Why the people building AI can’t tell you what’s next | Dianne Penn (Anthropic)

1. 0:00 – 2:31

   Introduction

   1. DPDianne Penn0:00

      In 2023 when I started, nobody said Anthropic and Claude and coding in the same sentence.

   2. LRLenny Rachitsky0:06

      I wanna go back to the beginning of Anthropic. I remember feeling, "Man, these guys have no chance. OpenAI is so far ahead."

   3. DPDianne Penn0:14

      At the time, I saw people were starting to use these models not just for code auto-complete, but actually writing long-form code, and saw an opportunity for us to train Opus 3 to be better at. That was the inflection.

   4. LRLenny Rachitsky0:26

      I always think about Opus 4.5 a year later during winter break when everyone was home, able to code.

   5. DPDianne Penn0:31

      What was magical about Opus 4.5 is we also now not just had a model, but a vehicle, a great product experience like Claude Code. Opus 4.5 wouldn't have had that moment without a product like Claude Code, and Claude Code wouldn't have had that type of adoption accelerated without Opus 4.5.

   6. LRLenny Rachitsky0:50

      I wanna talk about how the product role is changing.

   7. DPDianne Penn0:53

      For my team, the way to drive user value is to figure out the right user feedback, the evals. We actually have a saying on the team of, "Evals are the new PRDs."

   8. LRLenny Rachitsky1:02

      Something Garry Tan's been talking about, if you're willing to spend $100,000 a year right now on tokens, you are living the way somebody in 2028 is gonna live.

   9. DPDianne Penn1:10

      You have to sweat the tokens as much as you sweat the pixels. You have to be using the models to come up with good then great then better ideas, and there's no substitute for that.

   10. LRLenny Rachitsky1:20

       People need to be more ambitious with AI tools these days because they're just capable of so much.

   11. DPDianne Penn1:25

       One thing I ask the team is, let's say Claude 8 comes around, what changes in what users do? What does that mean for how you're building today?

   12. LRLenny Rachitsky1:36

       Today my guest is Dianne Penn, head of product for the AI Research and Labs teams at Anthropic. She joined Anthropic as the first technical product manager over three years ago, which is a lifetime in AI time, when the product team was just five engineers. She's helped ship every model at Anthropic from Claude 2 through Fable. She's also helped incubate and launch Claude Code, MCP, Skills, Claude Design, and also core capabilities like computer use, tool use, and reasoning. It is always such a treat and so mind-expanding to get to talk to someone who's at the very center of AI and product management. It's hard to imagine someone who has seen more of where things are going than the head of product for Anthropic's Research and Labs teams. Before we get into it, don't forget to check out lennysproductpass.com for a year free of the hottest and most beautifully crafted AI products in the world, available exclusively to Lenny's newsletter subscribers.

2. 2:31 – 8:55

   Early Anthropic days

   1. LRLenny Rachitsky2:31

      With that, I bring you Dianne Penn. [instrumental music] Dianne, thank you so much for being here, and welcome to the podcast.

   2. DPDianne Penn2:40

      Thank you, Lenny. It's so nice to see you again.

   3. LRLenny Rachitsky2:43

      I wanna go back to the beginning of Anthropic, uh, the early days. I remember when Anthropic first launched. This was, I don't know, years... The first model when it launched. Years ago, three years ago, something like that.

   4. DPDianne Penn2:55

      It was.

   5. LRLenny Rachitsky2:56

      Three years. I remember just, like, feeling that, "Man, these guys have no chance. OpenAI is so far ahead." Every... Just like, "How... What are they thinking? How is this possible? OpenAI has won. It's too late." Uh, things are very different now. The latest number I saw was Anthropic was making, like, I don't know, $50 billion in ARR. That's, like, what companies used to go public at. [laughs] Like, very successful companies went public at 50 billion in valuation. Anthropic reportedly is making that every single year. You joined as one of the earliest PMs. There were something like five engineers when you joined. The model hasn't, hadn't even launched when you joined. What was it like in those early days of Anthropic? What's something that might surprise people about what it was like a- at the beginning?

