Lenny's Podcast: Product | Career | Growth
00:28:09 · Dianne Penn
[00:28:09] S0: I think that's a lot of maybe the more day-to-day. I think one piece around researchers and research organizations like at Anthropic is there's also a vision of the future more broadly. So for example, things like, I think even at the founding of the company, researchers were talking about how do we get Claude to use a computer? How do we get AI to navigate a screen? So there's a lot of actually very founder-like energy is how I describe it within researchers are really bold and ambitious researchers, and we have a ton of those at Anthropic. So there's one layer of vision of what this technology can go. And then I think on this other side of the loop, there's also, now that this technology or Claude is in people's hands, how do we make it better today? So it's a medium and long-term, and a lot of energy thinking about that lens of the future. And also, in the immediate and short term, what are the improvement areas we can make? And so I think you're describing a really good sense of how do we make iterative improvements on different versions of Claude? The way that my team works with researchers is being very integrated and embedded in those loops, particularly areas where there's a lot of impact on users. So this is things like vision, computer use, coding, agentic coding, tool use, test time compute, things where there's a direct user impact. And then figuring out what are the ways to bring the user feedback, and ground it in a level that is understandable for researchers, and also actionable for researchers. And I think that's the second piece is actually a big part of the job and sometimes a hard part of the job. So for example, we might get feedback on claude.ai. "Claude hallucinated." It's very vague. If you bring that to a researcher and you say, "Please fix Claude from being hallucinated," it's not very actionable. And so part of the time of the team is understanding, okay, what's the trajectory of why that user gave that feedback? And it's consented. And so we look at, okay, what should Claude have called tools in that moment? Or from its current knowledge or it looked at the right document, but it looked at the wrong facts. In the first case, that would have been a failure on tool use.
00:30:54 · Dianne Penn, Lenny Rachitsky
On the second case, it would've been a failure on, let's say, search or knowledge insertion, search synthesis, or it could be something around alignment. And so bring that level of detail to researchers coming up with, is this a big enough problem? Figure out things like evals to then describe what we've improved it. Those are the levels of actionability, and it's the day-to-day language of their researchers. And so we try to stay very close to how to bring that in an actionable manner between users to the core model training and the research development loop. [00:31:36] S1: I was talking to someone the other day about how it feels like AI research is the place to be now if you want to be very successful in life. What does it take to become a really successful researcher from which you can tell? Not everyone's brain is going to work this way, but just say people are like, "Hey, I want to explore this career path." From what you've seen, what does it take to make it there? [00:31:59] S0: Researchers generally, or research and product managers working with research, or both? [00:32:04] S1: Let's do both. [00:32:04] S0: Yeah. [00:32:05] S1: But the researchers, PMs working with researchers also going to be very successful, but it feels like everyone's trying to poach all the top researchers across every company. So I know you're not an AI researcher, but just from what you've seen, just what does it take to make it in that career path?
00:32:05 · Lenny Rachitsky, Dianne Penn
[00:32:05] S1: But the researchers, PMs working with researchers also going to be very successful, but it feels like everyone's trying to poach all the top researchers across every company. So I know you're not an AI researcher, but just from what you've seen, just what does it take to make it in that career path? [00:32:22] S0: Yeah. I think a lot of the most successful researchers and research leadership at Anthropic are folks who are really strong first principles thinkers about problems. They reason through problems really well, who are just passionate about their research area and have a bold description of what that could look like. And then who are actually close to the details. And so our leadership, our chief scientists, our heads of fine-tuning and RL, folks are actually really close to the training runs and actually look at things like how the training run is going, evals, looking at the underlying data. So actually staying really close and be excited to be in the details, I think have been a sign of really strong researchers and developing taste. And I think another piece is just their ability to think big over time and be very ambitious. Like the Dario, like we can transform software engineering. And I think going in that direction, you learn so much. You have to shoot for the stars in many ways across your ideas, I think, in order to be a successful researcher.
00:32:22 · Dianne Penn, Lenny Rachitsky
[00:32:22] S0: Yeah. I think a lot of the most successful researchers and research leadership at Anthropic are folks who are really strong first principles thinkers about problems. They reason through problems really well, who are just passionate about their research area and have a bold description of what that could look like. And then who are actually close to the details. And so our leadership, our chief scientists, our heads of fine-tuning and RL, folks are actually really close to the training runs and actually look at things like how the training run is going, evals, looking at the underlying data. So actually staying really close and be excited to be in the details, I think have been a sign of really strong researchers and developing taste. And I think another piece is just their ability to think big over time and be very ambitious. Like the Dario, like we can transform software engineering. And I think going in that direction, you learn so much. You have to shoot for the stars in many ways across your ideas, I think, in order to be a successful researcher. [00:33:49] S1: I love just this meme of just be more ambitious comes up so often now, which is so hard. It's easy to say that it's hard to actually just like how big can you think, and how that's so much of what AI now unlocks, just be more ambitious. [00:34:01] S0: Yeah. [00:34:02] S1: Yeah.
00:34:02 · Lenny Rachitsky, Dianne Penn
[00:34:02] S1: Yeah. [00:34:03] S0: I think it's thinking through it once or twice end-to-end. And then being, I think, stubborn about the area and maybe more loose around the exact approach. It is a question we challenge ourselves with, but the technology is moving so quickly, and so how do you make sure what you're building is actually forward compatible? And so it's also actually part of, I think, the core product development loop to think bigger. One thing I ask the team frequently or how I think about when we're building a product is let's say Claude 8 comes around. What changes in what users do? And then what does that mean for how you're building today? Is it going to be forward compatible to that experience? So just grounding, I think being ambitious is very broad. And so trying to ground it in some ways of describing that.
00:35:13 · Lenny Rachitsky
[00:35:13] S1: And also, yeah, everything heading in a direction that all is cohesive and makes sense versus just ambitious in a completely different direction. Speaking of ambition and Claude 8, Fable/Mythos recently feels like hit this very new tipping point with models where it used to be you have an awesome model, release it. Hey everyone, welcome. Opus 4.5 is out. Everyone can use it. Mythos went in a very different direction. We got blocked. There was a lot of scrutiny, a lot of concern about what it was capable of. All the companies had to go make sure it wasn't going to hack into all their systems. And it feels like now every model, because they continue to get better, will now have a lot more scrutiny and there'll be more restrictions on who can use them, which feels like a big deal. How do you think about that? How does that change the way you operate?