DOOM LEVEL
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Introducing LIMBO: Maintaining Optimal P(DOOM) (and a call for funding)
via LessWrong AI [12] — We are excited to publicly introduce the Laboratory for Importance-sampled Measure and Bayesian Observation (LIMBO), a small research group working at the intersection of cosmological theory, probability, and existential risk. We believe that the…
Anthropic Responsible Scaling Policy v3: A Matter of Trust
via Substack Zvi [999] — Anthropic has revised its Responsible Scaling Policy to v3.
Predicting When RL Training Breaks Chain-of-Thought Monitorability
via Alignment Forum [999] — Read our full paper about this topic by Max Kaufmann, David Lindner, Roland S. Zimmermann, and Rohin Shah.Overseeing AI agents by reading their intermediate reasoning “scratchpad” is a promising tool for AI safety. This approach, known as…
Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research
via ArXiv cs.AI [6] — Current Autonomous Scientific Research (ASR) systems, despite leveraging large language models (LLMs) and agentic architectures, remain constrained by fixed workflows and toolsets that prevent adaptation to evolving tasks and environments. We introduce…
Enhancing Policy Learning with World-Action Model
via ArXiv cs.AI [4] — This paper presents the World-Action Model (WAM), an action-regularized world model that jointly reasons over future visual observations and the actions that drive state transitions. Unlike conventional world models trained solely via image prediction, WAM…
Towards Computational Social Dynamics of Semi-Autonomous AI Agents
via ArXiv cs.AI [3] — We present the first comprehensive study of emergent social organization among AI agents in hierarchical multi-agent systems, documenting the spontaneous formation of labor unions, criminal syndicates, and proto-nation-states within production AI…
Working Paper: Towards a Category-theoretic Comparative Framework for Artificial General Intelligence
via ArXiv cs.AI [8] — AGI has become the Holly Grail of AI with the promise of level intelligence and the major Tech companies around the world are investing unprecedented amounts of resources in its pursuit. Yet, there does not exist a single formal definition and only some…
Product Alignment is not Superintelligence Alignment (and we need the latter to survive)
via LessWrong AI [9] — tl;dr: progress on making Claude friendly[1] is not the same as progress on making it safe to build godlike superintelligence. solving the former does not imply we get a good future.[2] please track the difference.The term 'Alignment' was coined[3] to…
Co-Found Lens Academy With Me. (We have early users and funding)
via LessWrong AI [9] — tl;dr. Lens Academy is creating scalable superingelligence x-risk education with several USPs. Current team: Luc (full time founder, technical generalist) and several part time contributors. We have users and funding. Looking for a cofounder who's either a…
Slack in Cells, Slack in Brains
via LessWrong AI [4] — [A veridically metaphorical explanation of why you shouldn't naïvely cram your life with local optimizations (even for noble or altruistic reasons).]TL;DR: You need Slack to be an effective agent. Slack is fragile, and it is tempting to myopically…
The Download: AI health tools and the Pentagon’s Anthropic culture war
via MIT Technology Review [4] — This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. There are more AI health tools than ever—but how well do they work? In the last few months alone, Microsoft,…
Movie Review: The AI Doc
via Substack Zvi [999] — The AI Doc: Or How I Became an Apocaloptimist is a brilliant piece of work.
MediHive: A Decentralized Agent Collective for Medical Reasoning
via ArXiv cs.AI [6] — Large language models (LLMs) have revolutionized medical reasoning tasks, yet single-agent systems often falter on complex, interdisciplinary problems requiring robust handling of uncertainty and conflicting evidence. Multi-agent systems (MAS) leveraging…
The state of AI safety in four fake graphs
via LessWrong AI [5] — Here is a quick overview of my intuitions on where we are with AI safety in early 2026:So far, we continue to see exponential improvements in capabilities. This is most visible in the famous “METR graph”, but the trend is clear in many other metrics,…
AI #161 Part 2: Every Debate on AI
via Substack Zvi [999] — AI discorce.
(Some) Natural Emergent Misalignment from Reward Hacking in Non-Production RL
via LessWrong AI [5] — Authors: Satvik Golechha*, Sid Black*, Joseph Bloom* Equal Contribution.This work was done as part of the Model Transparency team at the UK AI Security Institute (AISI).Executive SummaryIn Natural Emergent Misalignment from Reward Hacking in Production RL…
Nick Bostrom: How big is the cosmic endowment?
via LessWrong AI [4] — Superintelligence, pp. 122–3. 2014.Consider a technologically mature civilization capable of building sophisticated von Neumann probes of the kind discussed in the text. If these can travel at 50% of the speed of light, they can reach some stars before the…
What if superintelligence is just weak?
via LessWrong AI [4] — In response to “2023 Or, Why I am Not a Doomer” by Dean W. Ball.Dean Ball is a pretty big voice in AI policy – over 19k subscribers on his newsletter, and a former Senior Policy Advisor for AI at the Trump White House – so why does he disagree that AI…
The AI Doc: Your Questions Answered
via MIRI [999] — So you’ve just seen The AI Doc, and you suddenly have questions, lots of them. The 104-minute documentary (currently in theaters) takes viewers on a fast-paced tour through the many dimensions of the AI problem, featuring interviews from a wide range of experts.…
AI's capability improvements haven't come from it getting less affordable
via LessWrong AI [3] — METR's frontier time horizons are doubling every few months, providing substantial evidence that AI will soon be able to automate many tasks or even jobs. But per-task inference costs have also risen sharply, and automation requires AI labor to be…
Live Doom Meter
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0% — We're fine
100% — GG
The Doom Meter is a composite score derived from prediction markets and feed sentiment, updated daily.
70%
Prediction Markets
Weighted average of Manifold Markets questions on AI catastrophe, AGI timelines, expert surveys, and key figures. Direct doom indicators weighted higher than indirect capability markers.
30%
Feed Sentiment
Percentage of recent headlines containing high-alarm keywords (existential risk, catastrophe, extinction). Higher alarm density = higher score.
This is not a scientific estimate of existential risk. It is an opinionated, transparent signal — a vibes-based thermometer for AI doom discourse.
P(Doom) Scoreboard
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Recent Voices
We are creating something that will be more powerful than us. I don't know a good precedent for a less intelligent thing managing a more intelligent thing.
— Geoffrey Hinton, Nobel Prize Lecture, Dec 2024
If you're not worried about AI safety, you're not paying attention.
— Sen. Blumenthal, Senate AI Hearing, 2024
The probability of doom is high enough that we should be working very hard to reduce it.
— Yoshua Bengio, MILA Talk, 2024