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 · existential-risk · 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
35 items · range -18 to +18
-100
-50
0
+50
+100
-2
Adverse readingFavourable reading
35 items read as mixed or uncertain
- China unveils draft export controls on AI technologies and chips
- ClawLoop: Self-Improving Agents via Harness-Native Training Loop
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks
- '500 new vulnerabilities' found in government, says AI minister
- OpenAI Engineer, Who Is Charged With Planting Bug, Advised Saudis
- The Ineliminable Anchor: Human Judgment as a Structural Requirement of Autonomous Artificial Intelligence Systems
- Developing Gemini 4 Argon responsibly and safely
- How “reverse game theory” could keep AI agents in check
- Evaluating Model Capabilities After Benchmark Saturation
- Nvidia, worth $5.5tn, donates $250m to UK for AI safety research
- Gemini 4 Carbon: Our next frontier model
- AI Risk Ontologies for the EU AI Act Compliance: AIRO, VAIR, and their Integration with DPV
- AI researchers stranded Claude on a virtual desert island. It started to “play.”
- Distilling Expert Judgment at Scale
- ESC (DC) 2
- Trust Responsive Runtime Authorization for Agentic AI with Empirical Characterization of Policy Behavior and Parameter Sensitivity
- Thick Alignment: A Social Framework for Values and AI Systems
- Whether Is Not When: Separating Occurrence from Timing in Polymarket Prices
- What Does AI Success Look Like? A Mixed-Methods Analysis of Supranational AI Governance Instruments
- Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle
- Bringing GPT-Rosalind to academic research labs
- Becoming real
- Is this idea from AI or humans? A detector claims it can tell
- Verification and Self-Improvement in Agentic AI: Foundations and Limits
- 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
- One essential ingredient for AI safety? Democracy | Matthew Botvinick
- ‘Bold but sane’: what we learnt from Anthropic’s 240,000-word IPO filing
- How we detect frontier model misalignment
- An updated Model Spec for more capable and beneficial AI
- Anthropic gives outside AI watchdog “employee-like” access
- Regulating for an AI Commons: Adopting, sustaining, and defending people-first innovation trajectories
- AI-Native Academia Should Build a Community Verification Layer
- Objective-Conditioned Active Preference Learning for Multi-Objective Alignment
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 existential-risk. 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 |