We estimate that, as of June 12, 2024, OpenAI has an annualized revenue (ARR) of:

 $1.9B for ChatGPT Plus (7.7M global subscribers),
 $714M from ChatGPT Enterprise (1.2M seats),
 $510M from the API, and
 $290M from ChatGPT Team (from 980k seats)

(Full report in https://app.futuresearch.ai/reports/3Li1, methods described in https://futuresearch.ai/openai-revenue-report.)

We looked into OpenAI's revenue because financial information should be a strong indicator of the business decisions they make in the coming months, and hence an indicator of their research priorities.

Our methods in brief: we searched exhaustively for all public information on OpenAI's finances, and filtered it to reliable data points. From this, we selected a method of calculation that required the minimal amount of inference of missing information.

To infer the missing information, we used the standard techniques of forecasters: fermi estimates, and base rates / analogies.

We're fairly confident that the true values are relatively close to what we report. We're still working on methods to assign confidence intervals on the final answers given the confidence intervals of all of the intermediate variables.

Inside the full report, you can see which of our estimates are most speculative, e.g. using the ratio of Enterprise seats to Teams seats from comparable apps; or inferring the US to non-US subscriber base across platforms from numbers about mobile subscribers, or inferring growth rates from just a few data points.

Overall, these numbers imply to us that:

  • Sam Altman's surprising claim of $3.4B ARR on June 12 seems quite plausible, despite skepticism people raised at the time.
  • Apps (consumer and enterprise) are much more important to OpenAI than the API.
  • Consumers are much more important to OpenAI than enterprises, as reflected in all their recent demos, but the enterprise growth rate is so high that this may change abruptly.
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I can't tell from the methodology: how much of this research was done autonomously by AI, and how much human intervention was there?

All of the research was done by FutureSearch, so AI, with a few exceptions, such as https://app.futuresearch.ai/reports/3Li1?nodeId=MIw9, where it couldn't infer good team/enterprise ratios from analogous products where numbers were reliable. Estimating ChatGPT Teams subscribers was the hardest part, requiring the most judgment.

Most of the final words in the report were written or revised by humans. We put a high quality bar on this to publish it publicly, and did more human intervention than normal.

Thanks for the breakdown!  I was surprised to see the percentage of revenue that came from individual ChatGPT Plus subscriptions was so high, but maybe I shouldn't have been given how slow the enterprise sales process is.

I did some digging into the data sources and I'm normally pretty skeptical of the kinds of data brokers that you sourced the ChatGPT Plus subscriber data from, but the enterprise data coming from OpenAI's COO directly does suggest that the rest of the revenue needs to either come from Plus or the API.  (I'm not sure about the Enterprise vs. Team ratio methodology, but I'd be surprised if it was off by a large integer multiple.)  That does leave less wiggle room, though it'd be good to get some kind of independent confirmation on either side (Plus or API usage/revenue).

Good point.  For this public report, we manually checked all the data points that were included here. FutureSearch threw out many other unreliable data points it couldn't corroborate, that's a core part of what it does.

The sources linked here are low quality data brokers due to a bug - there is a higher quality data source corroborating it, but FutureSearch doesn't cite the higher quality one. 

We're working on fixing this, and identifying all primary vs. secondary sources.

Cool, thanks!