# That-clause subjects Part of [The AI Tells Index](https://feedsquad.com/ai-tells). A tell signals low effort. It does not identify an author. Skilled writers produce every shape listed here, some of them daily, and automated detectors misread those writers for it at rates measured above 60 percent on non-native English prose. Nothing in this index proves that a machine wrote anything. Read an entry as one piece of evidence to weigh against the false-positive notes printed beside it. ## Facts - Id: that-clause-subject - Category: Structural and syntactic (structural) - Subcategory: clausal-subjects - Also known as: The fact that X shows, fronted clausal subject - Status: Active. Currently signals low-effort writing. - Severity: low - Evidence grade: peer-reviewed (a published study measures the pattern) - Languages: en - Added: 2026-08-15 - Updated: 2026-08-17 - Page: https://feedsquad.com/ai-tells/that-clause-subject ## Description A sentence opens with a that-clause standing in as its subject, and the main verb then evaluates the proposition. Reinhart and colleagues count the construction at 2.6 times the human rate in GPT-4o output, with a Cohen's d of 0.77, the weakest effect in their grammatical table. The figure is body-level. Low severity is deliberate: the evidence here is strong and the signal is thin. ## Why it reads as machine-written Fronting a proposition and then judging it is the shape of a sentence about to announce that something matters. On its own the construction means close to nothing, and plenty of good writing uses it. It earns a place in this index as a cadence marker that travels with the heavier tells, never as a finding by itself. ## Detection Type: statistical (a measured metric against a threshold with a stated basis) Metric: that-clause-subjects-per-1000-words Threshold basis: > No threshold ships. The 2.6 times ratio is a body-level population comparison and the effect size is the smallest in the source table, so a cutoff drawn from it would separate genres rather than drafts. Use the count as a FeedSquad review trigger alongside the other structural measures, and never on its own. ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > That the pilot ran without a single rollback shows that the deployment process was sound. That nobody raised a concern during the review suggests the team was comfortable with the approach. What the wider rollout will show is harder to say at this remove. That confidence is earned rather than announced is a thing organisations discover for themselves, usually more than once. After, repaired: > The pilot ran six weeks with no rollbacks, after the team moved deploys to Tuesday mornings and added a smoke test on the payments path. Note: Every sentence fronts a proposition and then grades it, and the grading is all the passage contains. The repair states the fact and names the two changes that produced it. Figures in this repair are invented for the specimen. ## False positives Who legitimately writes this way. Philosophy, linguistics and mathematics prose uses clausal subjects as standard equipment, because those sentences really are about propositions rather than about events. Any writing that discusses claims will produce them at a high rate, and so will a paper reporting what a result shows. Treat a hit as noise unless the surrounding text is narrative or promotional copy, where the construction has no work to do and is standing in for a plain statement. ## Model attribution Measured in GPT-4o output. Reinhart reports the gap widening for instruction-tuned models relative to base models. No vendor names the construction in published guidance. ## Sources 1. Reinhart et al., Do LLMs write like humans? PNAS 122(8) (arXiv:2410.16107) https://arxiv.org/abs/2410.16107 (tier: peer-reviewed; accessed 2026-08-14) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-15, Active: Published at 2.6 times the human rate in 2024-era model output, with no later study reporting a change. Severity is set low because the effect size is the weakest in the source table and the construction is ordinary in several genres. ## License CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ Attribution: The AI Tells Index, feedsquad.com/ai-tells Reuse the data, including commercially. Keep the attribution line. --- Dataset version 1.0.0. Schema version 1. Part of [FeedSquad](https://feedsquad.com). Built by [Herman Foundry](https://hermanfoundry.com) from Levi, Finnish Lapland.