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OpenAI Revenue Report — FUTURESEARCH
🌈 Abstract
The article discusses the methods used by FutureSearch, an AI tool, to estimate the Annual Recurring Revenue (ARR) of OpenAI's products, including ChatGPT. It outlines the three-step process FutureSearch uses: primary and secondary research, modeling the domain, and estimating the unknown.
🙋 Q&A
[01] Methods
1. What are the key steps FutureSearch takes to estimate the ARR?
- FutureSearch conducts broader research than other AI tools, including finding personal anecdotes, blog posts, and interview transcripts that may not be in highly-ranked search results.
- FutureSearch also fact-checks web sources when they conflict, removing data points that other AI tools may report as correct.
- FutureSearch models the domain by doing calculations, proposing ways to infer the answer, and exploring multiple lines of research until it finds a sufficient approach.
- FutureSearch handles uncertainty by using Fermi estimates from known data points and reasoning from analogous data points for unknown information.
2. How does FutureSearch's approach differ from other AI tools?
- Other AI systems may report factually incorrect information by substituting the number of mobile subscribers for all GPT Plus subscribers or misinterpreting the number of paying ChatGPT users.
- FutureSearch's research involves fact-checking and removing data points that other AI tools may parrot as correct.
3. What techniques does FutureSearch use to handle uncertainty in its estimates?
- Fermi estimates from known data points, such as extrapolating growth rates for ChatGPT Plus and Enterprise subscribers.
- Reasoning from analogous data points, such as using the ratio of individual to teams paying users for other apps to estimate ChatGPT Teams subscribers.
[02] Reporting
1. How does FutureSearch present its findings?
- FutureSearch reports the top-level answer as simply as possible, with the full justification shown across a linked set of pages, each answering a specific subquestion.
- The FutureSearch dropdown subquestion menu allows users to research the components of the question that they care most about.
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