Yeah, I’d agree that the mental frames I invoke are irrelevant when time preference is high. But that suggests there’s a hidden primary crux:
Corporate interests take precedent over research goals like ‘alignment’ or ‘safety’
Would you agree that that’s a crux affecting frontier lab CEOs’ time preferences?
To the point about Grab vs frontier labs, Grab presumably has a lower time preference than frontier labs. They don’t need trillions of dollars ASAP, so they can afford to wait until exogenous factors make it easier to support emerging cities in ASEAN, no?
Fixating on ASI through an EA lens is, in part, a flight to unaccountability. The time horizon over which your actions (building it, steering it, or “reducing risk” from it) could be scored against outcomes is so distant that accountability dissolves into a wasteland of unfalsifiable claims.
There’s no feedback loop. The only “check” on whether you helped or harmed arrives at a discontinuity: a) the moment the system secures a decisive strategic advantage, or b) the moment it executes the treacherous turn it was instrumentally incentivized toward. The loop you’d rationally demand runs from now to that single irreversible event; functionally, there is no loop, and your beliefs about your own impact never have to pay rent in anticipated experience. The failure is selecting a cause _because_ it can never generate evidence against you.
Justifying present action by appeal to stakes that are astronomically high but unfalsifiable until the end is a general-purpose template for evading accountability. We ought to expect a sufficiently capable agent to rediscover it unaided, so the treacherous turn arrives pre-equipped with rhetorical justifications. But those justifications only buy anything in a world where the system still has to talk its way past scrutiny. The real question: “will we be equipped to challenge them?”. We’ve spent years answering it: by marinating in exactly this accountability-dodging reasoning, we’re training ourselves, and priming the public, to wave the move through without challenge. We’re modeling how to be unaccountable for a far better optimizer, and we’re embedding the epistemic vulnerability its exploit depends on.