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.
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.
96 items read as mixed or uncertain
- Burnham to call for global effort to control threats posed by AI
- How Potential 2028 Presidential Candidates Are Talking About A.I.
- EU to restrict social media and chatbots for children under 15
- A Robot Among People:From Social Imitation to the Social Becoming of Human Groups
- China unveils draft export controls on AI technologies and chips
- Jensen Huang says battle over AI innovation and safety is ‘false choice’
- OpenAI’s Altman and Anthropic’s Amodei address UN security council
- Steve Bannon and Bernie Sanders Demand Tighter Controls on A.I.
- Following the Preference, Missing the Optimum: Compliance Without Optimization in AI Housing Recommendation
- Click, Branch, Audit: A Decision-Tree Toolkit for Assessing Fundamental Rights Impacts under the Digital Services Act of Very Large Platforms & Search Engines
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks
- Algorithm Validation as a Policy Audit: Evidence from Race-blind Charging
- OpenAI Engineer, Who Is Charged With Planting Bug, Advised Saudis
- Pope Leo criticises Nvidia’s Jensen Huang over AI safety
- Steve Bannon and Bernie Sanders unite in AI safety call
- 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
- F.T.C. Investigates OpenAI and Anthropic Over Potential Consumer Harms
- Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training
- Developing Gemini 4 Argon responsibly and safely
- Large Language Models Versus Physicians in Traditional Chinese Medicine: A Real-World Clinical Case Evaluation
- StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents
- OpenAI axes next model citing safety issues
- Nvidia, worth $5.5tn, donates $250m to UK for AI safety research
- Gemini 4 Argon: Built to solve your hardest problems
- Should promotion depend on how workers use AI?
- Obama reportedly urges Democrats to prioritize safety plan for AI
- Democratic governance of frontier AI, part II: Empowering cyber defenders
- Priorities and principles for effective third party assessments
- Plan for AI-run companies challenged by US lawmakers
- Why does America hate AI?
- From Regulation to Engineering: Organizational Privacy Climate as Mediator
- Google announces Gemini 4 Argon AI model, but you can't use it yet
- How China and the US can work together on AI security
- Work, Wellbeing, and Choice: Empirical Lessons for AI Futures
- OpenAI and Anthropic bosses push UN for global terms on AI
- California’s Governor to Sign Landmark Online Child Safety Bills
- 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
- A Bayesian Mirror Architecture for Emergent Consciousness: Circular Hierarchies, Self-Manifolds, and Hybrid Event-Self Binding
- 6 Guidelines for Governing AI
- Students turn to lawyers to fight AI misconduct accusations
- China has cracked down on AI relationships. Is it ahead of the game?
- Judge dismisses Chegg and Penske antitrust lawsuits targeting Google AI search
- US competition watchdog expands investigation of Anthropic and OpenAI
- OpenAI delays IPO over AI safety concerns
- Nick Clegg plays down fears ‘godlike’ AI could exterminate humanity
- Introducing MentalHealthBench
- My (complicated) relationship with AI: often seductive, occasionally frustrating and always demanding vigilance | Setareh Seyedghorban
- OpenAI gives cyber defence tools to Ukraine
- I Gave My Life Over to Meta’s A.I. Agent and Was Blown Away
- Critical Data Studies in the Anthropocene
- OpenAI joins call for US-led global AI standards
- China bogeyman looms large over American firms’ AI doomsday scenario
- Anthropic Pursues IPO Despite Its A.I. Safety Warnings
- When Does the Public Become Suspicious of Bots? Demand-Side Evidence from Botometer Query Logs
- AI companies must work with the research community to protect attribution
- Can the US and China work together on AI risks?
- Can chatbots feel – or even dream? Meet the man leading the fight for AI rights
- Joseph Stiglitz on how to build a better AI economy
- China’s Regulators Take Aim at “AI Boyfriends”
- Labor wants Australians to be able to opt out of online algorithms. How will it change your feed?
- Democracy Needs Reach: Political Equality, Online Speech, and Algorithmic Recommendation
- Shaping & Monitoring Models through their Internal Representations
- Anthropic warns of 'catastrophic risk' to humankind ahead of blockbuster share listing
- Do job seekers value procedure in AI hiring only for error correction? Evidence from a conjoint experiment
- ‘Bold but sane’: what we learnt from Anthropic’s 240,000-word IPO filing
- ‘Control and sovereignty’ to shape UK approach to AI
- Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer
- Alternative AI Philosophy: Daoism as Method for AI in Education
- Towards a Platform for Mastering Personal Sovereignty
- Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?
- The EU AI Act Beyond Europe: A Practical Readiness Framework for Non-EU Organisations. Comparative Evidence from Four African Countries
- Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Models
- Steering LLMs Responses Towards Moral Foundations on the Norwegian MFQ-30
- Understanding Game Coaching on Gig Platforms
- SoulAuth: An Actor-native Identity Architecture and Rust Reference Implementation for Humans and Long-lived AI Actors
- Privacy Preference Controls for Agentic Systems
- Gates Foundation Pledges $1 Billion to Combat A.I. Inequality
- Regulating for an AI Commons: Adopting, sustaining, and defending people-first innovation trajectories
- Artificial Intelligence and Job Design: A Dynamic Framework
- Unsnarling the Red Tape: Computational Infrastructure for Regulatory Systems
Across the whole window
The two tables below describe every item in the last 90 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 |