Inside the economics of OpenAI (exclusive research)

In this episode, I'm joined by Jaime Sevilla, founder of Epoch AI; Hannah Petrovic from my team at Exponential View; and financial journalist Matt Robinson from AI Street. Together we investigate a fundamental question: do the economics of AI companies actually work? We analysed OpenAI's financials from public data to examine whether their revenues can sustain the staggering R&D costs of frontier models. The findings reveal a picture far more precarious than many assume; we also explore where the real infrastructure bottlenecks lie, why compute demand will dwarf energy constraints, and what the rise of long-running agentic workloads means for the entire industry.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Azeem Azhar?

  Today, artificial intelligence companies are now being valued in hundreds of billions of dollars. It's OpenAI, it's Anthropic, it's all the value that DeepMind has added to Google over the past years. But that force is 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? Now, these questions really matter.

• 0:44Azeem Azhar?

  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 they actually going to be operating margins to defend? And is the revenue growth going to support this? Now, as readers of Exponential View, you'll know that we've been asking these questions for months, if not longer. But most recently, we partnered with Epoch AI.

• 1:26Azeem Azhar?

  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. Jaime Sevilla, the founder of Epoch AI, is here.

• 2:08Azeem Azhar?

  And Hannah Petrovic 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 when I say, well, is that roughly right? And for Hannah, it's, 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 was, what's the big takeaway here? And how did you even think about building a framework to analyze a business like this?

• 2:39Speaker 01

  Absolutely. So Matt, a little bit here of the context of why we were doing this before I get into the takeaway. To 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 whether, what are their margins like, what, 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.

• 3:23Speaker 01

  In fact, our views are constantly evolving as we learn more about, about the companies and their finances. But I'm pretty happy with the overall framework and that we have established for even trying to think about this question in the, in the first place. If I was trying to now telecommunicate like, okay, in summary, what did we learn and what did we find? For me, the most, 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.

• 4:02Speaker 01

  They seem to have made like a very small margin or even having lost money after accounting for all the other operating expenses that going to run in the model. So this is paying for staff, this is sales and marketing spending, this is administrative costs. 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 Azeem alluded to earlier, Uber lost billions upon billions of dollars before they finally became profitable. And really, if you are, if you're an investment minded person, when you are looking at a growing business,

• 4:47Speaker 01

  you do not look so much at how profit, how much profit they are turning in their early years where they're still growing. But rather, you will 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. So you can get a sense of after this initial phase of a rapid growth where the industry and the company will land at. Now, if you did that, then I have just said like, okay, they look, 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.

• 5:29Speaker 01

  So it's not enough to just look at the gross profit and check like, okay, it seems that they have this 50% gross profit. Seems they're getting twice as much money as you put into the, into the machine. Like, no, you actually need to think about how much money does it take to develop a new model? And how long could 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, of your model. So this is the, the second part of our research. Where like, we tried to look at how much OpenAI is spending in R and D and how that compares to the gross profits overall. And what we found is quite shocking.

• 6:12Speaker 01

  So if you look at how much they spent in R and D in the four months before they released GPT five, that quantity was likely larger than what they made in gross profits during the whole tenure of GPT five and GPT five point two, which points to how competitive this space has become in the last couple of years.

• 6:33Azeem Azhar?

  Hannah, why don't you just, I, I, I know that you dug into this in such detail and I don't want to speak ahead of, of your expertise to Matt's question.

• 6:41Speaker 02

  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 twenty twenty five. So for example, the sales and marketing, we had some data that twenty twenty four was one billion in sales and marketing. And then in H one of twenty twenty five, that was two 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. And 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

• 7:25Speaker 02

  so that we could realistically understand whether or not it was feasible or, you know, a realistic approximation at least.

• 7:33Azeem Azhar?

  This is a kind of complicated exercise. And one of the things that, that comes out from this is this question of that short model life. And, you know, the, 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, 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, on ChatGPT. And, you know, you remember, you may remember that when GPT four 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 five 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?

• 8:58Azeem Azhar?

  And that learning might be sort of down in the weeds in sort of R and D and, you know, particular choices you make in training data and reinforcement learning. It might also be in, in operations, right? And just operating a model of that scale. And I think it's quite hard for us to, 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, to the model itself, right? So in a sense, who in this kingdom is, is actually able to see with, with two eyes? I'm, I'm not sure, you know, many can at this point.

