
Anthropic has released version 3.0 of its Responsible Scaling Policy (RSP), a significant update to its internal safety framework governing the development of frontier AI models. The release comes amid heightened scrutiny from employees and external observers regarding the company's approach to AI risk, particularly following revelations about an internal staff survey expressing concerns over model capabilities. Dario Amodei has also published essays rationalizing the pace of frontier development, arguing for a measured approach despite pressure to accelerate. This policy update aims to formalize safety checks before deploying models with capabilities that may be difficult to fully verify or control.
Read originalTopicAnthropic Responsible Scaling Policy
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
WIRED AI · July 16, 2026 · Related
Anthropic · July 27, 2026 · Related
WIRED AI · August 27, 2026 · Background
AI Explained · August 27, 2026 · Related
TechCrunch AI · September 18, 2026 · Background
AI Explained · September 24, 2026 · Same story
TechCrunch AI · October 18, 2026 · Related
The Verge AI · October 27, 2026 · Related
© AI ExplainedAI researcher Albert Gu advocates for training models directly from raw data, challenging current tokenization paradigms.
© AI ExplainedReports indicate OpenAI is scrambling to fix safety issues in its training environment before shipping its next flagship model.
© TechCrunch AIApple is quietly pivoting to monetize the AI wave through its App Store, signaling that third-party AI agents could become a new revenue stream. This shift addresses regulatory headwinds and an 18% drop in U.S. commission revenue by potentially taking a cut of AI agent transactions. While details remain vague, the move suggests Apple sees AI as a key driver for upgrades and subscription growth. It marks a strategic adaptation to maintain services dominance amid global legal pressures.
© TechCrunch AITrajectory’s $45 million Series A signals a pivot toward making post-training accessible to non-technical experts. By automating continual learning, the startup lets domain specialists like lawyers and doctors refine open-source models through feedback loops without hiring expensive research teams. This approach bridges the gap between foundational models and specialized industry needs, potentially lowering the barrier for enterprise AI adoption. With backing from Sequoia and Nvidia, Trajectory is positioning itself as a critical infrastructure layer for vertical-specific AI applications.
© TechCrunch AIAnthropic’s revised S-1 filing drops a sobering admission: the company believes it is imminently close to Recursive Self-Improvement (RSI). This isn't just theoretical speculation; they explicitly state that AI-driven R&D may have already caused capability leaps they didn't initially recognize. While no current model has crossed the threshold, the warning signals a critical inflection point where automated progress might outpace human safety validation. It forces the industry to confront whether human-in-the-loop oversight can survive the acceleration it helped create.