   6. DPDianne Penn3:44

      I think a big part of what's made Anthropic today actually has been very much the core of even the early days. So I joined in 2023. Like you said, we had five product engineers. There was one engineer for the entirety of our API business [laughs] if you, if you believe. Um, and I think a big portion of it was the culture was really strong, and I think this is something I emphasize for folks who are interested in the company, um, really do walk the walk of, um, the mission and the culture and the values. Um, and the energy was very much like a startup, and I think you're right. We were very much trying to find our identity in the early years. Like, I think there's one piece around the technology, but how does that technology bring value to users, bring value to society, and what could it possibly be? And I think the early years were us exploring that in different ways. Like, we did start with, like, Claude.ai, like, another chatbot chat assistant, and evolving into things like tool use. Um, I think one of the moments where really we started to get into our groove was shipping things like Golden Gate Claude. I don't know if you, like, remember that.

   7. LRLenny Rachitsky5:01

      No.

   8. DPDianne Penn5:02

      Um, so this, this was actually up for about 24 hours or so. Uh, we had just published one of our, um, early interpretability research in early 2024, and one of the examples was essentially you could have what's called, like, features of the model within the layers which, uh, express certain types of, uh, thematics. So one of the, one of the themes that the researchers was able to identify was, uh, let's say bullet point writing. Another one was people and places, and one that really came up frequently that, uh, resonated was- The Golden Gate Bridge. And so when you actually, uh, essentially dialed up that feature, Claude would obsess about the Golden Gate Bridge. So meaning in every one of its responses, it would come back and talk about the Golden Gate Bridge. So if you said like, "Give me a recipe for making spaghetti," uh, it would say, "Here is a recipe, and the orange color is just like International Red that the Golden Bridge, Golden Gate Bridge looked like." Um, and so it was, like, really quirky, and we, we, we very much wanted to, in that situation, just bring that user, uh, bring, bring it to the masses and bring it to people who were starting to use Claude. And, uh, so the entire, uh, experience, actually, we spun up on our claude.ai website within 24 hours, and that took, like, engineering, product, design, uh, our, like, research teams all working together, and we were really, really proud of it. I think it maybe reached only 2,000 people, [laughs] to be honest. Uh, but it, it made us feel like, oh, we can actually bring new user experiences, showcase our research in a way that's different and authentic to us, and in a very startup-y like pace. Th-that, to me, was like one of those, like, maybe hidden inflection points of we were starting to find i- our identity, that we could build products, build experiences that were different from what our competitors had seen, what was already out there. [lip smack] I think that obviously Labs, Claude Code, et cetera, like we then started to identify ourselves as would we actually think the world, uh, how to think about AI, how to bring that closer to the public. Um, but it was a very bottoms-up culture, and so that entire experience was very bottoms up. I see engineers, I see, uh, designers donating time to work on. Um, and so I, I like to always use that as an example of like what the da- early days were like. But the culture and, and, and the values have very much, I think, stayed the same sin- since those early days.

   9. LRLenny Rachitsky7:46

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3. 8:55 – 13:50

   Big milestones

   1. LRLenny Rachitsky8:55

      today. What are some of the other, um, big inflection moments as you think about just Anthropic going from just this like lab that's trying to compete with this juggernaut of OpenAI at that point to what it is today? What are some moments that stick out of like, wow, that really changed things?