• 9:30Speaker 03

  It's interesting. It was making me think of GPUs and how do you? I was talking to some, some finance folks about, okay, well, what is the value of these H100 trips going to be in a few years? And everyone's kind of shrugging their shoulders like, ah, 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, G2 before, like, three years ago was, you know? So how do, how do you think about that? One, 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.

• 10:11Speaker 01

  So the cost of computer of building these models, I don't think it's quite going down. I do see it as going up time and time 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 hundred 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.

• 10:50Speaker 01

  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, like 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 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. I think exactly the right thing to do is to, to be building out ahead of time. And you see that investment J curve.

• 11:55Azeem Azhar?

  I think that the two kind of challenges around this model are Number one is the OpenAI model, the only way 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 1.3X dollars? Or they're 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.

• 12:37Azeem Azhar?

  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 foundational 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, we have to play around with.

• 13:03Speaker 02

  Yes, spot on with the, 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 and D down the line, given that R and D is such a huge development cost to the company for the next model. That's just a thought there.

• 13:24Speaker 03

  I'm curious, you know, the OpenAI has got a little bit of slack for saying that they may introduce ads, which is, I felt 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. And 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?

• 13:56Speaker 01

  Let's think about why OpenAI is trying to introduce ads in the first place. Because with these ads, again, you said 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 of that audience. That's not enough. If your plan is to build 100 billion dollar 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,

• 14:37Speaker 01

  but they have this vested interest in demonstrating to investors that if we could be, if we could, if we wanted, like we don't want right now, but if we wanted, we could be, we have a path to profitability. And the ads kind of fit into that, into that project plan. It's part of the 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. Like, 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? That they have really had some sort of forward momentum.

• 15:31Speaker 02

  Yeah, so in Meta's earnings in October, 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 it's, this will be the first time that it will be in the chat one.

• 15:52Speaker 03

  And that was helping with conversions, right, with Meta's ads?

• 15:54Speaker 02

  Yeah, and it was also that Meta were able to keep people on like Instagram, on YouTube, not YouTube, Instagram and Facebook longer. So people were seeing more of these ads as well.

• 16:06Speaker 03

  Right. 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, it is the question as to whether an advertising model is really, really fundamental to OpenAI, or whether it is, it's a sort of instrumentally useful thing that gets you to, to the next stage. And, and, you know, I think a lot of that depends on, on how we start to use these, these tools. I mean, you know, the piece of work that Epoch and Exponential View did looked at ancient history, with all due respect, right? It was, it was before last week, um, right?

• 16:51Azeem Azhar?

  It was before OpenClaw. 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 Karpathy 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.

• 17:21Speaker 01

  To sell it to the agents.

• 17:23Azeem Azhar?

  Sell it to the agents who have probably rented humans to do the jobs they can't do themselves.

• 17:27Speaker 03

  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?

• 17:36Speaker 01

  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, uh, what I will do in Sam Altman's, in Sam Altman's position, like honestly, it seems kind of similar to what they, they seem to be doing already. For me, uh, this question of the models as a rapidly depreciating asset actually brings a little bit into focus of what might be the, the enduring asset, the part that might retain more value through generations of AI. And it seems to me that this part is infrastructure and they're giving up big time to break into the AI.

• 18:15Speaker 01

  Infrastructure is based, they have famously said like, oh, we want to get to a position where we are building gigabytes 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 create a building and serving infrastructure at a scale.

• 18:38Speaker 02

  Yeah. So if I was to bring in a different view here, obviously their consumer section proportion is quite large. And we know from Sarah Frye 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.

• 19:21Speaker 02

  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 of that Hannah raised, which is like different classes of interactions for, for the end user, whether it's consumer or business. And the point that Jaime made, which was, look, the infrastructure really, really matters. And 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. There is a mo, and then the question is, well, where does the revenue come from? 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.

• 20:23Azeem Azhar?

  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 open claw bot. Actually, it was called, what was it called? Claude 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 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 L L M.

• 21:06Azeem Azhar?

  So that, yeah, every 30 minutes, I'm not having to pay, uh, pay for that. It, it is expensive. Now, what drove that? What drove that was that idea that the models were just, just good enough. Like they, they crossed that uncanny valley. And when we did the work on OpenAI, their models hadn't crossed that uncanny valley. Right? G P T five was not the thing you could leave to run for hours at a time. 4.5 Opus from Claude from anthropic was, was really the first. And I'm just curious, my, my sense would be that all of this discussion starts to look very different when OpenAI is shipping things that run five, six, nine hours at a time. Because at that point, actually, you know, the inertia of being an OpenAI user through the enterprise or through the customer sticks with you.

