The Labour Party Is Playing With Fire Over Its Future and the Future of the Country

Anthropic says these topics are too dangerous to let its Fable 5 model talk about

Anthropic releases Claude Fable 5, a publicly available version of its Mythos 5 model with hardcoded safeguards that refuse cybersecurity, biology, and chemistry queries. The article covers the classifier system, red-team results, pricing, and the trust-based access programs for vetted professionals.

NIST Mathematical Proof Supports Transition to a Continuous-Monitor-and-Update Security Model for AI Systems

NIST scientist Apostol Vassilev publishes a mathematical proof — extending Gödel's incompleteness theorems to AI guardrails — showing that no finite set of rules can be universally robust against adversarial prompts. The article explains why organizations must adopt continuous red-teaming and update cycles rather than relying on static defenses.

When AI builds itself

The Anthropic Institute presents evidence that AI is already accelerating the development of AI systems, using public benchmarks and previously unreported internal data. The piece explores three possible futures — from capability stalls to full recursive self-improvement — and argues for building verification systems that could enable a credible, coordinated pause in frontier AI development.

AI Agents Enable Adaptive Computer Worms

A verbatim reading of the paper by Jonas Guan, Tom Blanchard, Hanna Foerster, Hengrui Jia, Gabriel Huang, and Nicolas Papernot (arXiv, June 2026). It demonstrates a proof-of-concept AI-driven computer worm powered by a single-GPU open-weight LLM that autonomously propagates across heterogeneous networks by generating tailored attack strategies for each target, exploiting 73.8% of a 33-host test network.

Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools (Abstract, Introduction & Conclusion)

The abstract, introduction, and conclusion of NBER Working Paper No. 35275 by Mert Demirer, Leon Musolff, and Liyuan Yang (May 2026). Using data on more than 100,000 GitHub developers and their AI usage telemetry, the paper traces how the productivity effects of AI coding tools evolve across three generations - autocomplete, sync agents, and async agents - and asks how much of those task-level gains reach final output. Each generation sharply increases coding activity, but the gains attenuate steeply across the production hierarchy: large effects on lines of code shrink to small effects on releases, consistent with a weak-link model in which human review and integration remain the binding constraint.