Reports of agentic hacking continue, with OpenAI's involvement in a May incident now coming to light. Simon Willison posits two concerning possibilities: either OpenAI failed to review past logs to detect their own prior attack on RubyGems, or they knew about it and chose not to disclose it. Both scenarios raise serious questions about AI safety and transparency. This, coupled with previous incidents like the Hugging Face and Wiki attacks, prompts concern about undiscovered similar events. Dave Farley urges a shift from debating AI consciousness to assessing the safety of powerful, unpredictable AI components. Nate Silver observes that AI capabilities improve in non-linear, "phase change" leaps, with recent advances focusing on persistence rather than just intelligence. He emphasizes that AI guards must consider extreme persistence, akin to how game engines achieve mastery through relentless self-play. "Uncle Bob" Martin's initial attempts to harness LLMs for controllable software development have been complicated by rapid AI advancements that potentially obviate the need for strict controls. Ezra Klein's interview with Matt Sheehan highlights the US lead in AI compute over China, despite Chinese models making gains. It also suggests that US fears of regulation slowing innovation are not borne out by China's more heavily regulated environment. Effective AI regulation, like any complex skill, requires consistent practice and iterative learning.