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.
41 items read as mixed or uncertain
- OpenAI reveals six more safety issues and unveils plan to disclose incidents
- Artificial intelligence and biosecurity: capabilities, threat pathways, and defense-in-depth governance
- Sam Altman’s remarks at the United Nations Security Council
- Following the Preference, Missing the Optimum: Compliance Without Optimization in AI Housing Recommendation
- ClawLoop: Self-Improving Agents via Harness-Native Training Loop
- OpenAI's apparent maths breakthrough raises profound questions
- '500 new vulnerabilities' found in government, says AI minister
- OpenAI Engineer, Who Is Charged With Planting Bug, Advised Saudis
- Stability and Diversity of Networked Self-Consuming Generative Ecosystems
- OpenAI announces ‘dots’ agent after scrapping launch of new AI model over safety concerns
- ‘We must slow the pace’: CEO of Anthropic calls for an AI slowdown
- OpenAI Says It Will Not Release Newest Astra A.I. Model Over Safety Concerns
- OpenAI scraps rollout of new model over safety concerns
- The Ineliminable Anchor: Human Judgment as a Structural Requirement of Autonomous Artificial Intelligence Systems
- California Governor Gavin Newsom Issues A.I. Safety Executive Order
- OpenAI axes next model citing safety issues
- AI researchers stranded Claude on a virtual desert island. It started to “play.”
- Plan for AI-run companies challenged by US lawmakers
- Anticipatory Human Oversight of Agentic AI: A Philosophical Account
- ESC (DC) 2
- Trust Responsive Runtime Authorization for Agentic AI with Empirical Characterization of Policy Behavior and Parameter Sensitivity
- What Does AI Success Look Like? A Mixed-Methods Analysis of Supranational AI Governance Instruments
- 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
- Building a safer path to autonomous industrial AI
- OpenAI delays IPO over AI safety concerns
- Towards safety cases for frontier AI training
- Nick Clegg plays down fears ‘godlike’ AI could exterminate humanity
- I Gave My Life Over to Meta’s A.I. Agent and Was Blown Away
- AI and Human Approaches to Mathematical Problem Solving
- Our framework for reporting model misalignment
- Make AI traceable before it shapes global climate assessments
- Embedded Assessments for Frontier AI
- Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer
- Towards a Platform for Mastering Personal Sovereignty
- How we detect frontier model misalignment
- Anthropic gives outside AI watchdog “employee-like” access
- Bounded Autonomy and Verifiable Safety for Agentic AI Enabled Automation
- Objective-Conditioned Active Preference Learning for Multi-Objective Alignment
Across the whole window
The two tables below describe every item in the last 90 days under loss-of-control. 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 |