The Skip Podcast
00:00:00 · Dianne Penn
[00:00:00] S0: The first two months here should feel like some of the hardest of your career. That actually means you're doing it right because of the velocity and pace. What I've seen being successful are those who really try to rise to the challenge. And I try to give actually really hard initial ramp ups. For folks who really then ramp up quickly. They kind of get onto the acceleration. And for folks who are like, this is really hard. I have these reasons why I couldn't do X, Y, and Z. That's who ends up struggling more.
00:00:36 · Nikhyl Singhal
[00:00:36] S1: Hi everyone. Welcome to another episode of The Skip Podcast. I had the privilege of having a conversation with Dianne Penn, who's part of our group. And one of the early product managers at Anthropic actually helped build the research function. And it was a really insightful discussion on where product management's going and how does she maintain things like training product managers? What does the starter project look like when people join? What are the qualifications that you need to have? How early in career people that actually don't have a lot of product management experience are actually advantaged to those that are mid career and late career. How sometimes knowing too much, knowing how to navigate structure isn't as effective as knowing how to navigate your opinion, being stubborn and ultimately how to find joy in the pace that we're in right now. So anyway, there is a ton of great insight. I hope you enjoy the conversation as much as I enjoyed having it. We'll get to it right afterward from our wonderful sponsors. This episode is brought to you by Chargebee, the billing and monetization infrastructure for modern usage native products. The best product people have always known pricing is a growth lever. In the AI era, it's even more true. LLM costs are breaking the old economics of software and teams are shipping faster than ever. So your pricing has to move as fast as your product does and reliably. That takes a billing system built for it. Say you want to charge for active agent minutes or cap what an individual user can spend or experiment with seat types that scale with usage. Chargebee does all of that and more, metering, prepaid credits, entitlements, and the flexibility you need to scale. Companies like Code Rabbit, Lambda, Zapier, and DeepL are already building new business models on Chargebee. Learn more at chargebee.com. That's chargebee.com. Hi everyone. Welcome to another episode of The Skip Podcast. I'm delighted to be with my friend and member of our community, Dianne Penn. Dianne works at Anthropic, and I cannot wait for all of you to hear from her, her wisdom, her experience. She's probably the longest tenure product manager at Anthropic or close to it.
00:02:47 · Nikhyl Singhal
Dianne, you and I intersected each other at our former company, and so we've gotten a chance to work closely with one another, but I think I always love when I look at your background and how different it is and how many different types of experiences and how unknown Anthropic was to some extent when you joined. Maybe take the first couple of minutes, introduce yourself and tell us a little bit about how you got to where you are, but what were those transitions early in career?
00:03:16 · Dianne Penn
[00:03:16] S0: Thanks again, Nikhyl for having me. I'm Dianne. I lead our product team focused around research and labs here at Anthropic. This means one, working as product managers for our core foundation models. So everything from Claude two to now, the Claude 5.5 family that we just did our latest release, on Wednesday, to new model capabilities. So things like tool use, computer use, vision, reasoning, as well as on the lab side. This included initial incubations and, scaling of products like Claude Code, MCP Skills, and Claude Design. I think, Nikhyl, on your question around career, I definitely feel like I've had like a eclectic or pretty diverse set of experiences. I actually started out working as a bond trader in New York. Then started working in product management, at Credit Karma. First actually on the like strategy side before doing more product work. Then, as a PM on the Alexa AI team focused around model development. And that was how I really fell in love with AI and now working at Anthropic. I think something that really is kind of non obvious in a lot of those opportunities and roles is that I really just actually love figuring out the learning more than almost applying the specific like learning or field sometimes. And so I feel like there's people who really just like are really good at applying the same skill set over, over and over. And what I found was when I was in trading, the first like one to two years, like I got to like learn a lot about myself, how decisions get made, how to like stick with decisions, even if they don't work out perfectly. But it didn't feel really after a while, like rewarding in the sense of like, how was I contributing back? How was I making like new things like that are more creative or impactful? And so like, that's kind of how I found myself working in product management. It very much feels like there is users who have potentially problems or opportunities. We don't know how to frame that. And like initially, that's what attracted me to strategy. And in the strategy world, the hard part was actually, how do you actually make a strategy work well? And I think that was like one of the big learnings for me at both Credit Karma and Amazon and Anthropic, that the strategy is really important.
00:05:51 · Dianne Penn
We do need to have like a path of what we're trying to get to, but it's so much the execution and the details and the learning. And so in particular, I feel like between Credit Karma, Amazon, and Anthropic, like honing in on the like execution, the operational excellence, thinking about the every last detail has actually been one of the big things that I feel like has been both hard sometimes because there's so many details, like more details all the time, especially when you're building like larger systems and products, but have been really fun because the users are actually changing and are so different. I think the way that I kind of actually ended up, joining Amazon and Alexa was I feel like there was an era when folks talked about software was eating the world. And at Credit Karma, as you know, like the recommendations systems were very much becoming so core to how a user really interacted with their financial progress, their financial health. And it was very hard at that time for, I think most companies to actually be able to do foundational research or like R and D in terms of techniques. And I had a friend serendipitously reach out about this like first role as a PM with this, within this large, like, AI org at Alexa. And it didn't really feel obvious because Credit Karma was growing. We were just getting, I think, acquired by Intuit. But I just felt like that's opportunities to be like the first person working on this area and really reaccelerating my learning didn't come around very often. And even though it was... It felt like at the time, you know, LLMs wasn't even a term, we were starting to explore that this technology of like transformers and how you might replace like classic, like classifier systems for like natural language processing. And I think I just kind of fell in love with the root of the problem and the fact that like, it didn't mean we had the final solution or the end state for users and that this solution space was growing. And so I really felt like at the time it didn't feel obvious to go to Alexa, but looking back, I felt like that was one of those decisions that really helped my career. And then ultimately how I found myself in Anthropic as well.
00:08:22 · Nikhyl Singhal
[00:08:22] S1: I think that's really insightful. I think one thing that I think we all can agree on is that often careers make sense only in the rearview mirror. So like we look back and we're like, oh, let me tell you exactly what happened. And then I, we, I did this thing and it failed and it was so helpful, but at the time, you know, these are like earth crashing kind of decisions. I think what you're saying is you look for huge surface areas with high degrees of ambiguity, where the problem was sort of 1% solved versus say 95 solved. And yet those companies and opportunities often are theoretically high risk, not well known. And your point is, Hey, that's what kind of attracted me because as I was cracking the code, we were always solving new problems. And then the second thing I'm hearing is the detail point. It's like, well, the way to solve that wasn't just high level. It was a combination of being in the detail and being very strategic. So knowing the strategy, knowing there was a lot of directions you could go in and that you had to play a hands on role that felt like this combination of skill. Is that, is that a fair characterization?