Research / Sources
Expert essays on AI safety, policy and governance. ai-frontiers.org
Argues against nationalizing AI and proposes a narrower government 'halt right' to pause frontier AI firms, targeting catastrophic risks that taxation, antitrust and contract law cannot handle.
Proposes governance infrastructure for autonomous AI agents, including agent IDs, post-deployment model cards, legal personhood tradeoffs, and financial-system affordance regulation to contain rogue agents.
Argues that AI researchers lack a safe way to report jailbreaks of frontier models and proposes adopting cybersecurity-style responsible disclosure norms, citing universal jailbreaks and malicious misuse.
Argues against letting AI developers choose their own safety auditors, and examines private verification-organization and regulatory-market proposals for frontier AI, including the FRONTIER Act and California's SB 813.
Proposes catalytic regulation, using tax credits, procurement incentives and prestige mechanisms rather than mandates, to make safety a competitive advantage for American AI labs.
Argues that consumer humanoid robots such as Figure 03 and Unitree R1 are entering homes faster than rules govern them, and that regulators should set safety, privacy and liability standards now.
Argues that liability insurance and catastrophe bonds could cover extreme frontier AI risks and incentivize safety standards, but that underwriting gaps and catastrophic scale currently block a market.
Examines whether China's new AI safety body, CnAISDA, and domestic voluntary safety commitments signal a serious national commitment to frontier AI safety or are mostly symbolic.
Summarises Yudkowsky and Soares's book arguing that building superhuman AI by current methods likely leads to losing control, with extinction-level risk, and calling for coordinated action against the race dynamics.
An AI Frontiers opinion arguing that precautionary fears about frontier risks are keeping open-source models behind closed frontier models, and that openness should be the default.
An opinion piece arguing that frontier AI models kept private inside labs pose hidden dual-use risks, and calling for transparency and third-party oversight of these systems while protecting them from theft.
Analyzes the EU's finalized Code of Practice for general-purpose AI, arguing its Safety and Security chapter requires frontier developers to adopt concrete risk management practices that advance AI safety.
Argues that AI safety policy depends on trustworthy evidence, and that flawed benchmarks and corporate influence undermine it, so structural reforms for transparency and accountability are needed.
Argues that AI developers and regulators should adopt risk management techniques from cybersecurity, finance, enterprise and environmental fields, rather than reinventing them for frontier AI safety frameworks.