Discourse monitor
How the monitored publications were assessed
Discourse valence across every scored item: what the sampled sources published and how independent models read it. It is not a measure of public opinion, not a measure of the state of the world, and article volume is not agreement.
Mean discourse valence per day · power-concentration · last 30 days
This line is the mean of every item scored on each day. It deliberately ignores the reading filter, because a daily mean of only the mixed or uncertain items would move with how many were filtered out rather than with the discourse.
Discourse valence, mixed or uncertain
-2
Mixed or uncertain
38 items · range -18 to +15
-100
-50
0
+50
+100
-2
Adverse readingFavourable reading
38 items read as mixed or uncertain
- China unveils draft export controls on AI technologies and chips
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks
- OpenAI Engineer, Who Is Charged With Planting Bug, Advised Saudis
- The onward march of cognitive capitalism
- China’s extraordinary rise in science and how it will change the world
- AI Alignment for Digital Democracy: Fairer Voters Representation against LLM Biases
- China Condemns U.S. Ban on DeepSeek A.I. in Government Systems
- Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training
- Developing Gemini 4 Argon responsibly and safely
- StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents
- Nvidia, worth $5.5tn, donates $250m to UK for AI safety research
- Gemini 4 Argon: Built to solve your hardest problems
- Democratic governance of frontier AI, part II: Empowering cyber defenders
- Plan for AI-run companies challenged by US lawmakers
- Why does America hate AI?
- From Regulation to Engineering: Organizational Privacy Climate as Mediator
- Distilling Expert Judgment at Scale
- Beyond the Monolithic Interface: A Research Agenda for Plural Conversational AI
- Ghost in the machine: how AI is haunting the search for the supernatural
- AI agents need middle managers too
- Whether Is Not When: Separating Occurrence from Timing in Polymarket Prices
- ChatGPT’s expanding world of work
- To Manipulate or Not: Opinion Dynamics in LLM Interactions
- Peer review should constrain evaluative authority
- Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle
- Anthropic turns to lawyers to help AI agents strike deals
- Structural Dependence, Imitation or Herding? A Multi-Agent Simulation of Global Intermedia Agenda Setting
- Amp puts AI agents to work beyond code with OpenAI models
- Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn
- Maintaining Human Verification Capacity under Automation
- Shaping & Monitoring Models through their Internal Representations
- Anthropic warns of 'catastrophic risk' to humankind ahead of blockbuster share listing
- ‘Bold but sane’: what we learnt from Anthropic’s 240,000-word IPO filing
- ‘Control and sovereignty’ to shape UK approach to AI
- The EU AI Act Beyond Europe: A Practical Readiness Framework for Non-EU Organisations. Comparative Evidence from Four African Countries
- Privacy Preference Controls for Agentic Systems
- Regulating for an AI Commons: Adopting, sustaining, and defending people-first innovation trajectories
- Artificial Intelligence and Job Design: A Dynamic Framework
3560
discovered, all time
984
passed triage
2574
rejected at triage
3464
model assessments
710
multi-model panels
1780
distinct claims tracked
Across the whole window
The two tables below describe every item in the last 30 days under power-concentration. They are deliberately not narrowed by the reading filter (currently mixed or uncertain), because a mean taken only over items already selected for their score would just restate the filter.
By topic
| Topic | Branch | Items | Mean | Position |
|---|---|---|---|---|
| Existential and catastrophic risk | risk | 568 | -42 | |
| Governance and regulation | structural | 459 | -31 | |
| Concentration of power | risk | 451 | -36 | |
| Loss of control and alignment | risk | 337 | -49 | |
| Human agency and epistemics | risk | 166 | -26 | |
| Cyber capability | risk | 165 | -44 | |
| Evaluation and measurement | structural | 159 | -19 | |
| Scaling and architecture | structural | 137 | -16 | |
| AGI and timelines | structural | 132 | -29 | |
| Synthetic media and manipulation | risk | 104 | -44 | |
| Employment and displacement | risk | 62 | -31 | |
| Medicine and health | benefit | 43 | -14 | |
| Scientific acceleration | benefit | 42 | -4 | |
| Education and access to expertise | benefit | 29 | -17 | |
| Biological and chemical uplift | risk | 26 | -25 | |
| Climate and energy | structural | 20 | -33 | |
| Compute and infrastructure | structural | 18 | -8 | |
| Productivity and growth | benefit | 6 | -3 | |
| Abundance and material wellbeing | benefit | 5 | -1 | |
| Hype and disillusionment | structural | 5 | -45 |
By source
A source's mean says how the items it published were read. It is not a rating of the source.
| Source | Kind | Tier | Items | Mean |
|---|---|---|---|---|
| The Guardian: Artificial Intelligence | news | 1 | 241 | -52 |
| arXiv cs.CY (Computers and Society) | academic | 1 | 217 | -24 |
| Financial Times: Artificial Intelligence | news | 1 | 112 | -40 |
| arXiv cs.AI | academic | 1 | 99 | -15 |
| The New York Times: Technology | news | 1 | 60 | -36 |
| Ars Technica: AI | news | 1 | 59 | -51 |
| MIT Technology Review: AI | news | 1 | 34 | -42 |
| Nature | academic | 1 | 31 | -25 |
| BBC News: Technology | news | 1 | 29 | -35 |
| OpenAI | lab | 1 | 24 | -8 |
| arXiv cs.LG (Machine Learning) | academic | 1 | 24 | -16 |
| IEEE Spectrum: AI | news | 1 | 19 | -19 |
| The Economist: Science and Technology | news | 1 | 10 | -37 |
| CSET, Georgetown | government | 1 | 9 | -37 |
| Microsoft Research | lab | 1 | 8 | 0 |
| Google DeepMind | lab | 1 | 6 | -4 |
| Google AI Blog | lab | 1 | 2 | -9 |