News vs. deployment evidence
AI Trend Radar
Compare which AI narratives are accelerating over the last 45 days and how much verified deployment evidence supports them.
- Data as of
- Aug 25, 2026
- Dataset revision
- dsr-d2824fe839d09681
- Canonical record count
- 3,811
43
16
12
News vs. evidence
The ranking
"AI agents" leads trend strength at 78/100. 16 of 17 comparable narratives gained strength over 14D. 12 of 43 narratives remain discussion-only.
How to read the ranking
Rows are ordered by the selected metric. "14D change" is the trend-strength change against the nearest historical radar snapshot; ▲/▼ and the signed point value show the direction and size of movement. "45-day coverage" is the weekly news shape and total in the current snapshot. "Trend strength" is the current 0-100 score, "Confidence" is how much independent, complete evidence supports that score — treat a low value as a thin narrative — and "Evidence" is the deployment stage corroborated by the catalog.
Quiet0–<15Emerging15–<35Building35–<55Rising55–<75Surging75–100The lead is based on 76 news items over 45 days. Discussion-only means no deployment evidence has been corroborated by multiple sources yet.
43 of 43 narratives
Refine by signal and category
ⓘ How this is measured
Narratives (themes) are extracted from our continuously monitored AI-news and community stream — including public Reddit Atom feeds that require no Reddit API credentials — by an LLM classifier and normalized. One grounded mention creates an admin-review candidate; two recent mentions from independent source domains make it emerging; and three items make it established. Candidates stay off the public radar until they reach an admitted stage within the 45-day window. Each narrative is also classified against a stable topic-kind schema (model, technique, infrastructure, market/capital, policy…) by majority vote across its items — the schema's categories stay fixed over time so kind-level trends remain comparable across snapshots, and quadrants derive from them. Each news item additionally carries an event type (launch, funding, acquisition, adoption…), shown as the blip's event mix. Individual companies, products, and use-case types appear only when they dominate the conversation (at least 12 items with 6+ in the last 14 days) — shown as diamonds. A vendor's own newsroom never counts toward that vendor's dominance: only independent coverage does, and any own-channel share is disclosed on the blip.
The evidence ring comes from the use-case catalog: each news item is linked to published deployment cases by embedding similarity, but a case only counts toward the ring when at least two independent articles point to it (a single match is weak signal). A narrative's corroborated-case count determines its evidence strength — Strong (5+), Moderate (2–4), Limited (1), or No corroborated evidence (0). A narrative also has to carry the conversation itself before it can leave the outer ring: below 6 independent news items it stays uncorroborated however many catalog cases it resembles, because a handful of articles clears the case floors on embedding resemblance alone. Most narratives sit in the outer uncorroborated ring: far more of the AI conversation is talk than is supported by corroborated deployment cases. Marker size, color, and the primary ranking use a deterministic 0-100 trend-strength score combining velocity, acceleration, novelty, source diversity, persistence, narrative coherence, credible-author authority, deployment impact, and strategic relevance; missing factors are excluded and the available weights are renormalized. Confidence is scored separately from evidence volume, source breadth, temporal coverage, independent coverage, deployment corroboration, and data completeness. Unprofiled authors stay neutral at 1×; a conservatively curated voice counts 2× only when the narrative matches that person's configured expertise, and 1.1× otherwise. Employer alone never grants a boost. The raw credibility-adjusted momentum, source mix, lift, people, and weights remain visible in each selected narrative. ▲/▼ mark acceleration or cooling only when a real baseline exists. A dashed halo marks narratives new versus the previous daily snapshot. Select any blip to inspect the headlines, voices, and cases behind it.
Sources we monitor
- Reddit – r/LocalLLaMACommunity
- Reddit – r/artificialCommunity
- ZDNET – AIPress
- TechCrunch – AIPress
- The Verge – AIPress
- Reddit – r/MachineLearningCommunity
- AWS Machine Learning BlogVendor blog
- Web – RAG in productionWeb search
- Ars Technica – AIPress
- Hacker News – LLMCommunity
- Web – enterprise AI agentsWeb search
- Watchlist - forward deployed engineer (FDE)Web search
- Watchlist - Hypervelocity EngineeringWeb search
- Watchlist - self-improving agentsWeb search
- Google Cloud – AI & MLVendor blog
- Watchlist - loop engineeringWeb search
- Watchlist - ai securityWeb search
- Hugging Face BlogVendor blog
- Watchlist - ai governanceWeb search
- The AI Daily BriefPress
- MIT Technology Review – AIPress
- Hacker News – AI agentsCommunity
- The Register – AI/MLPress
- Google – The Keyword (AI)Vendor blog
- One Useful Thing - Ethan MollickPress
- VentureBeat – AIPress
- Watchlist - self-reinforcing agent-loopsWeb search
- Microsoft AI BlogVendor blog
Latest evidence
What is moving these trends
The newest source items behind the radar, with a short outtake and the narratives they support.
Showing 3 of 91
Watchlist - Hypervelocity Engineering
today
[Remote] Multidisciplinary Manager (Software Engineering) - FDEA software engineering management role involves leading multidisciplinary teams in the practical adoption of responsible AI practices, accelerators, and reusable engineering patte…
responsible AI practicesreusable patternshypervelocity engineeringWeb – enterprise AI agents
today
AI governance and accountability: enterprise best practices - DataikuThe article outlines governance practices for enterprise AI agents, including defining and documenting each production agent’s permitted scope of action. It frames accountability…
responsible AI practicesAI agent governanceWeb – enterprise AI agents
today
Enterprise AI Agents Adoption Statistics 2026 - Paul OkhremThe item discusses enterprise adoption of AI agents, emphasizing production deployments tied to business revenue. It cites a forecast that task-specific AI capabilities will be em…
AI agentsenterprise AI adoption