Inside the economics of OpenAI (exclusive research)

0:00Azeem Azhar:Today, artificial intelligence companies are now being valued in hundreds of billions of dollars. It's open AI, it's anthropic, it's all the value that DeepMind has added to Google over the past years. But that forces a really important question, and it's a question that is being asked by the mainstream, but also by specialists. Do the economics actually work? When you look at what it costs to train and run a frontier model, and what you earn from it before the next model comes along and replaces it, is that a profitable business? Are we looking at something a bit like Uber, which lost money for 14 years before turning a profit and is now handsomely valued, or something that doesn't have an end in sight?

0:42Azeem Azhar:Now, these questions really matter. The stock markets, well, big tech had a lurching week this week, and at one point, more than a trillion dollars was wiped off valuations. Wall Street's very linear investors were trying to digest the$650 billion of capital expenditure commitments being made by big tech for 2026. Some of that$650 billion is going towards AI infrastructure. Does any of this make sense? Are there actually going to be operating margins to defend and is the revenue growth going to support this? Now, as a reader's exponential view, you'll know that we've been asking these questions for months, if not longer.

1:22Azeem Azhar:But most recently, we partnered with Epoch AI. I'm sure everybody knows Epoch, but if you don't know, they are really preeminent independent research organization tracking some of the trends behind AI. You've probably seen their work on scaling laws and compute trends. So we worked with their team to dig into the actual margins of Frontier AI, and the results are really, really interesting. So whether or not you've had a chance to read our research yet, and you really should have done, this conversation will give you a really clear picture of where things stand and where they're heading. I've asked financial journalist Matt Robinson from AI Street, it's another Substack newsletter, to moderate this discussion and really put us on the spot.

2:05Azeem Azhar:Jaime Sevilla, the founder of Epoch AI, is here. And Hannah Petrovich from my team, she led the research on the exponential view side. She's also no stranger to large numbers. She has a doctorate in astrophysics. So what I say, well, is that roughly right for Hannah? Is it within a few orders of magnitude. Often, yes. Matt, I'm handing the stage over to you. The floor is yours. Maybe you guys could start with, you know, for someone who's getting into the research, what's the big takeaway here and how did you even think about building a framework to analyze a business like this? Absolutely. So, Matt, a little bit here of the context of why we were doing this before I get into the takeaway.

2:46To our understanding, no one had really taken on this humongous task of piecing together all the public information that there is about the finances of OpenAI or any large AI company, really, and trying to paint a picture of what are their margins like, whether they are making enough money to recoup the large cost of developing new products. So we did this hermeneutic exercise of just hunting for all the information that we could find and trying to make sense of it. Now, I won't pretend that we have arrived at the definitive answer. In fact, our views are constantly evolving as we learn more about the companies and their finances.

3:30But I'm pretty happy with the overall framework that we have established for even trying to think about this question in the first place. If I was trying to now tell communicate like, okay, in summary, what did we learn and what did we find? For me, the two most important takeaways is that, one, it seems likely that OpenAI during the last year, and especially while operating GPT-5, was making more money than the cost of the compute, which is the primary expense of operating their product. Though they seem to have made like a very small margin or even having lost money after accounting for all the other operating expenses that are going to run in the model.

4:11So this is paying for staff. This is sales and marketing spending. This is administrative cost. And this also includes the revenue sharing agreement that they have with Microsoft. Now, the raw profitability, the operating margins of a company are not necessarily what you want to look at when you are trying to assess whether the company will be profitable in the long term. As Asim alluded to earlier, Uber lost billions upon billions of dollars before they finally became profitable. profitable. And really, if you're an investment-minded person, when you are looking at a growing business, you do not look so much at how much profit they are turning in their early years or they're still growing, but rather you will rather look at the gross profit that they're making at their gross margins and how the revenue is scaling year after year.

5:03So you can get a sense So after this initial phase of rapid growth, where the industry and the company could land at. Now, if you did that, then I have just said like, okay, they look to have made like a decent gross margin. But there is one more wrinkle in that you need to account for here, Matt, which is that these products are really, really expensive to develop and they have a very short self-life. So it's not enough to just look at the gross profit and check like, okay, it seems that that they have this 50 % gross profit since they're getting twice as much money as you put into the machine.

