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 · agi-timelines · last 365 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
-4
Mixed or uncertain
34 items · range -18 to +10
-100
-50
0
+50
+100
-4
Adverse readingFavourable reading
34 items read as mixed or uncertain
- Jensen Huang says battle over AI innovation and safety is ‘false choice’
- Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement
- OpenAI's apparent maths breakthrough raises profound questions
- Stochastic Dynamics of Generative AI: From Autoregressive Models to Interacting LLM Networks and Neural SDEs
- OpenAI says it cracked 90-year-old maths problem in 88 hours
- The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
- ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
- AI’s transformation of mathematics is a turning point for science
- Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training
- ‘Immature playground boasting’: Mathematicians uneasy at OpenAI’s latest scalp
- 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
- The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents
- Genie 4: A new generation of world simulation
- AI researchers stranded Claude on a virtual desert island. It started to “play.”
- Google announces Gemini 4 Argon AI model, but you can't use it yet
- Reimagining research papers as interactive and reliable AI agents
- “We were looking at it wrong”: The explosive claim that’s upended the AI world
- Becoming real
- A Bayesian Mirror Architecture for Emergent Consciousness: Circular Hierarchies, Self-Manifolds, and Hybrid Event-Self Binding
- Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction
- Bringing AI to Autonomous Systems -- From Cognition to Collective Intelligence
- Introducing MentalHealthBench
- China bogeyman looms large over American firms’ AI doomsday scenario
- Is There Anything Google’s Fruit Fly Brain Can’t Do?
- OpenAI Says It Has Cracked One of Math’s ‘Millennium Problems’
- Anthropic warns of 'catastrophic risk' to humankind ahead of blockbuster share listing
- Endogenous Exploration in Reinforcement Learning with Intrinsic Curiosity
- Algorithmic Information Dynamics of Learning: A Certified, Differentiable Complexity Controller for Grokking
- How we detect frontier model misalignment
- Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?
- AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome
- Early Data Indicates an A.I.-Generated Drug Could Slow Aging
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 365 days under agi-timelines. 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 |