A simple argument for trying less hard

People often make arguments against “trying hard” (working very hard, pushing yourself to the brink, being intensely goal-directed, and so on) by pointing to the risks of burnout or of losing some kind of wholesomeness[1].But there’s another, very simple argument against it that I have not seen anyone fully make explicit[2], even though I think it’s very important. It goes like this:We face a lot of uncertainty about the sign of our impact.Therefore, we should be very vigilant about our epistemics to make sure that we are not having a negative impact in expectation.But trying hard deeply distorts our epistemics - it makes us more prone to motivated reasoning about what we’re doing, and leaves us with less slack to reflect on it.Therefore, all else being equal, we should try less hard.Crucially, this argument applies much more strongly to people working in “longtermist areas” - which other critiques of trying hard generally don’t do. For example, global health EAs whose terminal value is short-term welfare also face uncertainty about the impact of their actions - but much less (especially about the sign) than people trying to improve the long-term future. So this argument suggests that it’s especially dangerous for longtermists to try very hard.[3][4]I’ll go through the steps in a little bit more detail.UncertaintyMuch has been said in EA about cluelessness and crucial considerations, but I’ll highlight a few specific concerns that could make lots of current AI safety work[5] net negative (with no claim to novelty):AI governance interventions are obviously high-variance: bad regulation can easily make things worse, many interventions could increase the risk of great power conflict, increased political polarization around AI could be really bad, more centralization of power increases authoritarianism risk, and so on. And technical work can have flow-through effects on these variables that outweigh its direct effects[6].Activist work can polarize people against the cau