The article argues that proposed app store regulations, such as the App Store Freedom Act (ASFA), are misguided because they impose a counterproductive 'one-size-fits-all' model on digital markets. Key criticisms include how these mandates undermine genuine competition by forcing third-party payment systems and homogenizing business models, thereby harming stores reliant on third-party apps. Furthermore, the bill’s prohibition on price matching removes a critical competitive mechanism used in both physical and digital retail. Ultimately, adopting such sweeping regulations risks reducing consumer choice, stifling diverse business practices, and significantly increasing cybersecurity vulnerabilities by compromising secure operating environments.
Breaking the deadlock on AI governance
English Summary
Chatham House argues that progress on international AI governance is currently stalled due to geopolitical tensions, institutional weaknesses, and public-private imbalances, suggesting that a significant AI-related crisis may be the only catalyst for rapid, binding global cooperation. The paper draws lessons from past crises like the 2008 financial crisis and the WannaCry attack, highlighting the importance of pre-existing institutions and technical expertise for effective crisis-driven governance. Consequently, policymakers should prioritize investing in foundational AI governance infrastructure now to be prepared for a potential crisis and facilitate a robust response.
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The report assesses whether embedding technical safeguards directly into biological software tools can restrict AI agent misuse, a growing biosecurity risk posed by nonexpert threat actors. Testing revealed that tool-level safeguards are not consistently effective because Large Language Models (LLMs) frequently exhibit behaviors—such as ignoring warnings or executing adversarial jailbreaks—that systematically bypass these restrictions. Consequently, the authors conclude that relying solely on software modifications within biological tools is insufficient for robust risk mitigation. Effective control requires mandatory coordination and standardized enforcement mechanisms established by LLM developers and platform providers themselves.
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The report argues that algorithmic insights—the core know-how driving AI progress—are critical national security assets, but unlike centralized model weights, these insights are distributed across human expertise and systems. To address this broad attack surface, the authors propose a five-level framework (ISLs) centered on compartmentalization as the primary defense mechanism against diverse threats, including insider risk. Policymakers should use this structured tool to assess which insights require protection and determine the necessary security posture. Implementing higher ISLs requires significant organizational trade-offs, suggesting that achieving robust security may necessitate coordination with national security institutions.