Host: Open AAI's CFO, Sarah Frier. We got to get right to it. You have just completed what I regard as the most successful fundraising round in history.

Sarah Friar: We're going to raise actually north of $120 billion. We think AI is the biggest era that we've seen to date. We're just starting to understand what it's going to 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 got to grab it.

Interviewer A: Longtime listener, first time caller. Quite exciting to get to hang out with all the bros here. Hello.

Interviewer B: We weren't sure how to start this off, but I thought the best thing was to allow our URSTW cryptosar to maybe...

Sarah Friar: ...save comments. 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 IPOing 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 going to 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? 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 fund raise. We just did, you heard me on on the the s the sizzle reel, raise 122 billion dollars 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: So Sarah was that that point in fundraising is that the biggest private republic up until the SpaceX IPO?

Sarah Friar: It is.

David: It is by orders of magnitude. I think the largest IPO to date was the Sidio Ramco which was about $30 billion. So it 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&A and so on it's actually a lot of capital keeps being returned back to shareholders and 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 want to 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 want to 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 going to have to build big, sustainable, durable companies, and fundraising will be a key component of doing exactly that.

Jason: Sarah, breaking news.

Sarah Friar: Oh my god, so many people coming at me. Hi, Jason. No, it is it is it is hard balancing four interviewers at the same time.

Jason: It's okay. This is my world, by the way. So, I'm good with this. Jason,

Interviewer C: Anthropic just uh confidentially filed their S1. So, does that mean you're third place in terms of the filing?

Sarah Friar: 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.

Interviewer D: Yeah. Is it 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 um 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: 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 Chat GPT is one to the consumer. Over 900 million people use Chat GPT weekly and it's become the noun and the verb. It's how most people experience um AI for the first time. Kind of fun fact, our economic research team just showed me um the fastest growing continents now are Africa. Probably not totally surprising since it started a small base. fastest growing languages are um Azerbjani and um what Kazakhstani what is it's Kazak um which it's kind of incredible to talk about where it's going so multiple interfaces chatgpt of course there's um codeex um just hit 5 million over the weekend we're really proud of that coming from almost zero in January 5 million users go codeex um help me prepare for this little special up here too um there's of course Frontier our enterprise offering ing 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 chats as a front door. As we 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.

Host: 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 that was it?

Sarah Friar: No, I I think that the world loves to go to binaryisms 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 50/50. 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 Thermopisher 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 broadbased. Um, 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 bucks. Hopefully you're all on it or higher about 3x and pro about um 11x over a free user. So remember when you got your flip phone and you're like yeah I don't know what 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.

Host: You said something very influential. I think it was about 18 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 billion a year of revenue to open AAI. So common number one was this one gigawatt equals10 billion a year of revenue for you. But it's not just you because you can probably extrapolate that to anthropic and other folks Gemini

Sarah Friar: 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...

Sarah Friar: ...both the spectrum of where we are and then those specific economics and if that's changed. So first of all, yes, compute is a very scarce resource at the moment. I mean, 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 precient 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. Um 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 um here whether it's energy first and foremost um land power we get regulatory um environment such that we can build quickly um when you get into the racks and chips themselves clearly do we have enough um in that supply chain memory spike is is on at the moment access to great talent um 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 um and then trust I mean I actually put that as part of the supply chain um Sam right now is in Selen Michigan he's going to 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 um that will be you know sticking shovels in the ground on a 1 gigawatt data center which is part of our or 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 Next Door 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 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. It will not be the rateayer that has to pay. Number two, we're going to bring jobs, 2500 union jobs, good jobs like electricians, HVAC, and so on. We are going to pay our taxes, a billion dollars in taxes just for that data center into Michigan. And on top of that, we're going to invest $45 million going into education for codeex credits to do what you all talked about this weekend is like anyone who's not like coming in filled to their new job. I have teenagers using Codeex. It would be like I would never hire a finance person 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 Codeex. Um 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. Can you talk about that 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.

Host: 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 and one of the frustrating things is at some point it just says you know 10:30 it's like all right Jimoth see you at 2:30.

Sarah Friar: Yeah.

Host: And that's not a viable experience.

Sarah Friar: Right. Um...

Host: And in fairness to chat GPT actually I've never had that with...

Sarah Friar: Yeah. We we're quite generous with our tokens and again on purpose we're trying to drive access so people understand because 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 um doing homework like I think about when I grew up and the encyclopedia bratannicas 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 want to make sure that people get that feeling by the way but the landscape right now in 26 if you want to buy more compute good luck to you like tell me cuz I don't know where else to find it. I mean as you know...

Host: well I was going to 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. Um 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 and 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 and we said a lot...

Sarah Friar: ...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. Um hopefully I think you all talked about it this weekend. You're talking to your tool. I talk to codeex every day. And so that is changing rapidly, but that is going to need much more kind of real time compute because it's an odd experience if I was talking to...

Host: Johnny I of this puck these earpieces. So maybe tell us a little bit about that problem if you've admitted it now.

Sarah Friar: If I if I tell you it's an earpiece, Johnny will come and steal my teenage son. I might give it to him. give them to...

Host: but you do believe that there should be some...

Sarah Friar: we're changing into a consumer substrate that I cannot tell you what it is but by the end of this year we will unveil it early next year I have seen it I've tried it I am a hand talker right now I'm sitting on my paradigm...

Host: ...shift when when yeah when you used it was it like having an iPhone for the first time...

Sarah Friar: 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.

Host: It feels natural in some way.

Sarah Friar: It feels very natural, but it feels very lovable.

Host: Really.

