The Skip Podcast
00:09:31 · Dianne Penn
[00:09:31] S0: I think you're exactly right. Like in hindsight, it seems like all the different pieces hang together in the moment. I don't feel like I had a grand plan of this must be the way my career has to like work out. I think I very much try to look for what are things that really interest me if I had more time to look into. And then in particular, in the case of like Alexa and working on the AI team, I actually had friends who proactively were like, why would you leave Credit Karma? It's a startup. It was just getting acquired. You guys are going to grow to go to a large company. I actually ended up going almost like against some of my friend's recommendations because I felt like I personally wanted to learn how R and D, AI R and D worked. And so it was very much following kind of my own curiosity. And then I think on the second point around being in the details, this is something I think has been like really humbling to learn throughout my career, because it was kind of going from strategy into product management. And sometimes it feels like if you have the right strategy, you're going to have just exactly the right outcomes. And it just really made me appreciate back when, you know, we were working together, how hard every product decision actually was, not just the the initial plan, but the actual details to get to something valuable. And that is like 99% of what makes a great strategy actually work, that the details got worked out correctly.
00:11:03 · Nikhyl Singhal
[00:11:03] S1: Well, one thing that I think it just may... Just a quick pause, mean, I think there are a lot of people that are listening to this that do have roles that are more strategy oriented. And I would say Credit Karma had, as a company, unusually strong strategy, both personnel as well as there's a lot of thoughtful areas because there was a lot of surface area. How are you going to take on, you know, the auto industry or mortgages and other areas? I think the point that you're making though is very, very helpful. When you're a strategist today and you have strength and there's obviously huge IQ and opportunities to understand the market. But your point was that when you turned it into the execution and coupled it with that strategy, that's when you knew that it's more than just a plan. That's when the real learning took place. And that seemed like a big career unlock that frankly, lot of strategists don't do or not capable of doing. So one, it's an element of skill, but also it's an element of orientation, not to build bigger strategies at bigger companies, but actually go into the detail. And I think that's what I'm hearing as this sort of suggestion or career guidance for strategists.
00:12:11 · Dianne Penn
[00:12:11] S0: I think that's right for me personally. I think it's very much what is like the potentially the goal or what gives you your center of joy. And for me, like I always felt like when I was focusing more on strategy, there was something missing in terms of why I couldn't get to the right outcome necessarily. Or like, why doesn't the initial perfect plan work out perfectly? Right? Like why does the initial product pitch work? And what I have learned, I think both at Amazon and also now at Anthropic is that the 99% is after the write the initial plan. And it is just being in the details, figuring out every user flow, figuring out what people's feedback are from the model side, understanding, reading like model training transcripts, getting closer to details on user feedback. Like it's all of the other pieces that actually makes something successful, not just only the initial idea, which is important. But I think it just depends on kind of what is the impact you want to have on the world and like, what is your center of joy?
00:13:17 · Nikhyl Singhal
[00:13:17] S1: So what strategy look like in your day job today? You know, when you and I worked together, we were talking about, Hey, these are the three year, five year plans because we have this data. We want to be able to help users that have this challenge. You know, it was pretty, pretty long term in orientation. And these were industries that had existed for like a hundred years. You're describing now, you know, an industry that shifts every six weeks. You know, you could find yourself in hyper reaction mode because you're getting all the feedback you mentioned and the research in and of itself is driving some of the product plans. How have you remained strategic or is the strategy to be nimble and to be I'm curious as to how those two skills kind of come together at a company like Anthropic.
00:14:04 · Dianne Penn
[00:14:04] S0: One thing that has been helpful is because our research org is also very medium to long term thinking. And so there's almost like a helpful pull to not just be in the short term of like X, Y, and Z got shipped. We must do the same thing because like the research and like the company like mission and North Star is like very more long term and forward thinking. The way that it manifests, I think in how my team plans, how like we collaborate across like our roadmaps on the model or on the lab side is there are specific, like we do more almost monthly planning on just like a here's what we want to accomplish lens, but that's in the context of some high level assumptions or perspectives about the state of the world in like a six month to twelve month timeframe. What I mean is we don't spend, let's say three months doing an annual plan that goes out of date within like two weeks. We tried that for a little bit in the early years of we must do a full OKR process. Everybody must review the same OKRs, but then the OKRs quickly got like thrown out the window when our assumptions about usage or adoption were just completely underestimates. Right? And so what we ended up or the way that, like, I like to have on my team is what are some key assumptions in three to six months? And then what is a one month plan of how we accomplish continuing to navigate towards that while still being agile to the immediate dynamics? So what this means is right now we're thinking about things like life sciences. I think we are like Dario, etcetera, have talked a lot about like how this technology could be really beneficial for helping companies, life science organizations with curing diseases. That's not like a short term thing at all. But at the same time, we need to translate that into, okay, what are training or product investments or bets that we want to experiment with on a monthly basis? How do we make sure things like scientists and researchers get access to the models? How do we respond to initial dynamics in the market while trying to still make progress on that medium to long term?
00:16:30 · Nikhyl Singhal
[00:16:30] S1: You know, your team, for example, was the team that birthed, you know, Claude Code. And I think that, you know, when I look back at the sort of last year, you know, things like Claude Code, Claude Design were products and maybe arguably bets using your term that you decided to invest in. But then there may be product line areas like say Muse Open Claw, where there is less perhaps proactive work being done, at least from the visible sense. I'm just curious, again, going back to this planning, like how does both the research organization and the company decide what ends up maturing into something that is like a product, like say today's dash or yesterday's announcement around the dashboard products. Like how do you get something that's more capital P versus something that's a behavior and an area that one could build, but isn't necessarily a product that service. You mentioned life sciences might be there in twelve months or six months. How does that type of decisioning get made?