Inside the economics of OpenAI (exclusive research)
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Do the economics of AI companies actually work?
Today, artificial intelligence companies are now being valued in hundreds of billions of dollars. It's open AI.
What financial insights can we glean from OpenAI's public data?
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.
What challenges does GPT-5 face as a rapidly depreciating asset?
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. 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.
Why is OpenAI considering advertisements for revenue?
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?
What would you do differently if you were Sam Altman?
Now, as a reader's 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. 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.
Where do the real infrastructure bottlenecks lie in AI?
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, and Hanna 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 what I say, well, is that roughly right for Hanna?
What does surging compute demand look like in the AI industry?
Is it within a few orders of magnitude? Often, yes. Matt, I'm handing the stage over to you. The floor is yours.
What is the most surprising finding from the research?
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. 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 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.
But 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 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.
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Chapters
8 chapters
1
Do the economics of AI companies actually work?
0:00–0:05
2
What financial insights can we glean from OpenAI's public data?
0:05–0:13
3
What challenges does GPT-5 face as a rapidly depreciating asset?
0:13–1:02
4
Why is OpenAI considering advertisements for revenue?
1:02–1:16
5
What would you do differently if you were Sam Altman?
1:16–1:40
6
Where do the real infrastructure bottlenecks lie in AI?
1:40–2:20
7
What does surging compute demand look like in the AI industry?
2:20–2:27
8
What is the most surprising finding from the research?
2:27–49:38
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