Lenny's Podcast: Product | Career | Growth
00:38:55 · Tom Verrilli
[00:38:55] S0: Honestly, my second week at Whatnot, I remember sitting in a room watching, so one of those not familiar kind of commerce, largely auction platform, live commerce platform. 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. Otherwise, the classic auction environment, somebody bids in the last five seconds, it adds 10 seconds back on the clock, and a seller can decide what auction model they want. And I was sitting there watching a seller who was like, "These are taking too long. I wish this seven-second timer was actually three seconds because I want to move more product." And I watched two engineers in the office look at each other, and be like, "That's a [inaudible 00:39:33]. We could totally do that." And so they went and updated it in real time and then jumped in the chat of a show, and just said, "Refresh your app." And then all of a sudden, bang, it was operating that way. And I was like, "Cool. I'm with my people. I'm in the right place." Because that's the level of responsiveness that you can get in a live environment. And obviously AI means that that's really easy for lots of people to take on. Not that I would touch production code in that kind of way because that's a disastrous idea. But for qualified humans, it's a wonderful one. Yeah, listen, I have a lot of empathy for that because I've certainly seen it. Another plug for why having fewer more senior PMs is helpful because those are folks who've seen more of those reps and understand. But I also think a lot of the time that lack of clarity comes from the other thing, which is organizations historically underinvested in data engineering and data structures and good data labeling, and whether or not you've got, not just your taxonomies right, but whether or not you really understand whether or not your data systems and structures are set up well. And so what we've found is a lot of our best data scientists are pushing in that direction of are we actually correctly updating all of the ways in which our tracking and attribution works so that it's less easy for people to misunderstand those? And then yeah, using an AI tool to find a piece of data, much like using an AI tool to write code, doesn't absolve you of responsibility to make sure that that was good analysis, good code.
00:42:40 · Tom Verrilli, Lenny Rachitsky
It just tends to be leveraged for those people who are naturally inclined that way. [00:42:48] S1: So thinking about these different functions, data science, user research, design, engineering, PM, what I'm hearing so far is we'll need probably fewer PMs, we'll need fewer data scientists. Are there any other roles that you think they're trending down in terms of we'll need... Are there any roles trending up like, wow, we're going to need a lot more of this kind of person?
00:43:07 · Tom Verrilli
[00:43:07] S0: Well, funnily enough, as I say, I haven't spoken to a data scientist in a while. We've certainly still hired plenty because I do think all that tracking and attribution and measurement, really, really powerful. I also think one of the flip sides, if we say we need fewer, it's like fewer of for the same output doesn't necessarily mean fewer of in macro. Because if you are using these systems right, you can just grow more quickly. You can build more things, you can take on more stuff. So I would be surprised actually if we ended up with net fewer. I think it's more like net fewer vis-a-vis customer impact for both. Certainly I think trending up over time within these roles are these kind of, and I don't remember the exact label that people used to use, but this idea of tech leads that are kind of like a hybrid EM where you're running a very small team, running at things. Because same way that we'd say the cost of trying something has come down, the idea that says you can have core focus, which is a thing we're very big on. We've also found incubating a lot of smaller teams to go over and sit in the corner and just try and build this thing. It's not quite prototyping, but it's like go and take a swing at a thing that we've historically thought was too hard, is getting cheaper and increasingly has very high leverage. So the idea of engineering manager light, for want of a better term, is the thing that I'm definitely think that we'll see more and more of. It's not quite the technical member of staff, although that must be lovely, but certainly not the kind of full-blown EM role I think is one that definitely will come about more often.
00:44:49 · Lenny Rachitsky
[00:44:49] S1: So maybe just to close the loop on this part of the conversation, there's this trend towards everyone's kind of a builder, PMs are shipping a little bit, being a little more engineer. Engineers are taking on more of the PM work. How do you think this just maybe plays out over the next couple of years in terms of what product teams look like broadly? Is it still PM engineers, a designer, data scientist somewhere? How do you envision the canonical product team over the coming years?
00:45:16 · Tom Verrilli
[00:45:16] S0: Great question. And I don't know that I know what it looks like everywhere. I think what it'll look like at Whatnot is it probably still looks mostly like it has historically, which is there are reasons that you would have a specialist designer, specialist engineers, specialist product management. I think those very concrete teams will largely be preserved for very specific projects or things that we have high confidence or conviction that we need to solve, or we're pretty high confidence conviction that we have a path forward on a thing and we want to make good progress. And I think at the edges around that, there's going to be a lot more free space for people to play on like, "Hey, I'm reasonably sure I can go and make a meaningful improvement to this thing." And it doesn't matter if you are a designer, an engineer, a product manager, a data scientist, you can and you should. If you're sitting there on a Friday afternoon and you can't focus on the POD you're writing, but you're pretty sure you can go and fix something, go for it. And so I think it probably doesn't morph in the more formal sense, but I do think there's just a lot more free space for people who are well-versed in the customer problems, well-versed in the code base, and understand some of that macro context. We'll just be empowered to do more and more things.
00:46:28 · Lenny Rachitsky
[00:46:28] S1: Two and a half years ago, I had this post I put out where I said, "Why PMs are the best positioned role in tech to thrive in an AI world." And I feel like, even though the beginning of our conversation was like we should live in a world where we regret PM exists, I feel like we agree on this idea that the skills that seem to matter most and are going to be most valuable, whether it's a PM doing them or an engineer or designer, is very PM-y skills. I'll share a few examples from this post. Who is really good at this stuff, these things? Identifying what to build, distilling, communicating requirements, prioritizing everyone's ideas for the highest ROI opportunities, giving feedback and design to improve impact, developing go-to-market strategy, understanding business strategy. To me, this is what PMs do, and it feels like that's becoming more and more important. So I guess the question to you is, would you agree with this idea that the PME skills seem to be the most valuable now as AI takes on the building?