On March 30, 2026, I took a screenshot of my metrics. Domain Authority: 14. Linking domains: 21. Monthly users: 206. Pageviews: 819. Indexed pages: 48 of 87.
That day I stopped thinking of Google as my primary audience.
I didn't abandon SEO. I didn't delete my meta descriptions or burn my sitemap. What I did was invert the priority. Instead of asking how do I rank better at position 3 for this keyword, I started asking how do I get ChatGPT to cite me when someone asks about this topic.
Four months later, I have enough data to evaluate whether the bet worked.
What I built in 4 months
The complete stack: llms.txt, Content Signals, Markdown for Agents, Agent Skills, agent-permissions.json, agents.json, automatic JSON-LD Knowledge Graph, auto-linker, trilingual RSS, llms.txt generator tool, GEO Tarot, 22 Generative Engine Optimization concepts documented. All with vanilla PHP on shared hosting.
The numbers: March vs July 2026
Domain Authority: 14 → (current). Linking domains: 21 → (current). Monthly users: 206 → (current). Indexed pages: 48 → (current). Platform presence: 3 → 9.
I'll update these with real data before publishing.
What worked
Structured content with definitions, data, and comparisons consistently generates more engagement than pure narrative. This confirms exactly what the 602 prompts study predicted.
What didn't work as expected
Adoption speed of agent standards is slower than I anticipated. No commercial agent officially respects agent-permissions.json yet. AI crawlers don't visit llms.txt as frequently as expected.
The updated thesis
My original thesis was: if I build the right infrastructure now, when AI agents mature, my site will have an advantage. Four months later, the thesis holds but with a nuance: the advantage isn't just technical. It's learning.
Every standard I implemented forced me to understand how agents process content. That understanding informs every content decision I make. I don't just have the right infrastructure. I have the right mental model for creating content that will be relevant in a world where machines mediate most interactions with information.
The question isn't whether it's worth optimizing for machines. The question is how long you can afford not to.