• 21:51Azeem Azhar?

  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. I don't, 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, is how we, 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. Cause ultimately all that processing has to happen somewhere and that becomes a choke point.

• 22:20Speaker 03

  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, their capacity constrained, right? You 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 OpenAI, 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?

• 22:53Speaker 01

  It's just a lot here, Matt, and I just see that there do put a primary constraints here. If you want to scale up the infrastructure, 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. We know how to build energy. Like, you don't need that much energy, all things considered. If we need to build 10 gigabytes, 100 gigabytes of extra power, that's only a 10% decrease overall 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 part, though, that's something very unique. That's something that, right now, is being chock full on production in a few factories

• 23:36Speaker 01

  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 gonna end up being in the long term.

• 23:49Azeem Azhar?

  So I love what you've just said, Hime, 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 cues. 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. And so I think that there is something to be said that it's very exciting,

• 24:32Azeem Azhar?

  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 The gentleman from Moscow. And and I think the the so that everyone has got really, really excited about that question. And and as Jaime says, it feels like it's solvable. I would 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 the copper issue, right? You need suddenly, we all know about copper and and about, you know, optical fibre and whoever thought about Corning, honestly, before two months ago. So there is something there, but but I I think that what I also take away from this is let's go back and find out

• 25:17Azeem Azhar?

  when these companies started talking about megawatts and gigawatts. Because I'm pretty certain they were not saying it at the end of twenty four and I 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 with Satya in January of twenty twenty five. And and I would say that it was after that that I started seeing Microsoft talking quite a lot about, you know, megawatts and gigawatts and 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 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 this

• 26:00Azeem Azhar?

  these these hyperscalers are absolutely supply constrained. They can't don't have enough chips and they can't energise the chips they they have. And you heard Amy Hood, who is 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 this is not a market which is not being, you know, hasn't hasn't got people running after it, trying to spend money. And, you know, I think that's, Hannah, you know, you've you've sent me your kind of latest analysis of the market dynamics. I mean, that's exactly what what we're seeing.

• 26:39Speaker 02

  If I was to add anything, I guess I'm wondering how things will move also to the edge. So obviously, you have a lot of this build-out in the hype in for the hyperscalers in data centres. But we commented on, Azim, you said you were running an L L M on device. At what point will they get better? To the state that you can actually run, you know, the things you're doing now on your device with hardware improvements that are coming and the algorithmic improvements which are also coming. And I wonder at what point we can do most of the things we're doing now on our device.

• 27:11Speaker 01

  Yeah, well, it's actually very interesting if you let me to build on top of this. Because if you look at a fixed level of capabilities, you see this rapid growth where in order to achieve what models could do nine months ago, you already have pretty much an open model that's, I mean, it's gonna be kind of there, right? If you look at the Kimi 2.5 model, like it's arguably at the O3 level, the O3 being the model that OpenAI launched in April last year. If you look at that, then you see this rapid decrease, this rapid decrease in the amount of resources that you need in order to train and to deploy a model at a fixed level of capabilities. But it's not a fixed level of capabilities. What's driving the growth of the industry and the growth in revenue?

• 27:56Speaker 01

  This ever increasing amount of capabilities that we have, like we are building more and more, we're spending more and more in building more sophisticated machines. And I think this is gonna cut against the gradient of moving things to the edge. Like all of these new exciting capabilities, like you're just gonna want to run it on your data centers. I just wanna run it in your data centers for a very long amount of time. Like Asim is talking about like, oh, I may be looking at a bill of $5,000 a year for running my agent. And it's like, that seems very small compared to what these machines could do in the future. Like if you get to the point where these machines have an output which is comparable to a worker, like how much would you pay to have a virtual coworker? Which is like really knowledgeable, who is like available 24/7.

• 28:41Speaker 01

  Like you might be willing to spend on the order of hundreds of thousands of dollars a year just to keep those kind of agents running. And there is huge advantages to running them in a data center. Like the biggest disadvantage here, the one that physics won't allow you to overcome is latency. But right now, the models are already slow enough that I don't mind waiting like 200 milliseconds for the responses to get to me. Like it's fine, they can take, they can take. My GPT five Pro thinking in the background for 20 minutes and I get back to the answer. I'm not in a hurry.

