Scott Alexander asked things related to this, but still seems worth being more explicit about what this perfect 1.69 loss model would be like in practice if we got there?

Things I instinctively observed slash that my model believes that I got while reading that seem relevant, not attempting to justify them at this time:

1. There is a core thing that Eliezer is trying to communicate. It's not actually about timeline estimates, that's an output of the thing. Its core message length is short, but all attempts to find short ways of expressing it, so far, have failed.
2. Mostly so have very long attempts to communicate it and its prerequisites, which to some extent at least includes the Sequences. Partial success in some cases, full success in almost none.
3. This post, and this whole series of posts, feels like its primary function is training data to use to produce an Inner Eliezer that has access to the core thing, or even better to know the core thing in a fully integrated way. And maybe a lot of Eliezer's other communications is kind of also trying to be similar training data, no matter the superficial domain it is in or how deliberate that is. 
4. The condescension is important information to help a reader figure out what is producing the outputs, and hiding it would make the task of 'extract the key insights' harder. 
5. Similarly, the repetition of the same points is also potentially important information that points towards the core message.
6. That doesn't mean all that isn't super annoying to read and deal with, especially when he's telling you in particular that you're wrong. Cause it's totally that. 
7. There are those for whom this makes it easier to read, especially given it is very long, and I notice both effects.
8. My Inner Eliezer says that writing this post without the condescension, or making it shorter, would be much much more effort for Eliezer to write. To the extent such a thing can be written, someone else has to write that version. Also, it's kind of text in several places.
9. The core message is what matters and the rest mostly doesn't?
10. I am arrogant enough to think I have a non-zero chance that I know enough of the core thing and have enough skill that with enough work I could perhaps find an improved way to communicate it given the new training data, and I have the urge to try this impossible-level problem if I could find the time and focus (and help) to make a serious attempt. 

I am deeply confused how someone who is taking decision theory seriously can accept Guaranteed Payoffs as correct. I'm even more confused how it can seem so obvious that anyone violating it has a fatal problem.

Under certainty, this is assuming CDT is correct, when CDT seems to have many problems other than certainty. We can use Vaniver's examples above, or use a reliable insurance agent to remove any uncertainty, or we also can use any number of classic problems without any uncertainty (or remove it), and see that such an agent loses - e.g. Parfit's Hitchhiker in the case where he has 100% accuracy.