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 adverse items would move with how many were filtered out rather than with the discourse.
33 items read as adverse
- Mirror publisher to cut 220 editorial jobs as readers turn to AI summaries
- Students fear AI is about to kill their future
- Rightwing ‘great replacement’ fears mask tech's real great replacement | Jason Stanley
- The Capability Transfer: Cohort Reversal, Codified Expertise, and the Adaptability Tax of AI Transformation
- Digital Labor Platforms as Infrastructure of Extractivism
- When AI Enters the Workplace, Who Faces Greater Risks? A Gendered Analysis
- Tesla workers balk at training Optimus humanoid robots as replacements
- Navigating the Changing Landscape of Online Knowledge Consumption and Production in the Age of Generative AI: Evidence from Stack Overflow
- Australians are yet to see the benefits of AI and datacentres – and US midterms show they could be political kryptonite
- AI evaluators favor job applications written by certain AI models, independent of candidate quality
- Big AI is trying to own the pathway to work. Universities shouldn’t play along | Ella Hafermalz
- I watched a humanoid robot clean my apartment. It felt like a taste of the future.
- The hidden cost of flexible work in academia: women do more parenting
- Trump declares robot imports a national security threat
- What is it like to work when your co-workers are AI agents?
- China’s villages pay the price for Beijing’s AI ambitions
- Tell us: have you been monitored or assessed by AI at work?
- Student Use of LLMs and the Limits of AI-Generated Question Difficulty in Data Science Courses
- AI is generating a new wave of US populism. It could be a tipping point | Robert Reich
- BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals
- Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria
- AI-Driven Feedback Systems, Digital Labour, and Silent Quitting: Transforming African Workplaces
- Self-driving cars should be taxed to offset job losses, thinktank urges
- The AI industry is booming. Women are getting left behind
- Welfare-Opaque Income: Taxation under AI-Agent Delegation
- It’s time to block reckless AI IPOs and tax billionaires | Naomi Klein and Ilana Glazer
- China’s block on SoftBank deal fires the starting gun on the robot wars
- The future of the firm
- AI boom obscuring global trade disruptions, warns WTO chief
- Is a job guarantee the solution to mass AI unemployment?
- Is Universal Basic Income the Answer to A.I.-Fueled Job Losses?
- Detecting Deceptive Recruitment: A Signal-theoretic Machine Learning Framework for Early Identification of Labour Exploitation
- California’s new laws target workers’ biggest fear of AI taking their jobs
Across the whole window
The two tables below describe every item in the last 365 days under employment. They are deliberately not narrowed by the reading filter (currently 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 |