Verrilli is excited about PMs moving back to hands-on IC work. He personally spends ~50% of his time on IC work, and all PM managers at Whatnot spend 90%+ on it, because senior people can make faster, better decisions and stay connected to ground truth. ✦ AI generated
Tom Verrilli · Lenny's Podcast · 2026-08-02 · original ↗
starts at this moment · 36:54
“Something else you said that uh is changing in how PMs operate that you're very excited about is this move to IC work. PM's moving away from this kind of big org management world to actually doing the work. Talk about that and what that means for the role of product management.”
What does IC work for PM in this context mean? Does it mean shipping code, building or is it like running a team, writing owning a road map, writing the strategy dock? Imagine it's the second bucket. Uh whatever is required to kind of like most effectively ship is the short answer. Um have I personally shipped some production code at whatnot? Yes... But I do think it starts with like are you literally in the support tickets? Do you know what customer problems we're having? Have you pulled all of the data yourself so that you actually understand it? Have you sat with engineering and design? You know, have you queried the codebase directly in order to understand how things work? and then have you written the spec are you then running your stand up... You can see my scar tissue coming through. Um, uh, on our team, uh, everybody is like there are managers, there's like I think four or five people across the team who manage other PMs. All of them would spend 90 plus% of their time doing IC work. I'm still probably 50% of my time doing IC work personally.
verbatim transcript · starts at 36:54
36:42and all of that versus like if you build the right AI tooling, you can spot regressions really quickly, which basically lets people just kind of like go. So, I don't know what the future of data science looks like, but I think uh as a product manager, I've spent less time in the last year talking to a data scientist than I ever have in my career, even though I've probably spent 10 times
37:00more time in data and understanding actually how the product's working than I have ever have in my career. So, that one I think is really powerful. Second one I've already mentioned, which is like stop bothering engineers with how does the codebase work and actually just go and talk to Claude and understand it, which is really helpful. You know, I used to say early on in my career that
37:19the goal was always to be understand your systems at the boxes and lines level of, you know, which system drives which thing. And now there's no excuse not to understand that or a nuance layer. But the other one, and this might be very specific to whatnot, so I don't know that this will help everyone, but one of the things that I've been lucky to do in my career is basically worked
37:38on live products for a decade now. And so it's always been really cool to be able to ship a product and then watch a customer use it and watch them kind of figure it out. So like uh I think people have just gotten this experience with like listen labs and and you know others in that cohort of watching people use your product. But I've always been able
37:56to sit and watch people use a thing for the first time and go through that new user comprehension gap. What's really cool with a bunch of the AI tooling right now is as they're describing, oh, I'm having a problem, you can literally be watching the codebase live and work out, is that actually a bug that's happening right now, right now, or is that a comprehension gap where it
38:16doesn't work as expected and suddenly you've got this video artifact of someone using your product. You can be analyzing the codebase in real time and you can be just talking kind of through AI to the codebase to understand what's actually happening. And it's this it's like you know a feedback loop on steroids because all of a sudden you know exactly what's going on customer side, code side and observe side as as
38:37like a viewer uh in real time which is really cool. >> That sounds uh both awesome and very stressful to be building products that are that live in real time. I think about um Netflix where they invest in live now and that's just like all you've done for 10 years and how big of a deal that was for them. I know the scale is different but >> honestly my second week at whatnot um I
38:57remember sitting in a room watching so uh uh whatn not for those not familiar uh kind of commerce largely auction platform that live commerce platform um and there's varieties of different ways to run an auction but one of them is what's called sudden death which is like when the timer ends it ends right otherwise the classic auction environment somebody bids in the last 5 seconds it adds 10 seconds back on the