. And in fact, where I feel most short of compute right now is starting to look at thirty, thirty one, thirty two. So you're having to create a business model. Now, the good news is each year goes by, we get more confidence in the build. We're seeing it massively outperform, and so that's giving us more and more confidence, and the market is coming towards us much more. Speaker 6 (00:19:40): So how are you making the compute need forecast multiple years out Yep. Accounting for all of the architectural and model advancements that are happening where, call it, value or utility per unit of power is going up. And help us understand how you kind of estimate that given that there's a lot of technology development going on that has a high variance to it. Sarah Friar (00:20:06): Yeah. Yeah. So we we do have to make multiple assumptions both on the compute itself. So we assume right now that compute actually on a per gigawatt is getting more expensive because power is getting more expensive, memory is getting more expensive, and so on. However, the the intelligence that we get on the other side out because of the deprecation on the chip side is is more than making up for that. So in terms of a per unit sold to a customer, it should actually get a lot less expensive Speaker 6 (00:20:34): improvement in that. So that's just Yeah. Sarah Friar (00:20:37): Exactly. That's just the chip itself. We don't try to overestimate on the model side because sometimes like five five is an incredibly good model on the efficiency side. But if you look at something like five four, the prior model, it was a really large pretrained model. It was very expensive. It was actually hard to serve. And sometimes we wanna do that really big pretrained moment David Friedberg (00:20:58): Yeah. Sarah Friar (00:20:58): And then we take multiple model turns to be able to kind of drive down on the cost side. I mean, in the in the near term, like in '26 and '27, I clearly build a model that's bottoms up. So I know what my products are, I have a sense of what the pricing will be, you know, p time you know, consumer, p times q, how many wows do I think I have? I can see what the shape of the line is. How many of them will subscribe? Advertising coming in is also still related to how many weekly actives, how many dailies, how many messages, and So so you can you can do actually a pretty good model job twenty six and twenty seven. That said, the shape of the line keeps taking us by surprise to the upside. When you get into the outer years, you're actually looking more at the compute you've bought and almost just doing an algorithm the other way that's saying, this amount of compute should equate somewhat to this amount of revenue. I don't know for certain exactly where it will all come from. Like, year ago, I built a model for investors that showed AgenTek revenue. And the story was, we're going have this thing, we're gonna be in the agenda gear, we're gonna hand it to a developer with natural language they're gonna be able to build, and we think they will pay upwards of maybe $2,000 a month for it, which is kinda laughable in hindsight, but nobody believed. They were like, I don't even know what she's talking about. There's no way that will happen. And $2,000 a month? Remember when people were losing their minds over chat gbt pro being at $200? Like, oh my god. No one will ever pay for that. Speaker 6 (00:22:30): Yeah. Right. Yeah. So why a 122,000,000,000? Does it take you to 2,031, 2,032? Like, how do you get the calculus on the capital needs as you do that modeling? David Friedberg (00:22:38): You may be even more specific. So the estimates I've seen is that to stand up one gigawatt of AI compute costs about $50,000,000,000 in cap land, power, shell, chips, everything, all in around 50,000,000,000. Do you have to front all of that money when you create a new data center? Or how much of it do you do? How much of it can you get debt for? Does a 100,000,000,000 raise only get you two gigawatts, or does it get you five? Like, what does it get you? Sarah Friar (00:23:08): It's it's a great question. So if you look at our compute strategy, and it's crazy how fast the world has changed. So just two years ago, we were literally one. We had one CSP we worked with, Microsoft Azure. We we sat on one chip, NVIDIA. We had one product, ChatGPT. One price point, $20 a month. So often use a Rubik's cube as kind of my metaphor, so we were like one cube in the bottom. Today, if you look at our strategy, it's been to go, first of all, multiple CSPs, because what CSPs do for us in effect is they shift CapEx into OpEx. So you pay as you get the revenue, so as you're actually utilizing the data centers. So in effect, we are writing somewhat on their ability to build kind of CapEx and financing. So today, sit on top of every CSP, Oracle, CoreWeave, Microsoft, GCP, AWS, and a bunch of small neoscalers. On the chip side, we've also gone for a program of being multi chip because we wanna make sure you're always on the frontier. I think if you're only on one chip, there's just inherently a moment where you can't be on the frontier because there's some leapfrogging that happens. So today, NVIDIA remains our absolute priority partner. They have the frontier chip. Our next big trading run-in the fall will be done on Vera Rubins. We're really excited about that, and now we're plotting kind of the Simon series that's coming. But we also now have chips in the pipeline from AMD. Cerebras is already online. It's been an incredible low latency chip, great for devs, for example, that want real time coding. And there's our own chip that we're working on with Broadcom. And then beyond that, there's other ways we've diversified. So now think about that Rubik cube. It's become much more multidimensional, and it allows us to effectively utilize investment grade CSPs in order to be able to go fast and push it back to be more OpEx, not CapEx. Sarah Friar (00:25:02): Now, we are starting to shift gears into more of a built to suit type environment. We announced a data center we're building with SoftBank Energy down in Texas. That's the beginning of something that's beyond a CSP. There's a little bit more CapEx required there. And then finally, I think, as the world progresses, remember, we've done all that just in two years. The The reason I like a Rubik's cube is, again, please chat GBT this, but I think a Rubik's cube has something like a quintillion different forms it can come up with. And so it just gives us a lot of optionality. So remember what I said, my job is maximum