DOOM LEVEL
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Personality Self-Replicators
via LessWrong AI [5] — One-sentence summaryI describe the risk of personality self-replicators, the threat of OpenClaw-like agents managing spreading in hard-to-control ways. SummaryLLM agents like OpenClaw are defined by a small set of text files and are run by an open source framework which leverages LLMs
AI #158: The Department of War
via Substack Zvi — This was the worst week I have had in quite a while, maybe ever.
Introducing GPT-5.4
via OpenAI Blog [5] — Introducing GPT-5.4, OpenAI’s most most capable and efficient frontier model for professional work, with state-of-the-art coding, computer use, tool search, and 1M-token context.
Reasoning models struggle to control their chains of thought, and that’s good
via OpenAI Blog [7] — OpenAI introduces CoT-Control and finds reasoning models struggle to control their chains of thought, reinforcing monitorability as an AI safety safeguard.
Gemini 3.1 Pro Aces Benchmarks, I Suppose
via Substack Zvi — I’ve been trying to find a slot for this one for a while.
Mass Surveillance w/ LLMs is the Default Outcome. Contracts Won't Change That.
via LessWrong AI [3] — What's the best case scenario regarding OpenAI's contract w/ the Department of War (DoW)?We have access to the full contractIt's airtightOAI's engineers are on top of things in case the DoW breaks the contractThere's actual teeth for violationsBut even then, the DoW can simply switch vendors. Use Ge
I Had Claude Read Every AI Safety Paper Since 2020, Here's the DB
via LessWrong AI — Click here if you just want to see the Database I made of all[1] AI safety papers written since 2020 and not read the methodology. To some extent the core idea here is to encode as much info from these papers into something small enough that an AI with a specific problem in mind can take in all
An Alignment Journal: Coming Soon
via LessWrong AI [9] — tl;dr We’re incubating an academic journal for AI alignment: rapid peer-review of foundational Alignment research that the current publication ecosystem underserves. Key bets: paid attributed review, reviewer-written synthesis abstracts, and targeted automation. Contact us if…
A Tale of Three Contracts
via Substack Zvi [2] — The attempt on Friday by Secretary of War Pete Hegsted to label Anthropic as a supply chain risk and commit corporate murder had a variety of motivations.
Anthropic upgrades Claude’s memory to attract AI switchers
via The Verge AI [2] — Anthropic is making it easier to switch to its Claude AI from other chatbots with an update that brings Claude's memory feature to users on the free plan, along with a new prompt and dedicated tool for importing data from other chatbots. These upgrades could allow users who have been using rivals li
War Claude
via LessWrong AI [2] — What a weekend. Two new wars in Asia don't qualify as top news. My first reaction to Hegseth's conflict with Anthropic was along the lines of: I expected an attempt at quasi-nationalization of AI, but not this soon. And I expected it to look like it was managed by national security professionals. He
OpenAI’s “compromise” with the Pentagon is what Anthropic feared
via MIT Technology Review [4] — On February 28, OpenAI announced it had reached a deal that will allow the US military to use its technologies in classified settings. CEO Sam Altman said the negotiations, which the company began pursuing only after the Pentagon’s public reprimand of Anthropic, were “definitely rushed.” In its anno
How OpenAI caved to the Pentagon on AI surveillance
via The Verge AI [4] — On Friday evening, amidst fallout from a standoff between the Department of Defense and Anthropic, OpenAI CEO Sam Altman announced that his own company had successfully negotiated new terms with the Pentagon. The US government had just moved to blacklist Anthropic for standing firm on two red lines
Secretary of War Tweets That Anthropic is Now a Supply Chain Risk
via Substack Zvi [2] — This is the long version of what happened so far.
I checked out one of the biggest anti-AI protests ever
via MIT Technology Review [4] — Pull the plug! Pull the plug! Stop the slop! Stop the slop! For a few hours this Saturday, February 28, I watched as a couple hundred anti-AI protesters marched through London’s King’s Cross tech hub, home to the UK headquarters of OpenAI, Meta and Google DeepMind, chanting slogans and waving signs.
How to Design Environments for Understanding Model Motives
via Alignment Forum [5] — Authors: Gerson Kroiz*, Aditya Singh*, Senthooran Rajamanoharan, Neel NandaGerson and Aditya are co-first authors. This work was conducted during MATS 9.0 and was advised by Senthooran Rajamanoharan and Neel Nanda.TL;DRUnderstanding why a model took an action is a key question in AI Safety. It is a
PseudoAct: Leveraging Pseudocode Synthesis for Flexible Planning and Action Control in Large Language Model Agents
via ArXiv cs.AI [6] — Large language model (LLM) agents typically rely on reactive decision-making paradigms such as ReAct, selecting actions conditioned on growing execution histories. While effective for short tasks, these approaches often lead to redundant tool usage, un
AI Must Embrace Specialization via Superhuman Adaptable Intelligence
via ArXiv cs.AI [8] — Everyone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don't seem to agree on its exact definition. One common definition of AGI is an AI that can do
MMKG-RDS: Reasoning Data Synthesis via Deep Mining of Multimodal Knowledge Graphs
via ArXiv cs.AI [3] — Synthesizing high-quality training data is crucial for enhancing domain models' reasoning abilities. Existing methods face limitations in long-tail knowledge coverage, effectiveness verification, and interpretability. Knowledge-graph-based approaches s
I'm Bearish On Personas For ASI Safety
via LessWrong AI [5] — TL;DRYour base LLM has no examples of superintelligent AI in its training data. When you RL it into superintelligence, it will have to extrapolate to how a superintelligent Claude would behave. The LLM’s extrapolation may not converge optimizing for what humanity would, on…
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