An arXiv paper argues that while most organizations have adopted AI, their governance infrastructure lags behind, creating an "attestation deficit" where compliance claims cannot be verified. The author outlines an adaptive intelligence architecture intended to enforce AI policy continuously in real time rather than through periodic review.
A new arXiv paper contends that research on AI danger has concentrated on capability risks—systems growing too powerful, autonomous, or misaligned—while largely ignoring organizational factors. It applies the concept of normalization of deviance, where risky practices gradually become accepted as routine, to how AI labs operate. The authors argue that studying institutional culture and decision-making is necessary to understand how safety failures emerge.
In a Guardian opinion piece, Stuart Russell contends that safety obligations for AI developers should be treated as firm requirements tied to measurable outcomes rather than as schedules that can be stretched. The column follows the departure of a safety researcher from Anthropic, which capped a turbulent week of debate over how labs handle safety concerns.
Microsoft released a draft code of conduct that would govern how it trains new artificial intelligence models, an early step toward placing limits on its own systems. The company's AI chief said the technology must remain subordinate and serve people rather than the reverse. The move lands amid an ongoing public debate over how AI safety should be handled.
Major AI developers are arguing for rules that critics describe as designed to entrench their own market position. The discussion frames these lobbying efforts as an attempt at regulatory capture, where incumbents help shape the rules that govern them. Few concrete proposals are detailed in the report itself, which is a single discussion thread.