Fascinating. Chief Economist at Apollo: agents could cause a bank run by sweeping household cash into accounts paying 3-5% instead of the 0.1% national average, causing banks to lose a large share of their cheap deposits.

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.

I bet @littmath that in 5 years AI will be able to produce Annals-quality Number Theory papers at an inference budget at or below $100k/paper, at 3:1 odds in my favor.

We just raised a $120 million Series C at a $1.2 billion valuation, led by @khoslaventures, to pursue the full-stack approach to legal AI. Blackstone, Bain Capital, Craft Ventures, Coatue, Vanguard, New York Life, TIAA, Tony James, and Jeff Hammes (former Chairman of Kirkland & Ellis) also participated. Norm Ai (@normativeai ) has now raised more than $260 million since I founded the company less than three years ago to build agentic law. We’ve built a team of more than 200, primarily engineers and attorneys, to embed law into AI agents. Norm Law, an affiliated AI-native law firm running on the Norm Ai platform, uses those AI agents to serve clients like Blackstone as outside counsel, with attorneys supervising and improving the agents. Because Norm Law runs natively on Norm Ai’s agentic law technology and prices based on outcomes rather than hours, benefits of AI can now flow directly to the client. This creates a client-aligned incentive structure, unlike model providers, whose revenue is driven by token usage, and traditional law firms, whose revenue is driven by billing hours of human labor. The key distinction is the integration of the pricing model, the technology, and the people. Norm Law is chaired by Mike Schmidtberger, the former Chair of the Executive Committee of Sidley Austin. Other Partners include the former Global Head of Real Estate at Sidley Austin, a senior M&A Partner from Ropes & Gray, the General Counsel from Bain Capital Ventures, and key attorneys from Kirkland & Ellis, Simpson Thacher, Paul Weiss, Davis Polk, Skadden, Cleary Gottlieb, Latham & Watkins, Paul Hastings, Proskauer, and Pillsbury. Clients representing more than $30 trillion in assets under management use Norm Ai software, deploying legal AI agents directly for their in-house legal teams. Norm Ai’s technology is increasingly deployed to supervise other AI agents operating in regulated environments. When companies deploy their own AI agents in high-stakes roles like communicating directly with clients about investment products, Norm Ai's agents check that those agents are in compliance. The Series C will accelerate expanding practice area coverage and advance our supervisory agents.