1095 summarised stories about AI Governance, each linking back to the original source. Browse all topics →
OpenAI 2 months ago 45 ● 75 sources
OpenAI published details about its safety, security, transparency, and provenance practices to support compliance with European AI governance requirements. The company's documentation aligns with the EU AI Act framework as it continues to develop. OpenAI aims to demonstrate how its operational practices can meet regulatory standards across Europe.
Zvi (Don't Worry About the Vase) 2 months ago 38 ● 50 sources
US lawmakers introduced two bills to regulate AI development: the FRONTIER Act, which establishes federal oversight of frontier AI models through Commerce Department authority with testing and incident reporting requirements, and the AI Kill Switch Act, which requires large AI companies to be able to shut down model inference during crises. The Kill Switch Act sets maximum fines of $20 million per day for non-compliance and applies only to covered entities above certain revenue thresholds, while explicitly exempting open-weight models from shutdown requirements. These legislative efforts represent attempts to balance catastrophic risk management with open-source development, though enforcement mechanisms and definitional thresholds remain contested among policymakers, industry, and advocates.
Schneier on Security 2 months ago 25 ● 2 sources
A Harvard Kennedy School professor proposes a framework for deciding when to use AI for a task by distinguishing between "work" (where the outcome matters but the process doesn't) and "gym" (where the process and skill-building are essential). The author argues that using AI for gym tasks like student writing assignments is counterproductive because the struggle of writing, drafting, and editing develops critical thinking skills that atrophy without practice. This framework has implications for creative professionals and society's future relationship with AI-generated content and work.
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19 October 2026
Amazon’s biggest push against Nvidia this year landed today in the form of TorchNeuron, an AWS development tool designed to let PyTorch-based AI apps run on its Trainium chips with a one-line configuration change instead of a code rewrite. AWS says roughly 85% of AI apps use PyTorch, and that number matters because it turns the usual “porting tax” into a packaging decision: keep writing in PyTorch, switch the backend, move on. The company also plans upstream PyTorch changes to make the framework more hardware-agnostic, aiming to loosen the gravity that keeps developers clustered around specific accelerator ecosystems—an underappreciated lever in enterprise spending, where platform lock-in often lasts longer than model lifecycles.
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