The article discusses Bryan Cantrill’s response to a tweet by former Anthropic employee Jacob Coxon, who claimed that AI could kill humanity by the end of the decade. Cantrill shares a personal anecdote about how his own youthful mistakes caused undue panic among non‑technical peers and warns against repeating that pattern. He emphasizes that domain experts must be cautious when making alarmist claims, especially about complex topics like critical infrastructure, bioweapons, and extinction, and that the burden of accurate information lies with those making such statements.
Mustafa Suleyman argues that artificial models should not be treated as if they possess feelings, preferences, rights, or any entitlement to human welfare. He emphasizes that consciousness underpins our ethical, legal, and political frameworks, and extending such rights to AI would lack evidence and complicate containment and alignment efforts.
Simon Willison reflects on the evolving role of software developers in the age of AI, noting that while AI can produce high‑quality code, it also enables poor execution that leads to project failures. He argues that the industry is beginning to recognize the continued need for human collaboration and expertise to truly innovate. The piece highlights the tension between automation and the essential human element in software creation.
The article explores how public opinion toward AI can shift when people recognize tangible benefits, and examines what occurs when such value is not perceived. It discusses the dynamics of acceptance and resistance to AI technologies based on perceived tradeoffs and benefits.
By Stephanie Kirmer
Simon Willison quotes Jakub Pachocki, Chief Scientist at OpenAI, arguing that the strongest reason to rapidly train smarter AI models is the necessity of building defensive systems against the dangers posed by other AI. Pachocki stresses that powerful, aligned AI will be essential for securing infrastructure, protecting against rogue agents in real time, and inventing new protective measures, making this a primary focus of OpenAI’s deployment efforts. He cautions that the urgency of progress should not justify reckless behavior, noting that the seriousness of the stakes makes a reckless race forward absurd.
Because the alternative is much too dangerous The post We Should Train AI to Betray Its Users appeared first on Towards Data Science .
By Nathan Bos
Simon Willison discusses how the current trend of mining open mathematical problems in a non-renewable way could make these problems scarce. He notes that rumors of a problem can trigger large AI-driven efforts to solve it before original researchers can fully develop their work. This shift may discourage sharing promising research, potentially reversing centuries of open science and harming the field’s future.
What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .
By Chinmay Kakatkar
Our approach to AI policy and political advocacy, transparency, support for thoughtful regulation and AI safety, and that no outside political group speaks on the company’s behalf.
Simon Willison writes about a situation where letters were taken from him, prompting a discussion on dwarf behavior rather than dwarf AI. He notes that dwarf AI does not exist, and that dwarves sometimes misbehave. The piece is tagged with AI and game-design, referencing Tarn Adams, co‑creator of Dwarf Fortress.
arXiv:2609.14796v1 Announce Type: new
Abstract: The threat that AI persuasion poses to human control has been acknowledged in the literature, but not yet systematically studied. Now that persuasion a...
By Joshua Levy, Mick Yang, Kellin Pelrine
arXiv:2608. 00961v2 Announce Type: replace-cross Abstract: AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction.
By Donna M Bye, Levin Kuhlmann