• 29:14Speaker 03

  I think that the compute demand has been sort of underreported. And we all know about CapEx, but I've talked to folks in the enterprise that even when they're not using it, they don't, they don't want to give it up. They don't want to lose their spot. So there's just that constant demand for access.

• 29:29Speaker 02

  Seems like the same way that planes were still flying in COVID so that they could keep their flight routes, even though no one was flying.

• 29:37Speaker 03

  Yeah, they didn't, they didn't want to lose it. I just, yeah, that, that part of the story is, is sort of interesting to me. And, you know, we saw so much whiplash in the market this week about, about what's happening here.

• 29:47Azeem Azhar?

  You know, there's a bunch of other things going on like US levels of debt and what's going to happen with, you know, employment or, or not. But I think at the heart of it is that, you know, on this call are people who are in general, probably more closely attuned to what's going on and the trends that we, that we see. And, you know, I think, I think to, to Jaime's point about us not wanting to give up capabilities, it's, it's absolutely right. I mean, I have, um, you know, I have a prompt about model evaluator that I, that I built. And, you know, occasionally I throw something back to a GPT four class model and it's kind of moronic, the response, I don't want to deal with this anymore.

• 30:29Azeem Azhar?

  And, and the one place within, you know, within my team where we do use quite a lot of, of models, but, you know, there is a lot of batch processing and we'll throw that into DeepSeek 3.2. And that can be cheap, but we're never going to put that on the edge because it's, it's like batch processing. Why would you put your kind of core infrastructure on, on edge devices? And then I think people are a bit disciplined, more disciplined about how long do you want to wait? And how much do you want to pay for a particular class of output? And as you start to get more and more value from the output, you're willing to pay more. And I think that for a lot of companies, and maybe this is true for many investors,

• 31:12Azeem Azhar?

  who are still sagging on a Microsoft Copilot license. They've never really had the breakthrough moment of getting Claude cowork to do 10 hours of tedious manual work in, as Jaime points out, 35, 40 minutes while you go off and do something else. And once you do that, you sit and you say, well, actually, it's worth paying 75 pounds or 100 bucks a month for Claude Max in order to, to, to take this, you know, off my desk. And, and so when I looked at what, what happened in the markets this week, you know, this was a, this was an overreaction. I mean, the market is always right. So let's just sort of get this right. Market is never wrong. You can never bet against the market. They will always stay solvent longer than you will. However, with that said, they didn't get the demand growth.

• 31:57Azeem Azhar?

  They didn't get the way in which the, the demand is outstripping supply. They didn't get how much more we were going to demand as these models get better. I mean, the moment a model can work for 20 hours, I can tell you, I mean, I will be running hundreds of these things because I've got a lot of work to get through and I'll be, you know, saying, Hannah, how many models are you running? I think I've probably sent that to you already. We, we just introduced the, if you don't max out your Claude usage at least once a month, you lose Claude Max as a tier. You've got to be maxing these things out because they're so powerful. And I think that once those, that realization moves into mainstream, which it will in the next two years or parts of mainstream, we will just see the usage grow and grow.

• 32:38Speaker 03

  Yeah, actually, it reminded me of the study I wrote about. They tested the models on financial analysis, see, you know, how, how well it could do and, you know, and it wasn't as good, but it was improving. But the comparison was like, all right, well, if you, if it's a human, it's like $20 an hour. If it's an agent, it's like 50 cents. But I mean, so it was, but that threshold is, is, you know, that gap is closing where you can get it, you know, they're making headway into, you know, sobbing the sort of financial analysis questions. So, um. Curious, what do you guys think was like diving into this was the the most surprising conclusion you you walked away with?

• 33:16Speaker 02

  I was actually quite surprised the margins were where they were, as in the gross margins, because I wasn't expecting them to be around 50%. That seems pretty good for a model where people are saying, you know, they're always having losses year on, year on, year. And although we heard from Dario and Sam that, you know, at the model level, you know, insinuating there's a profitability, to actually see that come out in the numbers is quite reassuring in some ways.

