The AI Revenue Numbers Everyone Repeats Are Probably Wrong
The numbers people throw around are staggering. “OpenAI is on an enormous run rate.” “Microsoft’s AI business is bigger than people think.” “Anthropic revenue is exploding.” These claims circulate on social media, get picked up by newsletters, and harden into accepted lore before anyone checks the source.
Most are wrong. Or at least decontextualized to the point of being misleading. They come from copied screenshots, anonymous sourcing, or analysts triangulating from a competitor’s filing. The work required to actually verify an AI revenue claim is boring, slow, and hard. That’s exactly why it separates real analysis from noise.
The Hierarchy of Evidence
There is a pecking order for AI revenue data. Most people skip straight to the bottom.
At the top sit audited financial filings: 10-Ks, 10-Qs, annual reports. These carry legal liability for misstatement. Below that, official press releases and earnings webcasts — still binding, though less detail-rich. Below that, investor presentations and board decks. Below that, company blog posts and CEO interviews. At the very bottom: social media screenshots and secondhand reporting from outlets that didn’t file anything.
The gap between audited filings and social screenshots is enormous. A 10-K breaks out revenue by segment, explains accounting methodology, and discloses related-party transactions. A tweet says “sources say.” One is evidence. The other is entertainment.
Reading the Actual Filing
Take Microsoft’s 2025 Annual Report, available on their investor relations page. Hundreds of pages of management discussion, audited financials, and risk factors. The relevant AI revenue data isn’t in a neat box labeled “AI Revenue.” It’s distributed across segments: Intelligent Cloud, Azure, and the “More Personal Computing” group where Copilot subscriptions live.
The quarterly earnings press release for FY25 Q4 gives a more granular view: Azure revenue growth percentage, the contribution from AI services, and commentary about enterprise adoption. These numbers are footnoted, segmented, and traceable back to specific accounting policies.
A social media post claiming that Microsoft’s AI business has reached a specific run rate might derive from these same numbers. But the derivation requires assumptions about what counts as “AI.” Azure AI infrastructure? Copilot for M365? GitHub Copilot? The API usage from OpenAI running on Azure? The filing draws the boundaries. The screenshot doesn’t.
What to Actually Look For
Open a filing or earnings release and look for three things: segment definitions, growth rates over comparable periods, and the management discussion of what drove the change. Microsoft’s filing gives the actual segment language and management commentary. It doesn’t say “AI is huge.” It defines what is reported, what is attributed, and what remains outside the line item. That precision is the whole point.
Also watch for what’s not broken out. If a company reports “AI revenue” as a single line without segment detail, that’s a signal — either the number is too small to disclose separately, or the definition is too broad to be useful.
The Discipline Problem
Bad AI revenue claims survive because primary-source verification is genuinely tedious. You need to know which filing to open, which section to read, which accounting standard applies, and how the segment definitions changed year-over-year. Most people don’t have the time or the training.
The fix isn’t to make everyone a forensic accountant. It’s to demand that anyone making a specific revenue claim cite the exact filing, section, and table that supports it. If they can’t, the claim is speculation. The burden shifts back to the reader.
The AI industry will keep generating extraordinary numbers, both real and fabricated. The only way to tell them apart is to read the documents that carry legal consequences for being wrong. Everything else is just a copied screenshot.