   2. DPDianne Penn9:11

      Definitely when we were training and, uh, testing, uh, Opus 3. I think that was the moment when the company, I think we were less than 200 people still at that point, and it was very clear that we needed and wanted to create a frontier model, and a, uh, that was very important in terms of like our ability to reach like users, consumers, and, uh, to showcase our research. And we were looking for ways for also why should somebody choose Claude, and that was like a core question, and that was a core question we were getting asked in the early days. And I think with Opus 3, you know, it launched, I think, early March 2024, but there was many, many months of various teams across inference, across research, fine-tuning, pre-training, that rallied at different points and towards a common goal. And, uh, I think everybody that was involved was like really proud. I remember, uh, being the PM, us, uh, the research leads, myself, we were all in our, um... This was around December, so we were all at home in our various, uh, um, parents' homes and seeing everybody's background of like their childhood room. And everybody just working really hard, uh, to figure out the like what are we training the model for? Is it showing up the right way? So I think that was really powerful in terms of just building a lot of trust. And a lot of our research leads have actually, uh, from that time, are now like leading reinforcement learning, leading our character work, alignment work. So th- that foundational trust, I think, also helped us work well now with any of our production models across product and research, because we were working just so much in the trenches together in the early days. And then I think there were things like identifying that coding was important, right? In 2023, when I started, um, nobody said Anthropic and Claude and coding in the same sentence. I think competitor models like GPT-4 at the time was used a bit for coding, but it was one of many use cases. And one thing that, for example, I saw was people were starting to use code, uh, these models not just for code, not just like code autocomplete, but actually writing long-form code. And is that an opportunity for us to train, you know, Opus 3 to be better at? And it ended up being a relatively- Smaller change from a training perspective, but it ended up helping us differentiate in the early days, uh, competitively for users and actually bring a lot of the very early Claude enthusiasts and developers because we were, uh, providing a value that they didn't really think was possible at the time.

   3. LRLenny Rachitsky12:10

      It's so interesting you talk about Opus 3, like, uh, that's so long ago, and just, like, it's hard to think that was a big inflection. And so this is really interesting to hear that that was internally a big milestone. It almost feels like this confidence y'all built that, wow, we could really ship a frontier model, which is now today so not great [chuckles] if you compare it to what we've got today. What I always think about is Opus 4.5, which was, and interestingly, like a year later, also during winter break when everyone was home able to code. Uh, was that another big milestone?

   4. DPDianne Penn12:38

      Yeah. Um, Opus 4.5 was definitely another large moment. I think what was magic about- magical about Opus 4.5 is we also now not just had a model, but a vehicle which is, like, a great product experience like Claude Code. Um, one thing we say a lot on the team is you need frontier products in order to have frontier models and for people to feel the magic of frontier models. And I think, you know, we felt the magic of Claude Code for very, uh, for, for, uh, for many months before that. Uh, but the fact that the model essentially got to a level of intelligence where at a very broad level, users can experience both frontier intelligence in new use cases, allow it to run things end-to-end in an agentic manner. I think that was the inflection. It was actually both. I, I think Opus 4.5 wouldn't have had that moment without a product like Claude Code, and Claude Code, I think, wouldn't have had that type of adoption accelerated without Opus 4.5.

4. 13:50 – 20:02

   Inside the exponential

   1. LRLenny Rachitsky13:50

      So kind of speaking on, on this, on this thread, uh, Dario, interestingly, if you look back at all his predictions, he's just like, "Okay, coding's gonna be solved. It'll be 100% in, like, a year," something like that. He kept talking about how we're gonna do co- like, AI's gonna do all our code. And I remember everyone, uh, being like, "There's no way. This is way too complicated. How is, how is AI ever gonna get really good at this very complex thing that humans do? No, this is gonna be humans for a long time." He was completely right. Something else that he talks a lot about is this exponential that we're, now we're, now that we're on. That's the way he describes it now. We're like, we're on the exponential curve. I remember not long ago we were, new models were being released and everybody was like, "Okay, we're done. There's no more upside. It's plateauing. It's over. There's no more room to grow." Uh, and now it's like the opposite. Now we're inside. Like, if you think about the curve of the exponential, we're like inside of the exponential now, which by definition means every improvement is m- a massive jump because we're, like, on that hockey stick part. What's it like just being on the inside of this crazy historic moment when AI is improving so fast, so much is being unlocked? Uh, what is it like, and how should people prepare for the coming acceleration of more and more improvement from AI?