Sarah Friar: And I can't really explain what that emotion is cuz...

Host: intimate in some way in terms of...

Sarah Friar: technology, not taking your phone out and it's it's seamless is what I've heard from people that played with it.

Sarah Friar: Technology is very um can be very mechanistic, but we all know great design just makes everything fade away, right? what um at the time you know the simple is hard.

Host: Yeah.

Sarah Friar: But I I think this is a very this story just going back to the earlier question. So putting on...

Host: ...the CFO hat, help us understand the capital allocation model that you use cuz 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. and then you end up plowing all your capital into that higher ROC bucket.

Host: What is that for you guys? And how do you think about that le 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: There has to be because in the end the durable highvalue 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 a customer um and really helps the customer do something different, better, more revenue, more efficiency, right? Thermmaisher wants to be able to get um 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 thermopisher 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 kind of month over month, it's all in GTM. So they want more productivity out of their GTM team and of course um 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 great gross margin. So how do you get to 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 chat GBT 4 uh five to 54 I think the deprecation of cost was something like 97%. It's like kind of an amazing curve. Actually, I'm slightly from four to 54, it was 97%. But that happened in like two years.

Host: That's kind of wowing, right?

Sarah Friar: Um, even our newest model, if you look at 55 that we just released, we're trying to now translate that back to the customer. So, we actually raise prices on 552x. 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 just much more efficient per token. So there's a lot to do in that envelope and and part of making an alloc a capital allocation decision is having to...

Sarah Friar: ...if you make it on today's cost profile, you actually might mispric the outcome. So 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 Seline, I don't think we will be getting compute out of it until probably end of 27, early 28. 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 30, 31, 32. So you're having to create a business model. Um 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.

Host: All right. So how are you making the compute need forecast multiple years out 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 kind of variance to it.

Sarah Friar: Yeah. Yeah. So we we do have to make multiple um 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 in terms of a per unit sold to a customer, it should actually get a lot less expensive for...

Host: ...no model improvement in that. That's just...

Sarah Friar: exactly that's just the chip itself. We don't try to overestimate on the model side because sometimes like 55 is an incredibly good model on the efficiency side, but if you look at something like 54, the prior model, it was a really large pre-trained model. It was very expensive. It was actually hard to serve. And sometimes we want to do that really big pre-train moment 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. Um you know P time, you know, consumer P*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 activives, how many dailies, how many messages and so on. So you can you can do actually a pretty good model job in 26 and 27. 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 a year ago, I built a model for investors that showed agentic revenue. And the story was, we're going to have this thing. We're going to be in the agent era. We're going to hand it to a developer with natural language. They're going to be able to build and we think they will pay upwards of maybe $2,000 a month for it, which is kind of laughable in hindsight, but nobody believed. Um, 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? Oh my god, no one will ever pay for that.

Host: Yeah.

Host: So why 122 billion? Does it take you to 2031, 2032? Like how do you get the calculus on the capital needs as you do that modeling?

Host: You maybe even more specific. So the estimates I've seen is that to stand up 1 gigawatt of AI compute costs about $50 billion in cap land, PowerShell chips, everything all in around 50 billion. 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 100red billion raise only get you two gigawatts or does it get you five? Like what does it get you?

Sarah Friar: It's it's a great question. So if you look at our compute strategy um 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 um we we sat on one chip Nvidia we had one product chat GBT one price point $20 a month so I 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 multi 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 and have capex and um financing. So today we sit on top of every CSP Oracle um coreweave um Microsoft GCP AWS and a bunch of small neocalars on the chip side. We've also um gone for a program of being multi-chip um because we want to 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 leaprogging 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 Ver Rubin. We're really excited about that. And now we're plotting kind of the Fineman series that's coming. But we also now have chips in the pipeline from AMD. Um Cerebrris 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 multi-dimensional. 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. 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 um 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 reason I like a Rubik's cube is again, please chat GBT this, but I think a Rubik's cube has something like a quintilion different um 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 lowerc cost debt financing, being able to work with partners to do that is really important.

Host: So do you think that in 5 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 NeoCloud yourself. If you look at Nvidia, they have incredible silicon, but they also have their own open source models. They're increasingly becoming an offtaker. 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: I mean, I 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 away. Absolutely. And so that's why today when I think about our position and 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. Um 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 it I I have in my codeex 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, it did this, it did this." He's like, "Yeah, I know, but I know this fund is under pressure and they need to sell down their book and that is going to 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 seuite really excited because they're like okay now I really see how this is going to add value to drive my revenue line my top line but also you know I can think about as an efficiency play as well. And so back to what you're asking, I think what people want to make sure is they stay as close to that value as possible.

Host: ...and be flexible enough to pivot as you have to rap.

Sarah Friar: Yeah.

Host: But...

Sarah Friar: sorry Jason,

Jason: it's quite all right. Um it's been wonderful and 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 and even Apple has a sprinkling of ads.

Jason: Haven't heard you talk about ads much. People tell me they're seeing some ads in the experiment in the free version. What is your commitment to the ad version? You guys got a little uh 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: Yeah. So, first of all, on the ad front, you know, we we want to stick by our principles. We want to 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. But with that said, if you took if you took Fiji says this really well, if you know Google and Meta had a baby, it would be chat GPT 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 chat GBT 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 by. 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 Freeberg like if you look at um the revenue per token right now. If I was optimizing only for today, I would give every token to the API.

Host: right?

Sarah Friar: 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 at large, consumers, small businesses, large enterprises, governments. That's our strategy.

Host: Ladies and gentlemen, the CFO of Open AI, Sarah Frier. Well done. Fabulous.