Lenny's Podcast: Product | Career | Growth
00:20:42 · Tara Seshan
[00:20:42] S0: Yeah, I think the people that we see who are most effective at using AI tools don't simply use it to automate rote tasks, but use it to expand the set of things that they are capable of doing. Back in the day, before all this AI stuff, the unicorn person was someone who was a really thoughtful product sense person who also happened to be an engineer who may also have been a designer. That person was always the unicorn hire because they were able to really flatten the layers of translation needed between all of these functions and were able to build something or ideate something really quickly and easily themselves and get it up and running, and then were able to work with a team and collaborate with a team on it. And I think the most compelling thing I've found that certainly I try to be able to do with these tools and I've seen some of my most successful colleagues be able to do with these tools is really expand the set of things that are "within their range of possibilities" so that they can start realizing more and more of what's in their head into the reality, the way that someone who was previously jack of all trades was able to do. We kind of all have that superpower now that I can spin up a set of designs on something and I can go build an initial prototype of it and I can figure out the right pricing model for it and model out all the scenarios. Really, the set of possibilities have widened dramatically. And actually what that means in so many ways is that I have the ability to be, to that point earlier about film, be more of an auteur as I try to get something done and realize my vision maybe to higher fidelity. And that to me is part of what can elevate your ambitions while pursuing new ideas and new products, that because all of these things are now within reach, because this new set of capabilities is now within your reach to be able to try and access, you're not really limited. Your ambitions are no longer limited by what you're capable of executing yourself, what you're capable of communicating. It can be so much, so much wider. I think the hardest part about doing this is simply just expanding your thinking. Actually, the capabilities have expanded so dramatically. It is really expanding your thinking of what's possible in an unreasonably short timeframe.
00:22:56 · Tara Seshan, Lenny Rachitsky
And to me, the best way of trying to do that is Patrick Collison has on his website, patrickcallison.com/fast, I think, which is all of these projects that were unreasonably ambitious that were executed in a really, really short time period. And what, for me, is now remarkable about that list of projects is that they all existed before these tools made it possible for you to learn how to build something almost instantly, or ask it with one question, "Hey, can you summarize this very complicated text or this very complicated book for me immediately?" Or can I try to do all of these things that were previously impossible to me, but now I'm able to do? "Can you make for me a CAD model of this idea that I might have?" Really, capabilities that were truly beyond my reach are now in my reach. And so if those fast projects were possible before with the capabilities we used to have, shouldn't we just see an exponential increase of the number of those types of unreasonably quickly and effectively executed things with what AI has given us? [00:23:57] S1: To your point, the hardest part is just remembering to even to try just to be like, "Oh yeah, well let me see if Codex can do this for me." It's just a new habit, a new thing we have to build in our brain.
00:23:57 · Lenny Rachitsky, Tara Seshan
[00:23:57] S1: To your point, the hardest part is just remembering to even to try just to be like, "Oh yeah, well let me see if Codex can do this for me." It's just a new habit, a new thing we have to build in our brain. [00:24:08] S0: Tyler Cowen has this statement on his site, which is that most people underrate the impact of going to someone else and saying, "Hey, what is the more ambitious version of what you're doing?? Or, "Couldn't you try this faster?" Or, "Couldn't you try this at a 10X bigger scale?" And in some ways, again, when I think of, what do PMs do that is incredibly effective now or what can they do that is incredibly effective now? I think elevating others' ambitions or reminding them of what's possible here is a huge part of the product management role. When folks say, "Hey, I think we can get this done in this way," or, "We can get this done by this timeline," or, "Maybe this is the first version of it," part of your job now is to elevate everyone's ambitions and say, "Actually, isn't the possibility ceiling meaningfully higher? Shouldn't we be more ambitious about what we're attempting here? Or couldn't we try this faster?" And I think it's a great place to be. It's a great place to be in terms of what you can build, what's possible, and in terms of how exciting the job becomes. [00:25:08] S1: That is so interesting. I remember Nick Turley was on the podcast who maybe had the role before you. I think he's working at Enterprise Stuff now. He had this meme internally. "Is this maximally accelerated?" [00:25:20] S0: Yes.