5:41Like, no, you actually need to think about how much money does it take to develop a new model and how long would you expect that model to stay relevant before it becomes obsolete by your competitors or by open-weight alternatives that will make a dent to the usage of your model. So this is the second part of our research where we tried to look at how much OpenAI is spending in R &D and how that compares to the gross profits overall. And what we found is quite shocking. So if you look at how much they spent in R &D in the four months before they released GPT-5, that quantity was likely larger than what they made in gross profits during the whole tenure of GPT-5 and GPT-5.2, which points to how competitive this space has become in the last couple of years.

6:32Azeem Azhar:Hannah, why don't you just, I know that you dug into this in such detail and I don't want to speak ahead of your expertise to Matt's question. The methodology, how we actually got into it. And a lot of it was based on numbers that we could find in the past historically and then trying to predict what would happen in the rest of 2025. So for example, the sales and marketing, we had some data that 2024 was 1 billion in sales and marketing. And then in H1 of 2025, that was 2 billion, for example. So we can build a picture using constraints in this way. And from that, you can try and understand the costs of the company as a whole.

7:15And we broke it down into many categories, as you can tell in the piece. But each of those as well, I tried to break down further into the different separate components so that we could realistically understand whether or not it was feasible or a realistic approximation at least.

7:32Azeem Azhar:This is a kind of complicated exercise. And one of the things that comes out from this is this question of that short model life. And the family that we looked at was only really the preeminent family for a few months. Now, we know that enterprises and even enterprises don't change the API they're using the day a new one comes out. There's always a bit of a lag. But consumers do, right? Because that's what you get access to on ChatGPT. And, you know, you may remember that when GPT-4 was set aside from ChatGPT, it was an emotional support tool for many users. And they were very upset with how methodical and mechanical GPT-5 now felt.

8:16Azeem Azhar:And I think one of the uncertainties is to what extent do you actually learn and prepare for your next model based on the short life of the existing model, right? There are a couple of elements to it, right? One I think is a little bit more nebulous, which is that by having a really good model, even if it lasts for a short period of time, you maintain your forward momentum in the market in terms of customers liking you and your enterprise sales and so on. And that feels less tangible than the second bit that I think is perhaps a bit harder to unpick, which is what do you learn about running better and better models from actually having run a better model, even if it only lasts for four months?

8:57Azeem Azhar:And that learning might be sort of down in the weeds in sort of R &D and, you know, particular choices you make in training data and reinforcement learning. It might also be in operations, right, and just operating a model of that scale. And I think it's quite hard for us to know, I suspect it's hard for OpenAI or any of the other foundation models, to know the contribution of that second part to the model itself, right? So in a sense, who in this kingdom is actually able to see with two eyes? I'm not sure, you know, many can at this point. It's interesting. It was making me think of GPUs and how do you, I was talking to some finance folks about, okay, well, what is the value of these H100 chips going to be in a few years?

9:41and everyone's kind of shrugging their shoulders like I you know and and sort of putting it out there and it's kind of seemingly like a parallel to these models like what is the value of you know cheat to be four like three years ago was you know so how do how do you think about that one question I have is like you know you talked a bit about compute and costs there and you know this may be a little down in the weeds but the cost of compute in sort of buildings models is going down and how do you sort of see that kind of going forward? So the cost of compute of building these models, I don't think it's quite going down.

10:15I do see it as going up time and again. The pre-training seems to be going up. And despite rumors to the contrary, pre-training is not dead at all. People are building 100 billion data centers for a reason. They are invested in running very large scale experiments and very large scale training runs that are unprecedented inside. And I think that this is part of what contributes to these models being so expensive. Like one of the interesting things here when I think about OpenAI is the game that they're playing. The game that they're playing is not so much about becoming profitable right away. Rather, what they are trying to do is convince investors that they have a business and a research product that's worth scaling as much as possible.

11:00like driven by this conviction that through a scale, they're going to unlock new capabilities that in turn is going to unlock new markets and let them continue their incredible revenue growth.

