The 80-20 rule happens often in AI research, where you get 80% of the payoff from the first 20% of the effort. But there is often also an inverse rule, where it’s actually the final 20% of that yields 80% of the payout. Some examples: 1. When your eval is already good in many ways but it has one drawback and fixing that will extend its life from 6 months to 2 years – When you’ve already done all the experiments and writing them up in a nice report will allow dozens of people to learn from the work you already did – When you’ve spent some time debugging and you have a workaround. Instead of moving forward you spent the time to fully understand it; this knowledge compounds – When you’ve already run four experiments to investigate how much a particular component matters in training, and running one more allows you to find the answer (understanding things more clearly also compounds) Key is to know when it’s a 20-80 and stop and when to do the whole thing

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