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An analysis of how the tragedy of the commons applies to AI, discussing shared resources and collective action problems in AI development.
Analysis of the emerging geopolitical AI order: the US-led Pax Silica coalition, the EU's joining, and China's rival World AI Cooperation Organisation, suggesting a multipolar rather than bipolar landscape.
The EU's AI Act introduces new transparency labels and rules for deepfakes, aiming to track AI interactions and hold providers accountable for disclosure.
A detailed account of the OpenAI board's firing of CEO Sam Altman in November 2023, including the employee revolt, Microsoft's involvement, and the board's capitulation.
Rep. Lori Trahan criticizes the White House for blocking public release of its advanced AI model evaluation framework, arguing AI governance belongs in a civilian agency and Congress should set rules, citing her bipartisan FRONTIER Act.
The author argues that AGI must be built as an open-source framework with public inspection and governance rather than a closed proprietary weapon, claiming open source provides accountability while closed systems concentrate power. They advocate for open safety rules, audits, and staged releases to manage risks.
This paper introduces the Human Utility Factor (HUF), a computable welfare metric that reframes AI governance as a constrained optimisation problem with measurable levers for automation depth, redistribution, and employment coverage, validated through multi-agent simulations.
Stanford HAI discusses the emergence of world models and spatial intelligence in AI, calling for governance frameworks that address capabilities beyond language processing.
Mark Zuckerberg warns that concentrating AI power in a few large companies is dangerous, highlighting risks of centralized control over advanced AI.
The article argues that the AI race is shifting from raw model capability to trust, reliability, and safety, with companies like OpenAI, Google, Microsoft, and Anthropic investing in responsible AI and governments introducing regulations.
This paper develops a spectral framework for analyzing rotary phase alignment, semantic continuity, and representation drift in transformer language models, proposing a bounded spectral method and distinguishing internal coherence from execution-boundary governance.
Anthropic supports a petition for slowing AI development, citing their research on recursive self-improvement to allow society time to prepare.
After the AGI Summit, Charlie's view that the stronger AI becomes, the less we should let go resonated, emphasizing traceability of decisions and human intervention. The author thanked participants, noting that real conversations are the meaning of the journey.
Employees of leading AI labs including OpenAI and Anthropic have signed a statement urging the US government to support international efforts to slow frontier AI development and strengthen governance tools, citing risks of accelerating automated AI research beyond human control.
A first systematic public visualization from Airwars and an AI safety institute maps AI's operational role across all six stages of a modern military kill chain, directly fueling debates on AI governance and accountability.
The author warns that restricting open AI models concentrates intelligence in few hands, posing a threat to humanity, and argues for open models as a necessary counterbalance.
The article critiques private AI companies embedding safety restrictions in military AI systems, arguing that ethical decisions about surveillance and autonomous weapons should be made by democratic institutions rather than private corporations.
The post explores the potential design of agentic operating systems, where users describe outcomes rather than directly interacting with programs, and raises concerns about trust, privacy, opaque decision-making, and vendor lock-in.
Kai-Fu Lee comments that the notable aspect of the Open Weight letter is who didn't sign it, linking to Microsoft's response on open-weight models.
Docker Captain Karan Verma explains why AI governance must be enforced at runtime rather than relying on prompts, breaking down the execution, tool, and resource boundaries that build developer confidence in autonomous agents.