11:11Azeem Azhar:I would say I want to come to, I think that's exactly the right thing for them to do anyway. You know, as an investor myself, I want to invest in people who have optimistic views of the future and therefore believe you need to plant seeds today in order to harvest them in two or three or four years. And, you know, particularly in a business like this, where there is no asset, you know, there's no, there's no hotel that's been built that can be resold to, to another property developer. You know, it's an intangible asset that may not have that much salvage value, especially if people in the team leave.

11:47Azeem Azhar:I think exactly the right thing to do is to, to be building out ahead of time and you see that investment j-curve i think that the the two kind of challenges around this model are number one is the open ai model the only way to to do this and i don't just mean from the financial side i also mean from the strategic focus side we've seen anthropic do something completely different and the second challenge is and i think we went part of the way to answering this question is, is there a path to positive unit economics? In other words, are they producing something for X dollars that they can sell for 1.3 X dollars?

12:30Azeem Azhar:Or are they producing something for X dollars that they sell for half an X dollar, which was the story of a lot of the dot com, right? Cosmo.com and all these other things. And I think we got partway to answering that second question, which is that, yes, it's expensive. Yes, there is, you know, some kind of gross profit margin. The level that we estimate, I think Hannah can speak more accurately to this, is, you know, lower than a traditional software business. So we're learning that perhaps foundation labs don't look like software businesses. They look like something different. But, you know, these are the things I think that we have to play around with.

13:03Yes, spot on with the numbers there. The other thing I would like to also consider is that AI is also creating a flywheel in the development space of itself. So I'm wondering how that might affect R &D down the line, given that R &D is such a huge development cost to the company for the next model. That's just a thought there. I'm curious, you know, the OpenAI has got a little bit of slack for saying that they may introduce ads, which is, I thought, kind of peculiar that they did because we all have been, I mean, I've been using Gmail for 20 years. And actually, in preparing for this, I stumbled upon some research from Google, Larry Brennan, about how they were sort of against ads in the beginning.

13:45And they, you know, changed their minds. And I'm just curious how you think about ads and how that, you know, if you have, what, they have 800 million eyeballs every week or, you know, how that might play into this. Let's think about why OpenAI is trying to introduce ads in the first place. Because with these ads, I get that. You say it yourself, it seems that they have on the order of almost a billion users that they could monetize through ads. With their monetization plans, it seems that they might be able to reap a revenue of like maybe a couple billion, maybe even up to a few dozen billions of dollars off of that audience.

14:20That's not enough. If your plan is to build$100 billion data centers, that's not going to be enough to fund that. So why are they considering ads in the first place? I think this has to do a lot with this game that we're alluding to, where they are not looking to become profitable right away, but they have this vested interest in demonstrating to investors that if we want it, like we don't want right now, but if we want it, we have a path to profitability. And the ads kind of fit into that project plan. It's part of a way of expanding their market that's going to allow them to show, like, look, there's a path to$100 billion revenue between the ads, between business sales, between other markets that we could be unlocking here.

15:04like these models are not profitable right now, but they have these arguments that they can make, including ads that point to, that help them argue like, look, not right now, but they could be. We are not gonna, we're not gonna do that because we're more ambitious than that, but we could be profitable if we wanted.

15:21Azeem Azhar:We've seen some success with ads and Gen.AI. I think it was in Meta, wasn't it? They have really had some sort of forward momentum. Yeah, so in Meta's earnings in October, but they commented that their AI tool-driven ads were essentially bringing in$60 billion of revenue in ARR. So there is a considerable uplift already in the ad space there, but this will be the first time that it will be in the chat one. And that was helping with conversions, right, with Meta's ads? Yeah. And it was also that Meta were able to keep people on Instagram and Facebook longer, So people were seeing more of these ads as well.

16:05Right. I guess everyone can just make up their own ad and, you know, it's a lot easier to do that.