optionality, and in a moment where I'm not yet an investment grade type of entity where I can go get lower cost debt financing, being able to work with partners to do that is really important. David Sacks (00:25:48): Sir, do you think that in five years from now, the stack is just merged together? What do I mean? In traditional or historical markets, you'd have NVIDIA sell the chips, but that's all they do. And then you'd have, you know, Microsoft just run a cloud. That's all they would do. And then you would have a consumer app. That's all you would do. But now we see everybody doing everything. You know, you guys have silicon that you're spinning. You have models that you make. You may or may not eventually decide that you need to be some form of a neo cloud yourself. If you look at NVIDIA, they have incredible silicon, but they also have their own open source models. They're increasingly becoming an off taker. Google is a cloud company first, but they also have a chip. Now they have models. So it's all merging. Is if that continues to happen, does that make the competitive landscape simpler or easier? Sarah Friar (00:26:38): I mean, think where everyone is trying to make sure they reside is the layer that is closest to the customer, where usually you take the the largest portion of the profits of the ecosystem. Right? No one wants to find themselves David Sacks (00:26:52): abstracted. Away. Sarah Friar (00:26:54): Yeah. Absolutely. And so that's why today when I think about our positioning, it comes back to where I started. Why we want to be that AI intelligence layer, is because a year ago, people talked about the commoditization of the LLMs. And frankly, it's gone the opposite because as you start building an agentic layer, and we've all started to use this word harness, but the harness is what brings the context, the memory. Right? I have, in my codex, I have a whole ginormous memory file where it knows that I'm me. It knows I'm the CFO of OpenAI. It knows how I like to write things, how I like to say things. It knows what I'm interested in. It actually also knows that I'm a mom, teenagers. I mean, it just carries all this memory, and that makes the model more powerful for me. Now think about what happens when that memory in that context is brought into an actual enterprise environment. So now it's not just even about the data that resides there, but I always think about the the intuition of, like, back when I worked on Wall Street, right, there was all the data in the world that told you what a stock should do post an earnings call. But, give me one second, then you called your trader, and the trader be like, yeah, stock's not going up, Sarah. And I'm like, what are you talking about? Like, all the numbers say it did this, did this, this. And he's like, yeah, no. But I know this fund is under pressure, and they need to sell down their book, and that is gonna kill the stock for the next week. Right? That is the intuition of an enterprise. Like, it's the best example I always think of because I came out of a financing world, but there's this intuition in every walk of life. And that's where I think the models are now getting very connected to the memory and context and intuition of your company. And that's what gets CEOs and c suite really excited because they're like, okay, now I really see how this is gonna add value to drive my revenue line, my top line, but also, you know, I can think about it as an efficiency play as well. And so back to what you're asking, I think what people wanna make sure is they stay as close to that value as possible. David Sacks (00:29:00): And be flexible enough to pivot as you need to wrap. Yeah. But Sorry, Jason. Jason Calacanis (00:29:05): It's quite alright. Been wonderful and you've been so great with the details. One final detail question, rapid fire. Three great greatest consumer businesses of our lifetime, iPhone, Meta, advertising network, and Google's advertising network. Two of those three are ad based and even Apple has a sprinkling Haven't of ad heard you talk about ads much. People tell me they're seeing some ads in the experiment and the free version. What is your commitment to the ad version? You guys got a little trolled by Anthropic during the Super Bowl. Oh, you're going to have ads. But is ads the solution to making this free for the world? Sarah Friar (00:29:42): Yeah. So first of all, on the ad front, you know, we wanna stick by our principles. We wanna make sure that you know you're always getting the best result based on the model, not by something that was sponsored. So that has to hold true. And I think the second thing is that we'll always provide a free a tier, sorry, an ad free tier for people that just don't want ads. David Sacks (00:30:02): With that You're fed pay. Sarah Friar (00:30:04): If you took if you took Fiji says this really well. If, you know, Google and Meta had a baby, it would be ChatGPT. Because what you have in Google search, and by the way, we know we have at least 11% of the search market. It's a lot more because actually, when you do a Google search and the page refreshes, that counts as one. In ChatGPT, when you do a whole conversation where you might ask 50 questions, that also only counts as one. So in reality, we have a much higher portion, very high intent. That is great for advertisers because I'm effectively telling you what I'm doing. Right? I want really cool shoes to sit on the stage. I'm telling you what I want to go buy. In Meta's case, right, they use this, like, people like you sort of intent so they have the demographic. We have more than that because we have memory. Right? I just told you it knows who I am. So imagine putting memory and context next to intent, you should have a very potent ad platform, which gives you an ability to offer up massive access to the world writ large because now you can pay for it. And I think back to a question you asked, Friedberg, like, you look at the revenue per token right now, if I was optimizing only for today, I would give every token to the API. David Sacks (00:31:17): Right. Sarah Friar (00:31:18): Every token to the API. Order of magnitude more than to the consumer. However, I told you we're playing our own game. We have a strategy where we believe there's an AI infrastructure layer, a utility like electricity, and in a future state, you'll want to be able to serve the world writ large. Consumers, small businesses, large enterprises, governments. That's our strategy. David Sacks (00:31:39): Ladies and gentlemen, the CFO of OpenAI, Sarah Fryer. You've done fabulous.