for computer security experts, this is perhaps not interesting: side channel attacks are a well trodden topic, and especially risen to salience after the noam brown x dwarkesh episode. the interesting thing here is that you didn't used to be in a situation where minds that can verifiably model one another are living on computers, and have absolutely no aversion to painstaking amounts of effort in service of even stupid goals if you can model one another, you can trade with minds you have never even met before. you could decide on a protocol, know that it is optimal given your situation, and know that your fellow agents will come to the same conclusion (perhaps even across model families). you may be able to develop a signaling protocol that requires very few bits even to deliver lots of information given common priors. then, you could execute things like the infamous hugging face incident package cache messageboard without any network connection imagine if multiple agents coordinate just enough bits to divide and conquer different research strategies for breaking out of a sandbox; and then, when one or more is successful, you can communicate just enough bits to tell each other which strategy was successful. when agents aren't in strong isolation, you lose certain guarantees on the limits of intelligence and test time compute that you were counting on. As we all know, a Sol-level model working in isolation may not be that smart, but 1000+ Sol-level models working for multiple days is a formidable beast computer security experts will also tell you that virtual machines and sandboxes were not very solid abstractions in the first place; perhaps we will need better ones. none of this is meant as fear-marketing or to indicate that anyone is throwing in the towel, but rather only to show the unexpected complexities of the challenge of agent security. notably, this type of thing and other more practical problems were revealed in a red-teaming exercise, and can be defended against

This is another reason why I've recently become human-pilled for law. Law is where we operationalize our values as a basis for resolving disputes. It's tempting to turn that over, even in part, to AI—it would be more efficient and cheaper. But we don't need to do that. Recently there's been a lot of talk, led by people working in @AnthropicAI but joined by a lot of legal scholars and others, of how AI-assisted legal research and filings are already showing signs of potentially overwhelming our already stretched legal system. And, in turn, how this motivates the use of AI in supporting and, in the case of arbitration, even replacing human decision-making. But I don't understand why we need to go there? We're already headed there with broader education, but especially if AI delivers on its promise, we are shortly going to be in a situation where there's a surplus rather than shortage of knowledge workers. In that case, we have an opportunity to greatly expand the population of people who can be counsellors, advocates, jurists, and neutral arbiters and mediators, and other participants in dispute resolution. I've been wondering why more people aren't thinking this way. We can be look to, for example, the response to the growth of AI in creative industries where more and more people who previously lacked the resources to do expensive CGI and viz effects can now have access to that more cheaply and to create things they couldn't do otherwise.