Last week we did something stupid.

We paused all human coding. Gave Claude (Anthropic) access to our GitHub repo. Told it to build new features, fix bugs, and ship.

No human review. No guardrails. Just Claude and our codebase.

For 7 days, it ran the engineering team.

I'll go first.

Someone told me: "Just be consistent. Post every day. The algorithm rewards consistency."

So I did.

For six months, I posted every single day. Sometimes at 7am. Sometimes at 10pm. Weekends included. I wrote about our product, our features, our roadmap. I followed all the "best practices" hook in the first line, three takeaways, call to action at the end.

Six months ago, we ran an experiment with our own data.

At Rankfender, we tracked 5 of our own competitors across 8 AI systems. We log their share of voice, citation velocity, content gaps, platform variance. Months of raw numbers sitting in a dashboard.

I pulled 6 months of data and fed it into Claude. One question: "Based on this, who is most likely to overtake us in the next 6 months? Show your work. Use the data. Don't summarize. Give me the numbers."

The answer changed how I think about competition.

I'll start.

I believed that "keyword density" mattered. I spent hours making sure our target keyword appeared exactly 3-4 times per 500 words. I used tools that highlighted which words were "under-optimized." I even re-wrote paragraphs to squeeze in one more mention.

Turns out that hasn't been a real ranking factor for over a decade. Google's RankBrain (2015) and BERT (2019) made keyword density obsolete. These models understand context, synonyms, and user intent. They don't need you to say "best CRM for small business" five times. They know that "top CRM for startups" means the same thing.

What actually matters is topic coverage. Does your page answer the question completely? Do you cover related subtopics that a user would expect to see? Do you use natural language that matches how people actually ask questions?

Last month, I did something that felt slightly insane.

I took our product description, fed it into ChatGPT, and asked it to build a competitor. Not a parody. A real competitor. Better features, better positioning, better everything. I told it to be ruthless.

It did!

The output was polished. Confident. Structured like a real go-to-market plan. It named features we don t have. It positioned itself against us. It looked like a threat on paper.

Everyone is panicking about the March 2026 Core Update.
It started rolling out on March 27 and will take up to two weeks to complete .
The spam update hit just three days earlier and finished in 19.5 hours, the fastest spam update on record .

But here's what the data actually says.

JetDigitalPro analyzed 600,000 web pages across the update period. The correlation between AI usage and ranking penalties was 0.011, effectively zero . Google isn't penalizing AI content. It's penalizing low-value content that happens to be AI-generated.

Websites relying on mass-produced AI output without human oversight saw traffic drops of 60-80% . Affiliate sites were hit hardest 71% saw negative impacts .

I'll take the hit.

AI has no idea when someone is politely furious.

You know the email. "Hi team, just circling back on this again as I haven't heard anything. Thanks for your attention to this matter." Reads like a sweet grandma wrote it.

A human reads that and thinks "oh no, they are about to burn the building down." AI reads it and thinks "great sentiment, very positive, 98% satisfaction score."

For 20 years, SEO was a human game.
You wrote for people, optimized for Google's crawlers, and built backlinks by convincing other humans to link to you.
The inputs were human. The outputs were human.

GEO is different. You're optimizing for language models that extract and synthesize. The inputs are structured data, schema markup, comparison tables. The outputs are citations, not clicks.

So where does the human fit now?

What the data says about AI's performance:

We ran an experiment 2 weeks ago.

Control group: a two-hour roadmap review meeting. Six people in a room (virtual). We debated features. We argued about timelines. We discussed dependencies. We left feeling productive.

Test group: We fed the same roadmap into Claude. No slides. No politics. No one trying to protect their pet project. Just the raw plan. The prompt: "Analyze this roadmap. Identify the three most likely failure points. Use first principles reasoning. Assume we will follow your recommendations without ego. If you need more data, ask for it."

The results were not symmetrical.

We're enhancing Rankfender's Content Generation Engine (RCGE) and v2.2 is coming in the next few weeks. Before we lock things in, we want to know what actually matters to people who use content generation tools.

Here's what RCGE already does:

• Intelligence. It analyzes the top 10 ranking articles for any keyword and identifies patterns. What structure do they use? What headers? What formatting? What makes them get cited by AI? Then it builds a brief based on what actually works, not guesswork.

• Structure control. You can add, remove, and reorganize H2s before generation. No fixed templates. You decide the flow.

• Inline images. Generated articles include images, not just text walls.

• Regeneration. Mess up one paragraph? Regenerate just that part. Not the whole article.

What we're adding in v2.2: