Lenny's Podcast: Product | Career | Growth
00:05:14 · Tara Seshan
[00:05:14] S0: Or at least I'm not in that room for sure. But I think the piece that is really inspiring to me is that so much of what OpenAI does immediately becomes something that users can touch and feel in the product and that cycle is faster than anywhere else I've seen. When a market is more static or a market is more slow moving, you have the chance to actually do some grand strategy-esque work because it's more predictable or you can at least understand all of the pieces. As an example, payments is certainly a dynamic market to some extent, but it's also an established market and you're able to say, "Ah yes, if I take this batch, my competitor might take this other batch," or reason from first principles very rigorously through what all the next actions might be. And in fact, the nature of that market mandates that you do that. Winners will think more rigorously than everybody else. And if you aren't thinking rigorously, it shows up as carelessness because a lot of those decisions that you made could have been predicted. But in this market, it's so hard to understand what is going to emerge in the future. It's very emergent, it's very fast-changing, it's really dynamic, and most importantly, it's very, very important to stay tied to the research. And so actually being prolific and being more empirical is way more important than being maybe more academic or theoretical. And I think lots of the past companies I've worked at have been very academic and theoretical places. And it was a real switch to go from, rather than writing out some long reasoning doc, almost like a PhD thesis of what I think should be the plan for the next N amount of time, instead it's like, how do I get to something I can try out and test with users as fast as possible? And so yeah, that switch from theoretical to empirical felt very jarring at first. I was like, "Oh, am I not doing my due diligence here? Am I not being thoughtful enough? Shouldn't I be thinking through all of this in a ton of rigor?" But actually you got to try stuff and learn as much as possible. And what that means is the thinking you need to do is being as pointed as possible about what your core hypothesis is, and that hypothesis definition is the most important thing. What is actually, to use the Shishir Mehrotra phrase, like the eigenquestion, what is that specific most important thing to test?
00:08:55 · Tara Seshan, Lenny Rachitsky
And everything else, like any other grand strategy you concoct is not relevant. [00:09:03] S1: I'd love to hear more about that because that's really interesting as almost like here's the thing of the PM role that is not changing. So much is changing, the world is changing, but there's still this piece that is even more important. Speak more to that of what specifically that you think people need to focus more on.
00:09:19 · Tara Seshan
[00:09:19] S0: Yeah, there's so many trappings around the PM role of running execution on time and writing all these specific docs and presentations, et cetera. But the core of it has always been about, what is the most essential question you need to ask about your product? What is the thing that will determine whether your product works or doesn't work? How do you test that? How do you look at the results and how do you feed that back into a loop of refining your hypothesis and running it again? That truly has always been the PM job, and that involves, of course, trying to understand users, trying to understand the market, trying to understand the actual technology you're building, pulling those three things together to make the most sharp hypothesis you can, and then making the test as fast and effective as possible. And I think that is not only not changed, but it's become the most important thing at the company to be able to do. EMs are thinking this way, engineers are thinking this way, data scientists are thinking this way, designers are thinking this way. Everyone has moved to focus their efforts on this really, really important problem definition and testing loop. Like what are we actually doing and how do we know if it's working thing. And from a PM standpoint, it's great because PMs have always been really focused on trying to get that stuff right. That has always been the core of the job. And actually many of the other just trappings of the job have fallen away and that remains the key thing to get right every time.
00:10:50 · Lenny Rachitsky
[00:10:50] S1: You mentioned this idea of a loop. And there's a lot of talk these days, loops were so hot, I don't know, a few weeks ago on Twitter. And it feels like it continues to be a topic of discussion for knowledge work broadly. And the way I understand a loop, essentially AI, here's what success looks like, go off and build and figure it out until you achieve success. How do you think about just this idea of loops expanding from just software engineering to product management to all knowledge work? Do you think that's going to be a thing?
00:11:23 · Tara Seshan
[00:11:23] S0: I do think that increasingly the future of work will look more like steering than rowing in the sense that there will be agents that you'll be able to work with that do a lot of the rowing and your role increasingly becomes steering the ship in the right direction and pointing it in the right direction. And to that point, I think that, that steering might grow higher and higher and higher level. The steering used to be at the level of, I wrote this line of code, press tab, to, oh wait, now I'm directing something a little bit more comprehensive to maybe the goal level, maybe to an even higher level. I think the steering will continue to maybe go up layers of abstraction. But ultimately I think it's still on a person to be able to find which direction are we pointing this in and given feedback and additional data, where do I want to take this thing next? Some of steering I think is about certainly what the data tells you, but a lot of it is about making opinionated call. I think sometimes that we underrate that power of intuition or even positive determinism of what we want the future to be. Like picturing, hey, I would like the product to look this way, not because the converse is not an equally viable strategy, but because I would like the world to look like the direction that I'm pushing it in. And that, I think, will always remain a opinion at least right now that is required from a person. And so I think that loops are awesome. Running agents in increasingly, increasingly larger loops where they're doing more and more of that rowing for you is great, but right now you really still need to steer. And I think work will also look like steering with other people over a group of agents that you guys work with together. Bringing in other teammates into that interaction between you and the agent where it's rowing and you're steering feels also incredibly valuable.
00:13:20 · Lenny Rachitsky
[00:13:20] S1: That's such an interesting way of describing it. There's two thought here that come up. One is if everybody has access to the same tools, the thing that will separate you is human, the person basically. Otherwise, we're all just going to be building the same thing. Everyone could be asking, how do we win? What do we do? And then the unfair advantage almost is the human brain.