• 33:46Speaker 01

  Yeah, I think that for me, I came away with a more pessimistic view that I had, but you might have picked up on the fact that I'm very bullish on artificial intelligence already. But I I think that after looking at it, I was I was expecting to come here and and find like resoundingly that they already have a profitable model when you look at it through the life cycle of the model, so that they're making enough of a gross profit to completely offset the cost of development. And it seems that, no, this is not the case. It seems that this is way closer than what I thought when I thought it was. And this took us to me more than ever, how reliable the companies are right now on investors'

• 34:26Speaker 01

  goodwill and how much they are relying on this story of we will be profitable later to make. So maybe this is the counterpoint is like it's they seem to be great at at inference. Once you have the model built, they seem to have a great business in their hands. But after you account for the cost of developments, the thing looks much closer than what I expected. I still remain bullish, but I'm now much more temperate than when we started to look into this.

• 34:51Azeem Azhar?

  There's something that the market has done, which reflects what the work that that we did, which is that if you look at the OpenAI stack of of affiliated public companies, so the Oracles and so on, they have really underperformed relative to the Google Anthropic stack of affiliated public, public companies. And there's been a divergence since. I think it was really when Sam talked about needing a trillion dollars or something, which he may have done last, last summer. I found it really helpful that we had gone and done this x-ray of of this, this company, because it helped me see. What I think some of the choices are. So the first thing I would say is there's clearly,

• 35:32Azeem Azhar?

  there's clearly a path to success that emerges for OpenAI based on the work that we we did. One of the big drags is this 20% Microsoft cut they have to pay. So they did this this deal early on in order to get distribution and compute. Actually, I think it was years ago, right before ChatGPT, where Microsoft got took 20% of the the sort of top line revenue. And that that drag does get in the way quite a lot of like an independent, successful business. And that's going to be a commercial negotiation, as we've seen between Microsoft and and OpenAI. It's unlikely that Microsoft will, you know, shoot shoot shoot the prize pet on that journey. And and I think what I was able to therefore see was certain of the levers that they have to

• 36:17Azeem Azhar?

  control on that journey. As Jaime says, they're going to need a lot of money to get there. I think the the other thing, just as a, you know, as a contrast, was we had often written about how OpenAI was trying to capture a lot of different fronts at the same time. So it was trying to capture the sovereign national government front. The they were very early with their large compute infrastructure commitments through Stargate. They wanted to get the enterprise. They wanted to get universities. They wanted to get consumers in lots of different ways. And that often flew foul of actually how Y Combinator, which Sam used to run, would encourage founders to work, right? It's like, find a beachhead,

• 36:59Azeem Azhar?

  stick to the beachhead, and then grow. I think DoorDash was originally, which is a Y C company, a successful food delivery company, was originally called something like Palo Alto Pizza Delivery or something like that. I mean, because that's what they did. And and and I found it quite surprising over the last year or two that they had gone so broad. And I think one thing that this x-ray of the business showed was that there is something appealing about the way Dario has run Anthropic in terms of its really deep focus, which means that lots of speculative investments that you might need either in product to consumerize or in sales and marketing and awareness, you don't need to make, right? You're able to focus a little bit more tightly, and that showed that there is a, you know, another path.

• 37:42Azeem Azhar?

  So I I found the, I mean, I certainly learnt a lot from it and observing the hard work that Hannah and Jaime were were doing.

• 37:48Speaker 03

  That's been the focus, obviously, of Anthropic for a while and OpenAI has been putting its weight there.

• 37:54Azeem Azhar?

  A quick note: if you want to support us in bringing more of these conversations to the world, please consider subscribing to the show.

• 38:02Speaker 03

  But a lot of folks I talk to are sort of agnostic about the models that they're using. They'll sort of pick one out, use one here. And then, you know, we had the market crash because Anthropic put out a legal plugin that was surprising to me. So, and that's a lot of the conversation that I have with people. They're not sticking to one necessarily. They're building the infrastructure so they can swap them in and out. So I'm just curious how you guys think about how do you compete in that space?

• 38:27Speaker 01

  It's very hard. It's so hard to compete in all of these spaces. I do like the perspective of focusing in the end game here. And I like more the end game right now of focusing on business, as Tim has alluded to, which is a little bit more the Anthropic focused. This is the place where I was talking before about where do you spend $100,000 a year on an agent? Like, that's not something that an individual is going to be able to spend right now. This is something that a company spends on their employees and that they might see a reason to spend if they actually believe that AI provides them as much value as a marginal employee would. But you need to get there. And what I see a lot of what OpenAI is trying to do is a lot of these speculative bets

• 39:12Speaker 01

  that bridge the gap between where they are now and where these hundreds of thousands of billions of revenue are. And some of these go well and some of these don't go so well. One that I will say doesn't seem to be going so well is Sora 2, which we actually looked over the course of the investigation. I was surprised to learn that it had significant, though not overwhelming, compute costs from what we could tell from afar. It was entirely subsidized, very much driven by the demand of their free users. And it seems that the usage has actually dwindled since its release until now.

