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Andrew Critch argues that no technical AI research is safe or helpful per se. Advances in alignment, interpretability, or obedience can be misused, and usually shorten timelines. Whether your work helps humanity depends on how people will actually apply it, so researchers need explicit social models.
Joe Carlsmith asks: if value is fragile under extreme optimization, how different are human hearts from each other? Applied consistently, the logic behind AI pessimism suggests most humans might be "paperclippers" relative to each other, which reframes alignment as partly a balance-of-power problem, not just a values problem.
Nearly all AI company staff, including those deciding whether new models are safe to build and release, are paid substantially in equity, which likely loses value if their employer stops shipping models. That gives the people charged with sounding the alarm a huge personal stake in staying quiet. Adam argues for a simple fix: pay risk evaluators in cash.
John argues that AI alignment research has mostly converged on "streetlighting": tackling easy, tractable problems rather than the hard bottlenecks to survival. Selection effects reward visible progress, and newcomers flinch away from hard problems. His proposed fix: recruit people with physics-postdoc-level technical skill, and let them form a separate community.