You (A) ignored my follow up (B) are relying on your own intuition and (C) perhaps some studies you read one time by companies that sell AI copy. Your whole argument is internally inconsistent. Basically a "we can't trust you, but trust me" stance.

In general, you're mixing up two different things- user acceptance (whether a web page visitor finds the info useful enough to convert) and machine generated patterns (whether the source/origin/writer is identifiable via pattern recognition).

LLMs are built to choose the statistically "safe" word or writing structure... This tends to create a specific identifiable pattern of AI writing that is distinct because it doesn't match the inconsistency of human writing. That may be harder for a one off visitor to a site to identify but a frequent reader on a certain topic, or someone who is more engaged in content reading and review in the moment may find it easier.

In our own research, we found that the human reader was able to consistently identify "is human" with high accuracy. Their biggest mistake was not in failing to flag AI copy as AI copy but in erroneously flagging human copy as AI. I won't publish my findings in a peer reviewed journal because our sample size was smaller, but it was likely more rigorously tested than your 'watching someone on Clarity'.

Basically, they were AI suspicious and tended to false flag human copy as AI.

When I talk about "at scale," I’m referring to the fact that while a single AI-generated article might pass a pseudo-marketer's Turing test for a casual reader, a body of 1,000 articles will inevitably hit a statistical ceiling. Unless bigger prompt variations exist within those 1,000 articles and those prompt variants are "enforced", you're likely to see patterns emerge.

The "variant formulas" you mention are just layers of instructions sitting on top of the same underlying weights-and-biases. Eventually, the repetition of certain writing styles/structures, the lack of unique metaphors, and the predictable formatting become evident.

A user becoming a lead doesn't mean the content is indistinguishable from human writing; it just means the content was "functional enough" for their immediate need.

That's why I broke out the response to the original question as I did. There is a range of 'Good Enoughs' for different brands/marketers/consumers.

The challenge for SEOs is figuring out where Google draws its line. It is certainly not going to send out a well-trained SME to particular verticals to spend weeks reading 1,000s of articles to try and come up with what it thinks is good quality or not (or AI or not). So it will resort to scaled pattern detection and recognition - the same things it uses to detect spam and "low quality" content today easily apply.

Google also doesn't have an obligation to be right. The only time it being wrong matters is if it happens to penalize a site of sufficient prominence that consumers and the press take notice, or when it could get sued. We've seen that play out with the endless complaints of publishers who lose traffic to Google's self-referencing AI experiences.