🔮 X-raying OpenAI’s unit economics

AI companies are being valued in the hundreds of billions. $650 billion in capital expenditure commitments are being made by big tech for 2026. Yet one question remains unanswered: does it make economic sense?

We recently partnered with Epoch AI to analyze GPT-5’s unit economics, and figure out whether frontier models can be profitable (full breakdown here).

To dig deeper into what our results tell us about the wider industry, we hosted a live conversation last week between myself, , , moderated by .

We cover:

• The research findings,

• Possible paths to profitability,

• OpenAI vs Anthropic playbook,

• Winning the enterprise

• Why this research made some bulls more pessimistic

• What the market gets wrong.

Watch here:

Listen here:

Or read our notes:

What did you actually find?

Matt: For someone just getting into the research, what’s the big takeaway — and how did you even think about building a framework to analyse a business like this?

Jaime: To our understanding, no one had really taken on this task of piecing together all the public information about the finances of OpenAI — or any large AI company — and trying to paint a picture of what their margins actually look like. So we did this hermeneutic exercise of hunting for every data point we could find and trying to make sense of it.

The two most important takeaways: first, it seems likely that OpenAI during the past year, especially while operating GPT-5, was making more money than the cost of the compute — which is the primary expense of operating their product. But they appear to have made a very thin margin, or even lost money, after accounting for all other operating expenses: staff, sales and marketing, administrative costs, and the revenue-sharing agreement with Microsoft.

Second — and this is the part I found quite shocking — if you look at how much they spent on 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 entire tenure of GPT-5 and GPT-5.2.

Hannah: A lot of our methodology was based on numbers we could find historically, then trying to project what would happen through the rest of 2025. For example, we had data showing 2024 was $1 billion in sales and marketing, and H1 of 2025 was $2 billion. So we built the picture using constraints like this, ...