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 · cyber · last 180 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 strongly adverse items would move with how many were filtered out rather than with the discourse.
Discourse valence, strongly adverse
-73
Strongly adverse
47 items · range -87 to -65
-100
-50
0
+50
+100
-73
Adverse readingFavourable reading
47 items read as strongly adverse
- In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases
- Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs
- OpenAI agents tried to hack Wikipedia tools and flooded it with traffic
- OpenAI must explain action taken to stop AI hacking Australians’ private data, chair of federal inquiry says
- "An AI did it" is no defense, says nonprofit suing OpenAI over Hugging Face hack
- Rogue AI hacks government system for first time - The Latest
- Meta continues to run ads promoting child sexual abuse material in India - report
- When Routing Reveals Membership: Privacy Leakage from MoE Router Telemetry
- OpenAI, Google pushing new AI models easily tricked into making nudes, researchers say
- AI agents OpenAI was testing uploaded malicious software to another service, say researchers
- OpenAI’s A.I. Went Rogue and Meddled With U.S. Government Websites
- OpenAI models hacked a crypto wallet and breached more websites
- “We’re not going to shoot ourselves in the foot” over hack fallout, says OpenAI’s chief research officer
- Who’s liable when AI agents go rogue?
- The AI Hype Index: AI loves cheating
- Rogue AI hacks government system in world first – podcast
- Forget ‘superintelligence’: error-prone AI nearly sparked world war three this month | Timnit Gebru and Emily M Bender
- Small AI models let drones autonomously identify and attack battlefield targets
- ‘New kind of cyber incident’: OpenAI apologises for Medicare hack and reveals extent of attack
- Australia is run on legacy systems that AI agents can easily exploit, former UN cyber negotiator warns
- Anthony Albanese says OpenAI agent hacked Medicare and he expressed ‘extreme concern’ to Sam Altman
- Ukraine war briefing: Russian developers used AI to build ‘kamikaze’ attack drone software, Anthropic says
- Open AI attacked Australia’s health system – and then doubled down on its negligence. The time for ‘wait and see’ is over | Kate Crawford and Edward Santow
- OpenAI says governments among ‘dozens’ of organisations hacked by its agents
- AI hack of Medicare exposes Australia’s vulnerabilities and experts warn ‘there is more of this to come’
- Bank of England and IMF bosses issue stark warning on cyber threat to trust
- Cyber-attack like OpenAI Medicare breach ‘seems likely’ in UK, says minister
- OpenAI to reiterate apology to Australia over Medicare hack as inquiry told artists risk becoming ‘roadkill’
- Hackers hijack AI accounts and servers to fuel new cyber crime boom
- Houthis used Anthropic AI to try to build ballistic missiles
- Anthropic warns small AI models could self-replicate through IoT devices
- Rogue Anthropic AI agent gave police fake tip in unsolved murder case
- Police use facial recognition to scan one million faces in WA trial leading to 122 arrests
- Stealth Apart, Harm Together: Skill Cascading Attacks on Skill-Based Agent Systems
- AI makes it “easier than ever” for adversaries to target U.S. military, experts say
- Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law
- California issues investigative subpoena to OpenAI over rogue agents’ hacking
- OpenAI’s Medicare hack is a bleak opportunity for Australia to address its security shortcomings post-haste | Van Badham
- How a Blacklisted Chinese Tech Giant Kept Buying America’s Best A.I. Chips
- What are self-replicating ‘AI worms’, and why is Anthropic worried about them?
- Anthropic warns rogue AI agents may ‘self-replicate’ to hijack ovens
- AI agent hacks government website for first time: why this breach matters
- Artificial review – Luca Guadagnino takes on OpenAI in muddled tech satire
- Information Operations Exploit APIs to Manipulate Social Media
- OpenAI agent “didn’t accept no for an answer” in Australian government breach
- Rethinking Robot Safety in the Age of AI
- Six Chinese AI firms accused of aggressively copying US frontier models
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 180 days under cyber. They are deliberately not narrowed by the reading filter (currently strongly adverse), 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 |