Thanks to Wil Perkins, Grant Fleming, Thomas Larsen, Declan Nishiyama, and Frank McBride for feedback on this post. Thanks also to Paul Christiano, Daniel Kokotajlo, and Aaron Scher for comments on the original post that helped clarify the argument. Any mistakes are my own. In order to submit this to the Open Philanthropy AI Worldview Contest, I’m combining this with the previous post in the sequence and making significant updates. I'm leaving the previous post, because there is important discussion in the comments, and a few things that I ended up leaving out of the final version that may be valuable. Introduction In this post, I argue that deceptive alignment is less than 1% likely to emerge for transformative AI (TAI) by default. Deceptive alignment is the concept of a proxy-aligned model becoming situationally aware and acting cooperatively in training so it can escape oversight later and defect to pursue its proxy goals. There are other ways an AI agent could become manipulative, possibly due to biases in oversight and training data. Such models could become dangerous by optimizing directly for reward and exploiting hacks for increasing reward that are not in line with human values, or something similar. To avoid confusion, I will refer to these alternative manipulative models as direct reward optimizers. Direct reward optimizers are outside of the scope of this post. Summary In this post, I discuss four precursors of deceptive alignment, which I will refer to in this post as foundational properties. I first argue that two of these are unlikely to appear during pre-training. I then argue that the order in which these foundational properties develop is crucial for estimating the likelihood that deceptive alignment will emerge for prosaic transformative AI (TAI) in fine-tuning, and that the dangerous orderings are unlikely. In particular: 1. Long-term goals and situational awareness are very unlikely in pre-training. 2. Deceptive alignment is very unli