Fast-growing companies rarely quote last year’s sales. They quote a run rate. Take the most recent month and multiply it by 12. That is the run rate.
A Worked Example
The video uses round numbers. A company made $10 million in January and $100 million in December. Its run rate is $1.2 billion. But what it actually earned over the year was far less, about $500 million.
Both numbers are true. They answer different questions. The run rate says how fast the company is moving now. The annual figure says how much it actually earned.
The run rate shows the speed; the annual figure shows the distance. Round numbers for illustration.
Run Rate and Recurring Revenue
A related term is annual recurring revenue. It counts only revenue under contract that repeats, like subscriptions. Run rate is looser. It can include usage that may not repeat next month.
Why AI Companies Use It
AI companies favor run rate because they are growing so fast that last year’s total is already out of date. That is fair.
But much AI revenue is usage-based: customers pay for what they consume, and consumption can fall as easily as it rose. So a run rate is a snapshot, not a promise. One unusually good month, multiplied by 12, becomes an unusually good year that never happened.
Three Questions to Ask
Which month was multiplied?
How much of it is under contract?
What did the company actually earn over the last 12 months?
Run-Rate Revenue builds on Usage-Based Pricing. Much AI revenue is usage-based and may not repeat, which makes a month times twelve looser than recurring revenue.
Backlog contrasts with Run-Rate Revenue. Backlog is signed but unearned revenue; run rate projects the latest month’s earned revenue forward.
Run-Rate Revenue contrasts with Compound Annual Growth Rate(lesson unlocks soon). Run rate multiplies one recent month by twelve; CAGR gives one steady rate across many bumpy years.
The Business Engineer framework maps where each part of the AI stack sits and who holds the leverage. These pills are short lessons from the same library.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
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