AI existential risk probabilities are too unreliable to inform policy
This commentary argues that numerical probability estimates for AI existential risk are epistemically unreliable and should not be used as a basis for policy decisions. The piece critiques the practice of assigning precise figures to speculative scenarios, characterizing it as pseudo-quantification that lends false credibility to uncertain claims. The author contends that such estimates are laundered speculation rather than grounded forecasting.
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Scientists should use AI as a tool, not an oracle
This commentary critiques the feedback loop between AI hype and scientific research, arguing that scientists who treat AI systems as oracles rather than tools produce flawed research that in turn amplifies further hype. The piece examines how uncritical adoption of AI in scientific workflows can compromise research integrity. It calls for a more epistemically disciplined approach to AI use in science.
Anthropic publishes policy brief calling for targeted AI regulation within 18 months
Anthropic published a policy position paper arguing that governments have an 18-month window to enact narrowly-targeted AI regulation before risks in cyber and CBRN domains become acute. The post cites rapid capability gains—SWE-bench scores rising from 1.96% to 49% in a year, GPQA scores approaching human expert level—as evidence that frontier models are approaching meaningful misuse thresholds. Anthropic also reviews its Responsible Scaling Policy as a model for adaptive, proportionate risk governance and calls for similar frameworks to be adopted industry-wide and codified in law.
Do AI Risks Require Extraordinary Government Intervention?
A commentary piece from the AI Snake Oil newsletter (published via normaltech.ai) examines whether AI risks justify extraordinary government intervention. The piece appears to argue against shortcuts in AI governance, emphasizing the importance of rigorous policy work. The article engages with ongoing debates about the appropriate scope and urgency of regulatory responses to AI.
AI Scaling Myths
A commentary piece from normaltech.ai argues that AI scaling will eventually hit limits, framing the debate as a question of timing rather than whether limits exist. The piece appears to challenge prevailing optimism around continued scaling returns. Given the minimal body text, the depth of argument is unclear, but the topic directly engages the scaling laws debate central to frontier AI development.
Giving your AI a Job Interview
This commentary piece argues that as AI-generated advice becomes more consequential, users need systematic methods to evaluate AI reliability and quality—analogous to a job interview process. The author proposes frameworks for assessing AI outputs before trusting them for important decisions. The piece addresses the practical challenge of calibrating trust in AI systems across different use cases.
New Paper: Towards a Science of AI Agent Reliability
A new paper proposes a framework for quantifying the gap between AI agent capability and reliability, aiming to establish a more rigorous science of agent dependability. The work addresses the observation that agents may demonstrate high capability on benchmarks while failing unpredictably in deployment. The piece is published via the normaltech.ai newsletter, associated with the AI Snake Oil research commentary tradition.
Cyber Lack of Security and AI Governance
Zvi Mowshowitz's commentary addresses the intersection of AI capabilities and cybersecurity, framing recent developments around GPT-5.5 and a 'Mythos Moment' as catalysts for both internet security patching efforts and emerging AI regulatory frameworks. The piece situates cybersecurity as the underreported background story of current AI progress. It appears to analyze governance and safety implications of frontier model releases in the context of cyber vulnerabilities.
Andrew Ng commentary: Trump executive order on AI strikes reasonable balance but overregulation risk remains
Andrew Ng analyzes a new White House executive order on AI, characterizing it as a reasonable compromise between promoting AI development and addressing cybersecurity concerns. The order was partly motivated by Anthropic's Mythos system, which demonstrated automated vulnerability detection in code. Ng credits advisors David Sachs and Sriram Krishnan for keeping the order from being overly burdensome, while warning that legitimate cybersecurity risks now give lobbyists a stronger tool to push for excessive regulation. He argues that governments lacking technical judgment should err toward restraint rather than overregulation.