In the 'Fail corner,' Verrilli shares his most recurring failure: relying on average utility or adoption figures, which hides small groups for whom a feature is 100% of what they do, and can blow up a real community or business. ✦ AI generated
Tom Verrilli · Lenny's Podcast · 2026-08-02 · original ↗
starts at this moment · 135:25
“What's an example of a time in your career where things failed something you build some career move you made that didn't work out and then what what did you learn from that experience?”
there probably is a really common thread to it and I think it's probably an easy trap for for any PM to fall into which is like um averages mean nothing to the individual is probably the thing that I've like really scarred by. uh in any sizable population, it's really attractive to go and look at like average utility or average adoption of something and then you find that like you know only 3% of people use something and you're like cool we can probably get rid of that feature. It's not used widely but if you don't go a layer deeper and be like actually for like you know it's only 3% of something but there's a group of people for whom that's 100% of what they do... this is their core use case and for expediency's sake because somebody doesn't want to maintain a feature anymore you're just going to deprecate it and then it turns out you like blow up the use case of that group of humans... I think, you know, I've probably screwed up in all of the ways in my career, but most of the time I've made genuinely like I'm disappointed in myself levels of decisions. It's typically that I've relied on averages without thinking about the individual use cases that that are hidden underneath.
verbatim transcript · starts at 135:25
(01:13:43) like I fail more often than I succeed across the course of my career. Genuinely, uh to the to the blog you referenced right at the start, I published in the in the back of that the actual document we use internally to talk about how we build and it starts with like batting 500 is like the goal. So, like you're hoping to be right as often as you're wrong. So there's
(01:14:02) probably just too many specific examples of of times I've screwed up in my career. But there probably is a really common thread to it and I think it's probably an easy trap for for any PM to fall into which is like um averages mean nothing to the individual is probably the thing that I've like really scarred by. uh in any sizable population, it's really attractive to go and look at like
(01:14:22) average utility or average adoption of something and then you find that like you know only 3% of people use something and you're like cool we can probably get rid of that feature. It's not used widely but if you don't go a layer deeper and be like actually for like you know it's only 3% of something but there's a group of people for whom that's 100% of what they do. this is
(01:14:41) their core use case and for expediency's sake because somebody doesn't want to maintain a feature anymore you're just going to deprecate it and then it turns out you like blow up the use case of that group of humans and then to your last point about network effects the ongoing spiral effect of that can be enormous you know I I think about it a lot in e-commerce of like this is