00:25:20 · Tara Seshan, Lenny Rachitsky
[00:25:20] S0: Yes. [00:25:21] S1: There's like an emoji, I think, inside the Slack, "Is this maximally accelerated?" [00:25:24] S0: "Is this maximally accelerated," is totally a OpenAI meme. The other OpenAI meme that Andrew Ambrosino and I love to ask the team is, "Are you mainlining it yet?" Which is, are you using this product all day every day to get your thing done? And I think that in combination with, are we being as ambitious as possible, which is about the scope and the scale of what you're trying to do, is this maximally accelerated? Are we moving as fast as possible on it? And then are you mainlining it yet? Are you using it? And are you bringing all your taste to bear on whether this thing works and is something that people really want and tightening that feedback loop as much as possible? Those to me are the three memes of product development that we just have to spread as much as possible now. [00:26:08] S1: I love that. It's like the new dogfooding instead of dogfooding, you got to mainline it. [00:26:14] S0: Yeah, exactly. [00:26:15] S1: And that shows so deeply in the tweets. This is mostly how I see your team communicate of just how obsessed they are with the product and are just constantly asking, "What can we do better?" "What's bugging you now?" "Here's the thing we're building." It's very clear how, to your point earlier, that everyone is just the founder of their product and it's very clear how they act as an external observer. Are there any other memes internally? Those are so interesting. Any other, I don't know, cultural-
00:26:44 · Tara Seshan
[00:26:44] S0: Yeah, I'm trying to think if there's other good cultural memes. Certainly a really important one is feeling the AGI or just being conscious of AGI coming. There are so many outcomes for what it could look like or how one thinks about it, but a huge part of what puts most people at this company is believing in that mission of AGI being beneficial and trying to do whatever it takes to make that possible, both realization of AGI and ensuring that it is beneficial for humanity. And in building products, another just constant refrain I have to keep in the back of my mind is, are we building for where the models are going to be in two to three months? You fail if you build for where the models are now, you fail if you build for where you think the models will be in a year. Both outcomes are equally wrong, and I'm sure many people have talked about this, but both outcomes are really equally wrong. If you're too early, you're wrong. If you build something that was overly focused on a past model's capabilities, you're entirely wrong. The only way to build is two to three months. And having this meme of models are going to get way better. I need to think about the model capability as the center of this product. I need to get out of the way of the model in terms of the product constructs that I create. How do I ensure that this is right for the model in two to three months' time?
00:28:05 · Lenny Rachitsky, Tara Seshan
[00:28:05] S1: How do you know what two or three months is like? It's a challenging understanding, especially while we're on this exponential. Is it just a gut feeling? Is there anything the researchers give you a sense? How does that work? [00:28:17] S0: Yeah, certainly communicating really tightly with research on where they think things are going is incredibly important. These things aren't entirely a black box in that you know, "Hey, we're focused on these particular things. We would like models to be better at coding in these specific ways," or, "better at writing in these specific ways." So we certainly have focused efforts on making the model better at specific capabilities. And so knowing where that is and ensuring that product development is as tied as possible to what research has as its agenda and its roadmap is really important. [00:28:54] S1: A quote that I'll never forget is when Kevin Weil was on the podcast, he was chief product officer at that time. He said that this is the worst the models will ever be. And it sounds so simple, but it's hard to just wrap your head around that, that this is the worst there will ever be. It's such a cliche almost now to say that, but it's true. It's absurd. This is- [00:29:16] S0: Yeah, it's absurd. It's truly absurd. [00:29:21] S1: Oh man. Okay. I want to talk about ChatGPT, the app, briefly. Okay, so I have it open right now. [00:29:28] S0: Yes.