Lenny's Podcast: Product | Career | Growth
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.
00:13:42 · Tara Seshan
[00:13:42] S0: Yeah, I think it reminds me a lot of fashion actually in some ways. There are certainly functional clothes that everybody can wear and gets the job done. But so much about what you wear at least, or how I think about what I wear, is about what statement I want to make about my individuality or how I want to reflect to the rest of the world. And a lot of what makes that compelling is how it contrasts with other people's expression. The shirt I make makes a statement only because it is maybe different than what everybody else is doing or different than some cohort of people are doing or makes a statement about my group membership or something of that kind. And I think a lot of the products that we build feel similarly opinionated and artistic. Patrick Collison has this really nice statement, or maybe it's John Collison, has this really nice statement about software, which is that software is not like real estate. You don't put money in and get value out. It is a little bit more like filmmaking where you can put a lot of money into a film, but that doesn't guarantee that the film is successful or good. There is some auteur statement or is some opinionation and artistry that goes along with it. And I think that relies on you having something interesting to say or your team having something interesting to say about your product.
00:14:54 · Lenny Rachitsky, Tara Seshan
[00:14:54] S1: There's something Marty Cagan is big on, which is this idea that when you have an idea for a product or a future, rarely is that idea the thing that ends up being. There's this whole process you go through to figure out what the hell actually it should be. And it feels like that's kind of what you're saying here is you need to go through that process as a human to understand what it really is and what people actually want. It's never going to be like, okay, got it. Go build this thing. I got it from the beginning. [00:15:19] S0: Yeah, for sure. For sure. And those loops are moving faster and faster and faster. And so your ability to form those intuitions, get the information you need to form those intuitions, and then use that with people and agents to put that into action is the key. [00:15:36] S1: I'm curious what you think the next shift will be in how we work just broadly as knowledge workers. It feels like not only do you have access to the most advanced tools that other people don't get, also you work around the most AI forward people in the world. How are people working internally that you think will become a more normal way we all work using these AI tools in the next, I don't know, three to six months?
00:16:02 · Tara Seshan
[00:16:02] S0: Yeah, I think there's two aspects to this. One is continuing to work with agents at higher and higher levels of abstraction. So letting the agent do more and more for you independently, coming in, providing that steering, and then letting the agent continue to cook. Let the agent cook and provide details at higher of orders of abstraction feels like the way. People are increasingly thinking about agents that are persistent, that feel like teammates, that feel like coworkers, where you can work with them. The way I might work with someone on my team, which is they do a whole bunch of work, I provide input, and then they do work again. And we sync up at different cadences, look at each other's in progress work and provide more and more feedback. It feels like that coworker model is the way that things are certainly going. It feels like a much more natural interface for us to be able to work with agents, and we already see a lot of that internally as well. The second is that a lot of my work with agents thus far has been one-on-one. I work with my agent, maybe it's spawned some subagents to get some tasks done, but it's me and my agent together, and that is potentially divorced from what my colleagues are doing with their agents. And so there was a time where everyone internally was just sending their Codex threads, screenshots of their Codex threads to each other on Slack. And we're like, "Okay, well, I wanted to share with you how I got to this number. Here's how I got to this number. Here's a screenshot of what I did." But that's also not quite the most natural way for someone to collaborate together. And so as more and more work gets done with our agents, shouldn't we be able to get work done with our agents together? And what is the most natural interface to make that happen? And those are some of the things that we're thinking about.
00:17:50 · Lenny Rachitsky, Tara Seshan
[00:17:50] S1: Chat is what I'm picturing. That makes so much sense. It's like, okay, here's Tara's agent, here's my agent. She did some work on some analysis. I'd be, "Hey, my agent, Lenny's agent, go check, make sure this is legit and connects to the way I think about the world." [00:18:04] S0: Ideally, work feels like a multiplayer game where all of us together are getting stuff done, steering our agents as our agents continue to take care of more and more of those rowing tactical tasks. [00:18:15] S1: It's interesting how it's just been this slow progression of trust and just awareness that this can be how we work. Just this, "Okay, go work for longer, you can take on more." There's been this talk of the slow takeoff, the fast takeoff scenarios, and everyone's afraid of this fast AI takeoff where it's way too smart and now we're in big trouble. It feels very much like we're on the slow takeoff scenario, which is good, where it's just slowly iterating. It doesn't feel that slow, but in a sense, we're not to some 300 IQ AI.
00:18:47 · Tara Seshan
[00:18:47] S0: I mean, the models are incredibly smart. But I think a lot of the things that have enabled us to then work with our agents together or have the agents take care of higher and higher order abstraction things certainly are about the intelligence, their ability to perform long-running tasks, and how long they can stay on task. But also actually, there are very meat and potatoes, tactical things that make this possible. Agents working locally are really convenient because they have access to all the data that's on your machine. To make an agent successful in the cloud, there is a ton of cloud infrastructure that you have to build to make that possible, and just access to your systems. How can agents talk to all these third-party systems that have all of your data? Just like a colleague who you hire, who you lock into a room, never give them access to Google Docs and Slack, and I don't know, the company database would not be that useful to you. Similarly, a cloud agent that is similarly isolated will not be that effective. And so a huge part of making these agents useful and achieving some of these futures are on the intelligence side, certainly, but a lot of it is also just really tactical data access, cloud infrastructure and reliability pieces that feel much more prosaic than some of the broader intelligence questions but matter in some ways just as much for the end effectiveness.