Lenny's Podcast: Product | Career | Growth
01:03:03 · Cat Wu
[01:03:03] S0: Yeah. We can remove a lot of prompting interventions every time the model gets smarter. And we actually do this every time we launch a model, we read through the entire system prompt and we reflect on, okay, for each of these sections, does the model really need this reminder anymore, and if not, we'll remove it. The most exciting thing that new models unlocks though is just entirely new features. So there's a lot of features that we've been testing out with prior models and the accuracy wasn't high enough for us to want to launch them, and so one example of this is code review. We tried to build a code review product a few times and we've launched simpler versions of code review, which is the /codereview command in the past, and it was only with the most recent models that we felt like, okay, this code review is so good that our engineering team relies on this code review to pass before we merge PRs. And we found that this was... We've always dreamed of Claude being able to be a reliable code reviewer that we can confidently feel catches the majority of bugs, and it was only with Opus 4.5 and 4.6, and Sonnet 4.6 that we felt like, okay, we are now able to run multiple code review agents simultaneously to traverse the entirety of the code base and to synthesize a set of real issues that an engineer needs to address before merge. And so this is a new capability that the newest models have unlocked.
01:04:39 · Lenny Rachitsky, Cat Wu
[01:04:39] S1: This is another trend that is very common on this podcast of build something that will possibly be possible in the next six months, be kind of at the edge of what's working sort of, and then it'll catch up and then it'll be an amazing product and you'll be ahead of everyone. [01:04:52] S0: Yeah, exactly. It's pretty important to build products that don't necessarily work yet so that you know, okay, what is missing for this product to work? And then with the newest model, you can just swap it into the prototype you've already made and see, okay, does this new model close that gap? [01:05:12] S1: How much are you able to speak to just where things are going with Claude and Cowork as the vision of it? I imagine you don't want to give away too much about the goal, but it feels like there's all these awesome features being added on top, dispatch, control from phone and all these mobile app, all these things. What's kind of just a way to understand the vision for all these things long-term?
01:05:32 · Cat Wu
[01:05:32] S0: We think about this in terms of building blocks. So for both Claude Code and Cowork, the core building block is making individual tasks successful. So you wanted to produce some output, you give it a prompt description. Is it able to consistently produce acceptable output that you're able to either merge or share with your colleagues or external audience? So the task is the core building block. As the models get smarter, the task access rate gets a lot higher, and then we see people moving towards doing multiple tasks at the same time. So multi-Claudeing was this big thing towards the end of 2025, and it's only increased since then. And so we see this as, okay, great. One task works, and now you can do six tasks at a time. As the models get even smarter, the way that we are extrapolating this is, okay, next, maybe you're going to run 50 Claudes at a time or hundreds of Claudes at a time, and so what is the infrastructure we need to build to enable that? At that point, you're probably not going to run everything locally on your machine anymore. There's just not enough RAM to do it. And so we're thinking about how do we make it easier for you to manage all these? These will probably run remotely. How do we build the interface so that you as a human know which tasks you need to look into? How do we make sure that the agent is fully verifying its work so that when you look at a task and it says it's done, you can very quickly verify and fully trust that it is done to your spec. And how do we make sure that this process is self-improving so that when you do see a task that isn't done to your liking, you can give it feedback and the model will know for every future run to incorporate that feedback so it never makes that mistake again? So this is the progression that we're bringing our users along for.
01:07:23 · Lenny Rachitsky
[01:07:23] S1: There's a lot of people listening, a lot of product managers, a lot of maybe founders, a lot of other cross-functional folks listening. There's a lot of worry about just how their role... Just the future of their careers. What advice would you have for just people to not just survive this transition to this very AI-driven world, but to be really successful to essentially just to thrive in this future? What are just things people need to hear, need to be doing?
01:07:49 · Cat Wu
[01:07:49] S0: I think AI gives everybody a ton more leverage than they used to, and so I would push you towards anytime you realize that you're doing some manual task multiple times, think about how you can use Claude Code, Cowork, or other AI tools to automate that for you. Most people have creative parts of their job that they absolutely love and then tedious parts of their job that they really hate doing. I think the beauty of AI is that it can do those tedious parts for you. It can learn from every time that you've done that manual task and generalize and then run it automatically and so that you can focus on the creative parts and that means you can do a lot more than you used to be able to do. So I think my immediate push for people is figure out the repetitive parts that you can pass to Claude, iterate on those automations until the success rate is very high, and then focus on, okay, what more can you be doing for your team, for your product, for your company that people haven't had the bandwidth to pick up so far, or what is that pet project that you always thought the company should do that you've never had bandwidth to do? If AI can take care of the grunt work, then you have this extra 20% time now that you might not have before. So my push is to lean into these tools, hand off the work that you're not excited to do, figure out how it can accelerate you, and then as a result, you'll be able to do so much more.
01:09:19 · Lenny Rachitsky, Cat Wu
[01:09:19] S1: Something core to what you just shared, which I fully agree with is find problems to solve with AI. There's all this potential what all these tools can do. For a lot of people, the hardest part is just like, "What should I actually do?" And what you're saying here is just pay attention to things that you are doing constantly you can automate, pay attention to just ideas that have been floating around that you haven't had time to do. It's basically it's like solve a problem for yourself is kind of the core advice there. [01:09:46] S0: Exactly. I would also push listeners towards focusing on bringing your automations from, okay, this is a cool concept to like, hey, this actually works 100% of the time. Sometimes I see users trying to automate something, getting it to 90, 95% accuracy and then giving up on it, and if an automation doesn't work 100% of the time, it's not really an automation, and that last five to 10% does take more time. Also, building the automation is often a lot slower than you doing it yourself. I would encourage listeners to put in that time to scope some automation that you really want to get to 100%, put in the elbow grease to teach Claude your preferences, to give it feedback so that it can improve its skill so that it can get to that 100%, and then really then you'll be able to rely on it. There's just not much value in a 95% there automation. [01:10:48] S1: I am super guilty of that. This is really good advice for me.