AI content and Google: separating what's true from what's repeated

There's a persistent belief that Google penalises AI-written content on detection. That isn't what Google's guidance says, and it isn't what the ranking data suggests.

The reality is both less alarming and more demanding.

What Google's position actually is

Google's stated position is that it rewards helpful, reliable, people-first content regardless of how it's produced. Using automation to generate content primarily to manipulate rankings violates their spam policies — but that's about intent and quality, not about which tool typed it.

The practical translation: nobody's checking whether a model wrote it. They're checking whether it's worth reading.

Why AI content often does perform badly

Not because of detection. Because of what it typically is:

It's derivative. A model trained on existing content, asked to write about a topic, produces a competent synthesis of what already exists. Competent synthesis of existing content is precisely what search engines have plenty of.

It lacks specifics. No original data, no first-hand experience, no numbers from an actual account. Specificity is what distinguishes sources, and generic content has none.

It's produced at volumes that signal intent. Fifty posts appearing in a week is a pattern, and patterns get evaluated.

Nobody edited it. Published without review, it carries the tells — hedged claims, tidy symmetrical structure, conclusions that restate the introduction.

What actually works

AI-assisted, human-directed, experience-grounded.

The posts on this site are written with AI assistance. They're also grounded in things I've actually done — accounts I've run, builds that broke, numbers from real work. The model helps with structure and speed. The substance has to come from somewhere real.

That combination performs because it produces content that couldn't have been generated without the underlying experience.

The practical test

Before publishing, ask: could this have been written by someone who has never done this?

If yes, it will read as generic and rank as generic, regardless of who or what wrote it.

Add what only you have:

• Actual numbers from actual work
• A specific failure and what it cost
• A view that contradicts consensus, with reasoning
• Details someone would only know from doing it

On disclosure

Google doesn't require you to disclose AI assistance. Whether you do is a trust decision rather than a compliance one.

My position: I'd rather say plainly that I use AI in the process, because pretending otherwise while writing about AI would be an odd contradiction. What I won't do is publish something I haven't verified and can't stand behind.

What I'd avoid entirely

• Publishing unedited output. Ever.
• Content at volumes you can't review. If you can't read it, don't publish it.
• Generated statistics. Numbers must come from a source you can name.
• Fabricated experience. Writing "in my experience" about something you haven't done is the fastest way to be caught out by someone who has.

That last one is the real risk with AI content in expert niches. The model will happily write confident first-person experience it doesn't have, and readers who know the subject will spot it immediately.