16:11Azeem Azhar:And you're sort of you're stuck in there. But I think that this this question about the ads is is a really important one. I think, Jaime, what you've suggested is is really intriguing. So it is the question as to whether an advertising model is really, really fundamental to OpenAI or whether it's a sort of instrumentally useful thing that gets you to the next stage. And I think a lot of that depends on how we start to use these tools. I mean, the piece of work that Epoch and Exponential View did looked at ancient history with all due respect. It was before last week. right it was before open claw it was before opus 4.6 and you know whatever else anthropic comes out with and we looked at a particular world before what i think andre carpatti called the threshold of coherence for agents and he described this moment where the agents are now good enough that you can get them to do lots and lots of things for you and and so that also makes me wonder whether that traditional ad model makes any sense because there ain't going to be any eyeballs.

17:21Azeem Azhar:Just sell it to the agents. Sell it to the agents who have probably rented humans to do the jobs they can't do themselves. You guys have spent, you know, you spend a lot of time doing some rigorous work here. Say you swap seats with Sam Altman. What do you do differently or what do you keep the same? I mean, first of all, I will just go and look at their finances and actually get a clear picture of what's going on. After having done that, what I will do in some moments in some elements position. Like, honestly, it seems kind of similar to what they seem to be doing already. For me, this question of the models as a rapidly depreciating asset actually brings a little bit into focus of what might be the perduring asset, the part that might retain more value through generations of AI.

18:09And it seems to me that this part is infrastructure and they're giving up big time to break into the infrastructure space, they have famously said like, oh, we want to get to a position where we are building gigawatts of power at a time, which is a really ambitious, a very ambitious goal. But it makes sense from my perspective. If you think that the software part is rapidly depreciating, you might want to get in on this part of creating a building and serving infrastructure at a scale. Yeah, so if I was to bring in a different view here, obviously their consumer section proportion is quite large.

18:46And we know from Sarah Fryer that about 60 % is now consumer, 40 % enterprise. So obviously the enterprise push is there, and we know that the enterprise push would be, you know, bringing in money for the company quite well. The consumer side is very competitive given Gemini and, you know, other AIs which you can easily have on your device. say like Samsung I just hold my finger over a button and I have access to Gemini so there's very little friction in using it but so if I was Sam Altman I would want to try to see if I could do something different on the consumer side and obviously we know like they're hoping to bring out a device that is a unique different side of targeting that consumer component and if there's other things they can do there that would keep, you know, their consumer money coming in.

19:37Azeem Azhar:Okay, we've had two different views here. Matt, I'm going to give you a third view, just to really make you work hard for your moderator's seat. Let's take this idea that Hannah raised, which is like different classes of interactions for the end user, whether it's consumer or business. And the point that Jaime made, which was, look, the infrastructure really, really matters. The infrastructure obviously matters an enormous amount, because in the last week or two, if you've been using Anthropic, it got really slow because we all got excited about Opus 4.5. And then the question is, well, where does the revenue come from?

20:11Azeem Azhar:Where is the point at which people start to spend more and more? And one thing I would say from just looking at the exponential view bills, our bills have gone up since Opus 4.5 came out. Okay, because everyone is coding more. We're running many more background processes that are chewing through tokens. And I thought that was all true until I installed my OpenClaw bot. Actually, it was called, what was it called? Clawed initially. And I've called mine Mini Arnold in homage to the second Terminator that came back to protect us. and but mini arnold is a greedy and forgive my french mofo he he will chew through 20 to 30 dollars of tokens a day so we're talking five grand a year in order to do my bits and pieces and i've pushed him down to haiku which is the cheapest anthropic model i i do the heartbeat on a local llm so that every 30 minutes i'm not having to pay uh pay for that it it is expensive Now, what drove that?

21:12Azeem Azhar:What drove that was the idea that the models were just good enough, like they crossed that uncanny valley. And when we did the work on OpenAI, their models hadn't crossed that uncanny valley, right? GPT-5 was not the thing you could leave to run for hours at a time. 4.5 Opus from Anthropic was really the first. and I'm just curious my sense would be that all of this discussion starts to look very different when open AI is shipping things that run five six nine hours at a time because at that point actually you know the inertia of being an open AI user through the enterprise or through the customer sticks with you and and the only thing I would say is just think about dear old mini Arnold who cost me the same as you know four Starbucks flat whites a day to do whatever the hell he's doing in his Mac Mini.

