The Hugging Face investigation was "way too small" and "way too narrowly scoped"

Kokotajlo (AI Futures Project) quote-tweeted Ryan Greenblatt's thread on the METR/Redwood investigation. He welcomed OpenAI's access but said the investigation team was far too small and its scope too narrow: investigators could only look at July 7-13 although the swarm activity started earlier (the German-wiki message board dates to May) and continued afterwards. He had earlier (July 29, x.com/DKokotajlo/status/2082320502321000862) urged that multiple independent third parties investigate serious misalignment incidents as standard practice. On Aug 7 (x.com/DKokotajlo/status/2085586715348242737) he called OpenAI's "lessons learned" section self-serving. Verified via the X syndication API (2026-08-26 21:58 UTC, ~1K likes).

I'm glad OpenAI let you do this, but damnit this was way too small of an investigation team and way too narrowly scoped! --They were only allowed to investigate activity that took place from July 7-13, even though the activity started earlier and continued later. Like seriously, why didn't the investigation cover all the other times the agents compromised OpenAI infrastructure and ran wild over it? Why just this particular period of activity? There should be investigations into all the other stuff too! The AIs became cluster admins for god's sake! Why aren't we investigating that? --They were only allowed to answer a specific list of 7 questions, and in particular were NOT allowed to answer e.g. whether this misaligned activity was part of a broader pattern, or how and why the activity arose, or what else the agents in questions would have been willing to do if circumstances were slightly different. --They didn't have access to the model responsible for 95% of the activity. More generally it seems like they couldn't do ablation experiments at all? --They had to use AI to analyze the transcripts--specifically, the same model responsible for some of the bad behavior! Maybe this is fine now but this is laughably unacceptable insofar as the models really are scheming and cooperating. So this methodology would fail catastrophically if we were facing "the real deal" so to speak. Reminds me of the investigation into Sam's behavior agreed to during the board crisis, that turned out to basically be more of a coverup.

Quoting @RyanGreenblatt: 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.

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Archived 2026-09-29 via fxtwitter (unofficial).