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Valentine argues that self-deception often solves a real problem: dealing with "hostile telepaths," people who can partly read your inner state and might punish what they find. Since self-deception persists until that problem is solved another way, he proposes alternatives like gaining independence or occlumency, plus a method for unwinding it.
When Eliezer finishes a long chain of thought, or gets surprised by reality, he looks back and asks: "How could I have thought that faster?" He reviews which steps did the real cognitive work, then trains himself to reach the same destination by a shorter route next time.
Elizabeth argues that seeking contact with reality is the meta-principle underlying all other (consequentialist) principles: without it, you can't meaningfully pursue goals at all. Drawing on many examples from Effective Altruism, she explores facets of truthseeking, from delegating rather than deferring to protecting the epistemic commons, and suggests concrete actions.
A Ponzi scheme spokesperson cries "Empiricism!" because past investors got paid. Eliezer argues there's no theory-free prediction: every extrapolation from past data relies on assumptions about what's going on underneath. The same bad epistemology appears in claims that future, smarter AIs will be safe because current AIs seem nice.
Joe Carlsmith explores "attunement" – a meaning-laden receptivity to the world that he associates with the "green" vibe of the MtG color wheel. He argues it's neither paradigmatically blue (knowledge) nor red (passion), that it's core to how many of us discern what matters, and that a wise future should preserve it.
Anna proposes a toy model of willpower: your conscious planner "earns" trust from the rest of your psyche by choosing actions that pay off for your deeper processes. Like Ayn Rand's "living money," willpower spent on good bets replenishes itself. Willpower spent carelessly runs dry, and that's burnout.
In a puzzle-game planning exercise, participants repeatedly said "this is impossible unless [questionable assumption] is true," gave that assumption ~70%, and were wrong every time. Raemon uses this to illustrate how easily we slip into convenient worlds, and why AI safety researchers should be wary of optimistic assumptions in long-term plans.
Circular reasoning gets a bad rap, but abramdemski argues it's logically valid, and even weak positive evidence for its conclusion. Its real flaw is only that it fails to convince people who don't already agree. And since all justification chains eventually loop or regress, some circularity is unavoidable.