22:01Azeem Azhar:I don't know what he's doing. I don't ask. It's his private space. You know, get on with it, Mini Arnold. So that is that for me is like is how we merge, you know, Hannah's observation, like kind of the user experience, user interactions patterns with Jaime's point, which is this is all about infrastructure, because ultimately all that processing has to happen somewhere and that becomes a choke point. And to that, you know, this week, as you mentioned earlier, that, you know, the markets were caught flat footed, I guess, to say the least about this, you know, ever expanding compute spend. To me, what's interesting is that, you know, as the hyperscalers just reported, they're capacity constrained, right?

22:34You know, we're seeing these big rollouts where, you know, CapEx is huge, but yet they can't meet demand, right? And I think that, you know, maybe it's a little separate, sort of beyond open AI, but I'm just curious, like, how do you see just, it's just wild that they're spending this much money and they just can't catch up? It's just a lot here, Matt. And I just see that there are two primary constraints here. If you want to scale up the infrastructure, is you need enough GPUs and you need enough energy. It seems to me that energy right now is the thing that everyone talks about. It's something that we know how to solve.

23:08We know how to build energy. Like you don't need that much energy, all things considered. If we need to build 10 gigawatts, 100 gigawatts of extra power, that's only a 10 % increase over all installed capacity in the US. This has happened in the past. In the 2000s, they built enough gas infrastructure to match that level of expansion. The GPU bar, though, that's something very unique. That's something that right now is being chalked hold on production in a few factories in Taiwan. And they have been trying really hard to expand on it with pretty limited success. So it feels to me that that's probably where the bottleneck to scaling is going to end up being in the long term.

23:49Azeem Azhar:So I love what you just said, Jaime, because, of course, you know, the general note out there is it's all about the energy. Like energy is the bottleneck. And I think it's pretty clear that the energy is constrained because of lead times and so on. But if you listen to Elon Musk talk about why he wants to put data centers in space, every single thing, reason, comes down to things we've done to ourselves, grid permitting, backlogs. I mean, these things are queues. They're not walls. They're not laws of physics. You know, the laws of physics are, you know, black body radiation and the speed of light and all these other things that Hannah knows much more about than I do.

24:27Azeem Azhar:And so I think that there is something to be said that it's very exciting if you're in the energy space to suddenly be important because we'd rather forgotten about you for the last, you know, 10 years or so. And, you know, Europe hadn't really thought much about its energy and was caught flat footed by, you know, the gentleman from Moscow. and and i think the the so that everyone has got really really excited about that question and and as hymie says it it feels like it it's solvable i would push a little bit on on that because there's just a lot of you know supply chain questions that have to get fixed the copper issue right suddenly we all know about copper and and about you know optical fiber and whoever thought about corning honestly before two months ago so there is something there but i think that what i also to take away from this is let's go back and find out when these companies started talking about megawatts and gigawatts, because I'm pretty certain they were not saying it at the end of 24.

25:25Azeem Azhar:And I have to go back on my notes. I think the first time I started hearing them talk publicly about gigawatts was, you know, I met with Satya in January of 2025. And I would say that it was after that, that I started seeing Microsoft talking quite a lot about, you know, megawatts and gigawatts, or as Doc Brown from Back to the Future would say, gigawatts. And so this is kind of new to them, and it's also new to the energy business. But there's definitely, I think this is the thing that the markets didn't get at the start of this week, which is these hyperscalers are absolutely supply constrained.

26:04Azeem Azhar:They don't have enough chips, and they can't energize the chips they have. And you heard Amy Hood, who is a Microsoft CFO, say, I had to make a choice of whether I put processing power to third-party services on Azure or power Microsoft Office, our first-party apps, and I had that trade -off. I mean, this is not a market which is not being, you know, hasn't got people running after it trying to spend money. And, you know, I think that's, Hannah, you know, you sent me your kind of latest analysis of the market dynamics. I mean, that's exactly what we're seeing. If I was to add anything, I guess I'm wondering how things will move also to the edge.