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 · 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
92 items · range -18 to +18
-100
-50
0
+50
+100
-2
Adverse readingFavourable reading
92 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
- Google DeepMind and Singapore Government announce new AI tool to protect citizens against scams
- Stochastic Dynamics of Generative AI: From Autoregressive Models to Interacting LLM Networks and Neural SDEs
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks
- '500 new vulnerabilities' found in government, says AI minister
- The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
- OpenAI Engineer, Who Is Charged With Planting Bug, Advised Saudis
- The onward march of cognitive capitalism
- AI’s transformation of mathematics is a turning point for science
- China’s extraordinary rise in science and how it will change the world
- AI Alignment for Digital Democracy: Fairer Voters Representation against LLM Biases
- DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
- 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
- The Ineliminable Anchor: Human Judgment as a Structural Requirement of Autonomous Artificial Intelligence Systems
- Developing Competency in AI Ethics: A training framework
- Developing Gemini 4 Argon responsibly and safely
- How “reverse game theory” could keep AI agents in check
- StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents
- Evaluating Model Capabilities After Benchmark Saturation
- Whose Ground Truth? Embracing Ambiguity in Human-Centered AI
- Guarding the Gates of Knowledge
- AI Security Literacy: Ecosystem-based Competency Framework and K-12 Learning Platform
- Nvidia, worth $5.5tn, donates $250m to UK for AI safety research
- Gemini 4 Argon: Built to solve your hardest problems
- SlackBench: Benchmarking Agents on Collaborative Projects Grounded in Real Code Repositories
- Gemini 4 Carbon: Our next frontier model
- An AI Factory for Technology Education: An Agentic Pipeline with Closed Loop Audience Feedback
- The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents
- Democratic governance of frontier AI, part II: Empowering cyber defenders
- Genie 4: A new generation of world simulation
- 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.”
- Plan for AI-run companies challenged by US lawmakers
- Why does America hate AI?
- From Regulation to Engineering: Organizational Privacy Climate as Mediator
- World Bank says ‘small AI’ could transform developing countries
- Distilling Expert Judgment at Scale
- Beyond the Monolithic Interface: A Research Agenda for Plural Conversational AI
- Partnering with Hacktron to find vulnerabilities in OpenAI’s systems
- ESC (DC) 2
- Trust Responsive Runtime Authorization for Agentic AI with Empirical Characterization of Policy Behavior and Parameter Sensitivity
- A Feasibility Condition for Confidence-Gated Tiered LLM Inference
- Thick Alignment: A Social Framework for Values and AI Systems
- Ghost in the machine: how AI is haunting the search for the supernatural
- AI agents need middle managers too
- Don't Pick a Winner: Knowledge Conflicts Need a Prior, Not a Rule
- Uneven Use, Similar Trust Generative AI in Denmark and Romania
- Whether Is Not When: Separating Occurrence from Timing in Polymarket Prices
- AOS-Bench: Can LLM Agents Manage Dynamic Workloads?
- ChatGPT’s expanding world of work
- When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
- What Does AI Success Look Like? A Mixed-Methods Analysis of Supranational AI Governance Instruments
- 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
- When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability
- “We were looking at it wrong”: The explosive claim that’s upended the AI world
- Anthropic turns to lawyers to help AI agents strike deals
- Bringing GPT-Rosalind to academic research labs
- Structural Dependence, Imitation or Herding? A Multi-Agent Simulation of Global Intermedia Agenda Setting
- From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring
- Do Vision CIL Methods Transfer to Protein? A Diagnostic Benchmark of Frozen Protein Encoders
- Amp puts AI agents to work beyond code with OpenAI models
- Becoming real
- Is this idea from AI or humans? A detector claims it can tell
- Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn
- Verification and Self-Improvement in Agentic AI: Foundations and Limits
- Maintaining Human Verification Capacity under Automation
- Shaping & Monitoring Models through their Internal Representations
- Scaffolding GenAI Use in Ethical Essay Writing: A Three-Year Experience Report in Introductory Computing
- 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
- ‘Control and sovereignty’ to shape UK approach to AI
- HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation
- Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
- How we detect frontier model misalignment
- Playing Bias: Epistemic Games for Interpreting Text-to-Image Models
- An updated Model Spec for more capable and beneficial AI
- Synthesizing symbolic models of human and machine reasoning at scale
- OpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery
- The EU AI Act Beyond Europe: A Practical Readiness Framework for Non-EU Organisations. Comparative Evidence from Four African Countries
- Anthropic gives outside AI watchdog “employee-like” access
- Global economy set for AI-driven surge, FT survey suggests
- Privacy Preference Controls for Agentic Systems
- Regulating for an AI Commons: Adopting, sustaining, and defending people-first innovation trajectories
- AI-Native Academia Should Build a Community Verification Layer
- Artificial Intelligence and Job Design: A Dynamic Framework
- Offline Apprenticeship Learning for Human-Centric Environments
- 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. 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 |