Most of what's sold as AI search optimization has never been tested by the people selling it, and this episode is me checking the biggest one against server logs. Ahrefs looked at 137,000 domains in June: 97% of llms.txt files got zero requests in May, and the biggest readers of the rest were SEO audit tools. I also lay out the line I use to sort every pitch: a hack tries to influence what the machine says about you, architecture changes what a machine can read and do on your website.

Chapters

00:00 The AI optimization economy and its zero evidence
02:50 llms.txt checked against 137,000 domains
06:00 The main readers of llms.txt are SEO audit tools
08:53 Why this market keeps producing hacks
10:23 Visibility scores and the prompt problem
12:43 Every generation of hacks dies the same way
15:08 What survives model updates
18:39 The mirror: fix what the internet thinks you are

Key Numbers

  • 137,000 domains in Ahrefs' June 2026 server-log study
  • 28% had a valid llms.txt file, and that number is the ceiling, their customers skew technical
  • 97% of those files got zero requests in May, not low traffic, zero
  • Of the 3% that got fetched, around 22% of the readers were SEO audit tools, the tools that flag you for not having the file
  • My own Cloudflare logs at nohacks.co show the same thing, nobody fetches it

Three Takeaways

  1. The pitch is a screenshot, the truth is in the logs. Before you pay for any AI visibility work, ask for evidence at the level of server logs, and watch what happens.
  2. A hack tries to influence what the machine says about you. Architecture changes what a machine can read and do on your website. The first is rented and dies at the next model update, the second is owned.
  3. LLMs are a mirror of everything happening online. If ChatGPT doesn't call you the best X for Y, the honest question is whether the internet agrees you are, and that's the problem worth fixing.

What to Do

  • Sort anything you bought or got pitched this quarter with one question: does it change what a machine can read and do on the website, or what the machine says about it?
  • Ask any vendor for their evidence before money leaves your account. Logs, tests, a mechanism, the same bar you'd use for anyone touching revenue.
  • Check your own server logs for who actually fetches your llms.txt, it takes five minutes
  • Try this: open free ChatGPT logged out of search, type "what is [your name] known for," and send me the screenshot at hi@nohacks.co. I want to see what you get.

Sources

No Hacks runs no sponsorships and is funded by advisory and audit work. 

If your website needs to work for machines as well as people, start with a fixed-scope Machine-First Architecture audit: https://nohacks.co/audit

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