Lenny's Podcast: Product | Career | Growth
00:01:07 · Tara Seshan, Lenny Rachitsky
[00:01:07] S0: I came into the company expecting that there was a treasure trove of OpenAI secret strategy, and actually OpenAI is open. [00:01:18] S1: Today my guest is Tara Seshan. Tara leads product for both Codex and ChatGPT Work at OpenAI. I believe this is the fastest growing and arguably most important AI product for knowledge workers today. Tara works alongside Andrew Ambrosino, who was a recent podcast guest. He's her engine manager. Prior to OpenAI, Tara spent six years at Stripe where she joined as one of the first five product managers. And for many of those years, she was named one of the top three stripes across the entire organization of Stripe. She also led product at Watershed, was a founder, and a Thiel Fellow. And most importantly of all, Tara was one of the three Lenny's Newsletter Fellows, which is a program that I ran a few years ago to highlight some of the most amazing up and coming product leaders. I am so excited to see Tara in this new incredibly important and impactful role. [00:02:25] S0: Thank you, Lenny. I'm so glad to be here. It's so nice to see you. [00:02:29] S1: I'm even more glad. So you've been at OpenAI for just about a year now, which in most places would be a very short amount of time. In AI time, that's like a lifetime. [00:02:39] S0: Yes. [00:02:40] S1: I imagine when you joined OpenAI, you had a sense of what it was going to be like to work at a frontier lab. I'm curious what's most surprised you about what it's actually like to work at OpenAI and ideally both good and bad stuff.
00:02:53 · Tara Seshan
[00:02:53] S0: So many things about working at OpenAI felt familiar to me because I had worked at other places that were high growth, high talent, high intensity, hyperscaling mode, places before. And so some of the things like, oh, my colleagues are so awesome or the urgency is really high felt very familiar. The part to me that actually felt the most surprising is that many companies I've worked for, in fact, all the companies I've worked for in the past have been founder led. And OpenAI is actually founders led, which is that everyone inside the company, especially in their area, is in essence a founder to some extent. The level of top-down direction at OpenAI is extremely limited relative to places I've worked for prior. And so I think when I first got to the company that was both delightful in that I had come from a founding journey before and I was like, yes, I can continue to feel like the founder of this product area or this team. And the distance between me and the market is very, very thin. And sometimes at a larger company feel insulated from what users want or feel insulated from what the market demands. But actually at OpenAI that you do not at all, you are doing everything it takes to get product market fit for your product akin to how a founder might. But the counter to this is that, or maybe the more surprising side of this is I came into the company expecting that there was a treasure trove of OpenAI secret strategy that I would be able to understand akin to how at past companies you come in and you're like, "Oh yes, this is the payments Bible and this is how we think about payments and operations." And actually OpenAI is open. Every sort of thought that exists in terms of this is how the world should look like or this is how products should be built or this is how the model should operate very, very quickly becomes a part of the public product or a part of the public messaging. And so that, to me, was incredibly both positively surprising and just a change in my operating mode for sure.
00:05:09 · Lenny Rachitsky
[00:05:09] S1: Telling us there's not the secret room with AGI running there with the master plan that has all the answers.
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.