DOOM LEVEL
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Real-Time AI Service Economy: A Framework for Agentic Computing Across the Continuum
via ArXiv cs.AI [3] — Real-time AI services increasingly operate across the device-edge-cloud continuum, where autonomous AI agents generate latency-sensitive workloads, orchestrate multi-stage processing pipelines, and compete for shared resources under policy and governance…
Payorian cooperation is easy with Kripke frames
via LessWrong AI [3] — The context is MIRI's twist on Axelrod's Prisoner's Dilemma tournament. Axelrod's competitors were programs, facing each other in an iterated Prisoner's Dilemma. MIRI's tournament is a one-shot Prisoner's Dilemma, but the programs get to read their…
Can governments quickly and cheaply slow AI training?
via Alignment Forum [999] — I originally wrote this as a private doc for people working in the field - it's not super polished, or optimized for a broad audience.But I'm publishing anyway because inference-verification is a new and exciting area, and there few birds-eye-view…
Your Causal Variables Are Irreducibly Subjective
via LessWrong AI [7] — Mechanistic interpretability needs its own shoe leather era. Reproducing the labeling process will matter more than reproducing the Github. And who can blame us? Causal inference comes with an impressive toolkit: directed acyclic graphs, potential…
Mox is the largest AI Safety community space in San Francisco. We're fundraising!
via LessWrong AI [5] — Summary: Mox is fundraising to maintain and grow AIS projects, build a compelling membership, and foster other impactful and delightful work. We're looking to raise $450k for 2026, and you can donate on Manifund!OverviewWho we areMox is SF’s largest AI…
Thoughts on the Pause AI protest
via LessWrong AI [4] — On Saturday (Feb 28, 2026) I attended my first ever protest. It was jointly organized by PauseAI, Pull the Plug and a handful of other groups I forget. I have mixed feelings about it. To be clear about where I stand: I believe that AI labs are worryingly…
Anthropic Officially, Arbitrarily and Capriciously Designated a Supply Chain Risk
via Substack Zvi [999] — Make no mistake about what is happening.
The Elect
via LessWrong AI — I was different in Michael’s prison than I was outside, looking the way I did when we fell in love so long ago, in that time before we could change our forms. Stuck in some body that was not of my choosing? Does that seem strange to you? It was not like that…
Shaping the exploration of the motivation-space matters for AI safety
via LessWrong AI [5] — SummaryWe argue that shaping RL exploration, and especially the exploration of the motivation-space, is understudied in AI safety and could be influential in mitigating risks. Several recent discussions hint in this direction — the entangled generalization…
Towards automated data analysis: A guided framework for LLM-based risk estimation
via ArXiv cs.AI [2] — Large Language Models (LLMs) are increasingly integrated into critical decision-making pipelines, a trend that raises the demand for robust and automated data analysis. Current approaches to dataset risk analysis are limited to manual auditing methods
SkillNet: Create, Evaluate, and Connect AI Skills
via ArXiv cs.AI — Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Without a unified mechanism for skill consolidation, agents frequently ``r
AI Safety Has 12 Months Left
via LessWrong AI — The past decade of technology has been defined by many wondering what the upper bound of power and influence is for an individual company. The core concern about AI labs is that the upper bound is infinite.[1]This has led investors to direct all of their mindshare towards deploying into AI, the tech
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…
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