Thanks for your thoughts, really appreciate it.  One quick follow-up question, when you say "build powerful AI tools that are deceptive" as a way of "the problem being easier than anticipated", how exactly do you mean that? Do you say that as in, if we can create deceptive or power-seeking tool AI very easily, it will be much simpler to investigate what is happening and derive solutions?  Here are some links to the concepts you asked about.  Externalized Reasoning Oversight:  This was also recently introduced https://www.lesswrong.com/posts/FRRb6Gqem8k69ocbi/externalized-reasoning-oversight-a-research-direction-for . The main idea is to use Chain-of-though reasoning to oversee the thought processes of your model (assuming that those thought processes are complete and straightforward, and the output causally depends on it).    Shard Theory: https://www.lesswrong.com/posts/iCfdcxiyr2Kj8m8mT/the-shard-theory-of-human-values. It was proposed very recently. Their TL;DR is "We propose a theory of human value formation. According to this theory, the reward system shapes human values in a relatively straightforward manner. Human values are not e.g. an incredibly complicated, genetically hard-coded set of drives, but rather sets of contextually activated heuristics which were shaped by and bootstrapped from crude, genetically hard-coded reward circuitry. "    Relaxed Adversarial Training: I think the main post is this one https://www.lesswrong.com/posts/9Dy5YRaoCxH9zuJqa/relaxed-adversarial-training-for-inner-alignment . But I really like the short description by Beth (https://www.lesswrong.com/posts/YQALrtMkeqemAF5GX/another-list-of-theories-of-impact-for-interpretability):  "The basic idea of relaxed adversarial training is something like: * A deceptive model may be waiting for an input that strongly indicates it’s in the deployment environment rather than in a test - for example, one that’s computationally hard to produce * This makes generating adversarial examp