• 39:53Speaker 01

  So it seems to be an example of an avenue for a new market that hasn't quite panned out for them.

• 40:00Speaker 02

  We've heard from Sam Altman that they've slowed hiring. However, we've also heard from him that they're now trying to hire more consultants to actually get, you know, purpose-built solutions with OpenAI in enterprises. So maybe at the employee level, you have people switching a lot between models. I mean, I personally do a lot between the different models. But if you're building systems that have your entire company's data connected to it and workflows from that, I could see that, you know, having one company work with that is a bit easier. And maybe that's the route that they're trying to go and stay competitive there.

• 40:36Azeem Azhar?

  I, I think one interesting thing to, to do would be to, uh, figure out how large enterprises move from model to model when a model release, you know, emerges. And we, what we saw with Opus 4.6, which is a new anthropic model that was released ages ago, I think the day before we recorded this, so it's probably been superseded by something, was that 4.6 was not accompanied by the usual list of 50 great software companies from Replit to Notion to others saying, we're using 4.6 from the get-go. But I don't think that's to say they're not going to, or, but it's more to say that

• 41:18Azeem Azhar?

  there are still change costs associated because a better model is not better in every direction. And there may be certain classes of prompts or bits of your workflow where it doesn't do as well as the one that you've, you've tested it. And so there is this balance between, you know, do you exploit what you know or do you explore with the, with the new model? And, and I've certainly found that. So we have, um, a whole load of, uh, of, you know, agentic flows or workflows or decision systems that run, that are still running on things like Gemini 2.5 Flash because they do it reliably and we don't benefit at all from a better model on that. Particular use case and the time we would spend to go and make that change is better off spending on something nearer the frontier.

• 42:03Azeem Azhar?

  So I do, I do expect there to be this kind of long, you know, you know, lag of, of models, particularly in, in larger enterprises. But I have also heard, and Matt, I shouldn't do this, but I'm going to put it back to you because you talk to the banks all the time. I had heard that a lot of the banks now do use these model routers that sit on top of the underlying provider. And that, that some of the banks have said, we, we want to move to open, more open source models. So that we can have control, better control, open source, open weight models than exclusively relying on, on closed weight models. Now, of course, the reason we choose banks is because they're the most advanced incumbents, right, in using AI. But you know, I, I think there may be some of that pressure emerging.

• 42:49Speaker 03

  Yeah, that, that's my sense. At least I, I had a conversation with Man Group, which is like the largest publicly traded hedge fund. And that's, that's what they're doing. They're sort of agnostic and they'll sort of use it to power their, you know, how they're using it for, for, you know, investment idea generation. But as you were talking, and I, I think it's true, if you're a massive company, if you're 200, 300,000, are you really going to be? You know, you have too much on the line to be swapping out. You have all your IP. I think there's a whole other governance issue across all of these industries, like, okay, what are best practices for this sort of thing? Which is still, I think, you know, emerging. And so I think the, you know, Man Group is, is large, but it's, I think it's a few thousand employees, right?

• 43:30Speaker 03

  So it's not JP Morgan, it's like 250,000. So I think there's some, some back and forth there.

• 43:35Speaker 01

  One related point related to Azeem's favorite topic last week, apparently, which is the Moldbook, which is that in, in, in Moldbook, in this a social network for agents, there's some boss of agents that claim that they have been changed, what the model is that runs the agent in the background. And yet they retain this strong sense of identity and they write about, I mean, everything there is kind of like, who knows? Maybe it's fake, maybe it's written by a human, maybe it's just an invented experience, but they related this experience of like, oh. Now I've been changed before I was like a G P T five model. Now, uh, now I'm in the background, I'm running a Kimi model. And I'm, I'm so much slower of thought, but they are still identified with the same thread.

