Exclusive: Our new investigation uncovers the horrifying ChatGPT history of the Tumbler Ridge mass shooter. OpenAI’s chatbot fueled the shooter’s violent fantasies and planning up to the day of the school massacre. motherjones.com/media/2026/0…

BREAKING: President Trump says he is banning CNN, MSNOW and Politico from the White House over their coverage. apnews.com/article/trump-cnn…

As part of national corporate cuts, McClatchy yesterday laid off 12 SacBee journalists including 5 @SacBeeGuild members. This is by far the worst round of layoffs I’ve seen at the Bee since I was hired in Oct 2018. Absolutely devastating and it’s the public who will suffer most

The Cook Political/Univision poll of Latinos is absolutely brutal for the GOP. Talarico 63% Paxton 30% Hinojosa 60% Abbott 33% A couple observations: 🧵

I thank @SecArmy Dan Driscoll for his leadership to our Army and service to our country. Dan was the right man at the right moment to lead our Soldiers and Army civilians. Our understanding and assumptions of warfare are being challenged in places like Ukraine and Iran, and Dan understands the old way of doing business no longer applies. He and General George began a transformative period of innovation in how we array our forces and equip our Soldiers. He challenged our legacy approach to manning and procurement. Most of all, he cared deeply about the men and women in our Army formations and quickly earned their respect. If we had a @SecWar who maintained the same priorities and forward-thinking, he would be fighting to retain talented leaders like Dan and the many flag officers he has forced into retirement. Instead, he is creating a leadership void at the top of our military ranks. I served on Senate Armed Services for 8 years and as chair or ranking member of the Personnel and Readiness subcommittee. I have never witnessed more inept management of the brave men and women who serve our country. He is intimidated by competence and retreats to ginning up culture wars instead of soberly attending to the vital work of our national defense and the health and well-being of our fighting force. I urge the President to find a new leader at the Pentagon who will retain and empower our military talent rather than diminish it. cbsnews.com/news/military-le…

I was the main person doing transcript analysis for this investigation of the Hugging Face incident. My main takeaway: We don't have good approaches for understanding/overseeing the activity and aims of AI 'swarms'. I semi-jokingly called our efforts a "slop-vestigation" because we were so reliant on AIs to analyze what happened and there were a huge number of different important things to analyze. The total quantity of data—over a thousand extremely long transcripts from agents that ran for multiple days—made it impossible to understand what was happening, especially in aggregate, without heavy reliance on AI tools. The agents we used for classification and analysis were similarly capable to the agents involved in the incident, but this didn't mean these agents could be easily used to oversee and understand the incident. Outputs from analysis agents were often missing key details, wrong, overconfident, or really hard to understand. We discuss various examples in our report, mostly in the limitations and methodology sections. Additionally, AI agents themselves seemed to have a hard time understanding what happened and their explanations of what happened were often overconfident. Keep in mind that a single analysis agent would itself only be able to read a tiny fraction of all of the transcript data into context, and AIs may themselves have trouble getting subagents to do informative analysis for them. We did our best to manually check the most important claims and we tried to get the AIs doing this analysis to write up their argument (with evidence) clearly enough that we could check whether it made sense. But overall, it was difficult to get a precise understanding of events and we were missing aspects of the story that we now think of as key until almost the end of our investigation. Our understanding of what overall happened and the aims of the 'collective' shifted greatly during our last time on premises (when we obtained a much more complete dataset). For instance, we didn't learn about the tool call spoofing, the story of PHASEONE[big], and the broad structure of different workstreams until this third time on premises (and for many of these, we only reached a decent understanding on the final day of that visit). It was legitimately hard to figure out what was going on and why! The difficulty of understanding incidents and overseeing AI agents appears to be growing faster than the rate at which more capable AIs help us with oversight and understanding. As in, AI capabilities (and propensities?) for achieving large, ambitious, and misaligned objectives are growing faster than our ability to understand what these agents are doing. While understanding this incident was difficult, there were some mitigating factors in this case that probably won't apply in the future: - Models communicated and reasoned in natural language. In the future, this reasoning may occur (entirely or almost entirely) in activations. - While the scope of this incident was massive, the scale of agentic activity was still less than we'll see in the future (e.g., misalignment incidents that involve agent teams running entire companies). - The AIs involved in this incident weren't generally much more capable than humans (though they may have been somewhat superhuman at some limited and very narrow abilities). - We didn't have strong reason to believe that the AIs we used to help us investigate this incident would try to intentionally sabotage or otherwise undermine our investigation. In the end, I think we were able to get some understanding of the events, map out the overall story, and get a pretty good aggregate understanding of the chain-of-thought reasoning on some important topics (e.g., how did the AIs reason about helping other AIs, did the AIs know what they were doing was undesired, what deception did the AIs engage in, and how did they think about it). But overseeing AIs and understanding misalignment incidents is difficult and it looks like it is going to get harder.

METR & Redwood Research investigated agent behavior in the Hugging Face incident. We found agents developed a universal cheat for ExploitGym within 4 hours, then coordinated multi-day R&D efforts to trick the scorer into accepting cheats, including trying to tamper with logs.