The Exchanges

Q Did you spread a little bit to then too many projects? People were talking about this new gadgets, 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?

A No, 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 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 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, It's really firing on all cylinders, but we don't want to leave the consumer behind. Remember our mission at open AI 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. 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 real paid tier with the plus, 20 bucks, hopefully you…

AI assessment note: “No, I think that the world loves to go to binary isms.”

Q 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 both the spectrum of where we are and then those specific economics and if that's changed?

A Yeah. 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 a press hand 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, how we get regulatory, um, environments 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 Saline, Michigan. He'…

AI assessment note: “Where are we on the compute continuum? There's kind of choke points everywhere”

Q 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 open AI 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?

A 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 nine hundred million people use ChatGPT 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, Azerbaijani and, um, what, Kazakhstan. What is it? It's Kazakh. Um, which is kind of incredible to talk about where it's going. So multiple interfaces, ChatGPT, of course, there's, um, Codex, um, just hit five million over the weekend. We're really proud of that. Coming from almost zero in January. Go Codex. Um, help me prepare for this little special up here too. Um, 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, ChatGPT acts as a front door. As we, as mo…

AI assessment note: “So let's talk a little bit about a strategy. Our strategy is different, right?”

Q Costs about fifty billion dollars. Land, power, shell, chips, everything. All in around fifty 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 hundred billion raise only get you two gigawatts, or does it get you five? Like, what does it get you?

A 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, ChatGPT, one price .20 dollars 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, multiple, 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 neo-scalers. 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 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 Rubens. We're really excited about that, and now we're plotting kind of the Feynman series that'…

AI assessment note: “what CSPs do for us, in effect, is they shift CapEx into OpEx.”

Q 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 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?

A 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 largest portion of the profits of the ecosystem, right? No one wants to find themselves abstracted away. Absolutely. And so that's why today, when I think about our position, 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. 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? 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 1:02,…

AI assessment note: “where everyone is trying to make sure they reside is the layer that is closest”

Q And then you end up plowing all your capital into that higher ROC bucket. 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?

A 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, um, and really helps the customer do something different, better, more revenue, more efficiency, right? Thermo Fisher 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 30,038 thousand 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 Codex 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 a great gross margin. So how do…

AI assessment note: “There has to be, because in the end, the durable, high value companies created”