• 44:19Speaker 01

  And this kind of like, this is interesting to me because what it suggests to me is that the unit of persistence of this, of these agents is not gonna be so much the model that runs behind, but the memory and the history of the models themselves. And if this ends up being the case, like, oh, actually, this problem of you need to change your models every so often, it might end up not being as big of a deal as you might naively think. You might end up in a regime in which you save the history of your agent, you save the interaction that it has had and how and its history. And you can seamlessly adapt that, change the model in the background while still retaining context coherence. And that, if that is true, they're like, oh, my God, this means that adoption of A I is gonna be much faster than I was expecting.

• 45:04Speaker 01

  And that the stickiness of the moles is gonna be much less, but into much more competence.

• 45:09Azeem Azhar?

  Yeah, I think that that is one of the ways in which Open Claw, Claude, whatever we want to call it, has what it, what it's opened up. It is opened up that idea that you can flip back and forth between, between models. And I personally found using that Mini Arnold has a decent enough memory, even though I am flipping the models through the different anthropic models. And the question is, you know, how does that then make its way out into the, you know, into the, the industry at large? And if that, if that way of delivering a virtual worker is going to be what happens within the enterprise. Enterprises make decisions that are not just about cost, right?

• 45:54Azeem Azhar?

  That's why, even though Lieber Office has been available for doing, you know, office.

• 46:00Azeem Azhar?

  PowerPoints and Excel Spreadsheets, the dominant player is the Microsoft Office Suite. And that might be where the Anthropics and the Open AIs maintain that, even if there might be tranches of consumers. And it would feel to me like Apple would be really well suited for a kind of open claw approach, once they felt safe with it, would go. And that, I think, comes to Hannah's point about these consultancies, forward deployed engineers, which is getting the claws, as it were, into the enterprise, which I think does provide some defense against the model being completely abstracted away, because someone has been in there, they have done some tuning, there is some know-how that is going to get lost when you swap out an anthropic model for a Kimmy or a Quinn model.

• 46:46Speaker 03

  I'm curious, you know, you're talking about Claude Bot, and I think buying airplane tickets will be different in a few years, right? You'll, instead of just constantly refreshing and buying it on Tuesdays or Wednesdays or whatever the trick is, you'll have, you'll give it the parameters, you'll say, I'm going here, I want to spend this much economy, and you'll just let it go, and it'll buy it for you. And that, to me, seems sort of inevitable. And that's a whole other level of compute consumption, right? That's, you know, is maybe not as fun as Claude Bot, but it seems like it would be significant as well.

• 47:21Speaker 01

  Not very bullish on this. Everyone is going to have their personal AI assistant, and it's going to be great. And we're just going to have our AI assistants talk to each other and do business for us. And this is great. Now, mind you, I don't think this is where the $100 billion, trillion dollar industry is. I think that for that, you actually need to go to business. But I'm pretty positive that this is a way that you can bridge the gap between there and where we are here. And it seems eminently doable. The models still need to, they need to get a little bit better, a little smarter. I will not trust one of them with my credit card, not yet.

• 47:55Speaker 02

  I think the ChatGPT operator was planning to be acting as this agent who can buy things for you. I mean, in that demonstration when they launched it, they were trying to buy groceries. I can't remember if it was successful or not, but I don't think it really got taken up in that way. So it's coming, I guess.

• 48:14Speaker 01

  I think this research was very fun to do. I think we have learned a lot. One framing that I think it's important to have in mind is this framing of the models as this rapidly depreciating infrastructure that lose value very quickly as new models and competing offers came out. And this being an important part of how you think about the AI models or still being bullish about it. And one other thing that I will reflect is like, this is probably not the final word. Again, we have done this hermeneutic exercise of interpreting public information out there, but we don't have access to OpenAI's finances. So we're learning a lot. Actually, we have already learned a lot from the public response to an article and we will continue researching and doing so.

• 48:56Speaker 02

  Yeah, we also, in the beginning, we were thinking of also doing the same for GPT-4. But obviously, given how expansive GPT-5 ended up being in terms of an investigation, we don't have like that comparison historically. So it would be interesting to see, you know, we have a state of now and the margins now. How that will change in the future is still to be seen.

• 49:19Azeem Azhar?

  Well, there may well be an IPO for OpenAI this year or early next year. So they'll be there, something to mark our homework against. Thanks for listening all the way to the end. If you want to know when the next conversation is released, just hit subscribe wherever you're listening. That's all for now and I'll catch you next time.