   2. DPDianne Penn15:05

      One thing I like to say on the team is most of us weren't, like, actively working yet when the internet transitioned from this novelty to something that everyone can use. And it feels like that's just taking humans, uh, I think analogies are helpful. And so, like, the analogy of that is, I think, a couple of things. Um, number one is adaptability becomes very important. Um, I think we, we have evals, we have, you know, on the safety side, safety testing, red teaming on the capabilities and product side, new prototypes, products like Claude Code, Tag, and others. But it's very hard to predict the exact moment or the exact model, and so the adaptability of when you're faced with new information, how do you then make better decisions versus keeping the same plan? And so, like, that agility is really important. I think another piece is with that, how do you actually be thinking very first principles and reason through what's next? What's the so what? How do we invest in new products? How do we invest in explaining the differences to users? So a lot of the, a lot of the experiences, I think, of being in that exponential is that pace, understanding how you operate and make better decisions, and then applying that first principles thinking to then do something that maybe we pull up a plan that, uh, we would ex- were expecting a few months from now, but now the model can actually do, uh, and work on and actually bring that to users. So this is things like Cowork, Skills, Tag. You know, as the, the, it's a very positive self-enforcing loop, and I, I, I think a big part of it also is just having the, like, trust in each other, like making sure we have, like, we're, we're thinking through the right decision-making. We're bringing folks along. Some teams might see the exponential, feel it faster than others, so how do we kind of have the grace to bring the organization, the growing organization and company along on that?

   3. LRLenny Rachitsky17:22

      So what I'm hearing here is you almost don't know what will be possible with every model release. And so the important things to focus on is being adaptable as things emerge. Uh, to your point, the product itself has to stay up to, has to-

   4. DPDianne Penn17:38

      Yeah

   5. LRLenny Rachitsky17:38

      ... catch up to what is possible. To your point-

   6. DPDianne Penn17:40

      Yeah

   7. LRLenny Rachitsky17:40

      ... again, just like it can do so much, but people may not understand how to do it and may not be able to do it, so the product making it easy and even just, like, telling you, "Here's something you could do," feels like an important part. Is that roughly what you're describing?

   8. DPDianne Penn17:52

      I, I think so. I think, um, there's some really interesting graphs in the original scaling law papers, and I think- Folks are very familiar with the scaling loss in, in the lens of, um, as you add in more compute and data, what's called loss, AKA the loss from next token prediction, uh, goes down. And so it's a very smooth linear curve of, like, the models get more intelligent as you scale them up. What's actually also interesting, uh, in that paper is there are these, like, very, uh, different emerging capability graphs. And so for example, uh, as you add in more data and you train the models with more compute, you essentially see these actually discontinuous emerging capabilities jump. So the models go from one plus one being a thing that it can't ca-calculate, to a thing that it could reliably calculate. And so these emerging capabilities, this, like, some nature of, like, predictability is, is, is not necessarily everyone knows the exact moment. Like, you need the evals to be able to assess that, has actually always been a part of, uh, how this technology works, and also what makes, like, things like safety harder. Because unless you have the evals, unless you have the systems to test, um, these jumps might actually happen and y- y- you don't know.

   9. LRLenny Rachitsky19:15

      Hmm. That's so interesting that you may have developed this, like, AI brain that, uh, can do something you're not even aware of. And so part of the job is just uncovering, wow, it just got really good at this thing. What can we do with that?

   10. DPDianne Penn19:27

       I think there's, like, product overhang and user overhang, like, to, to maybe put it in our, um, PM language, even on today's models. And I think there's, like, a lot that, uh, we could be exploring on, like, our current opuses, and definitely with, like, Fable, for example. And that, that discovery is actually another part of what's been in the early days of Anthropic's DNA, and I think is also continuing to be a big part of how we operate in product, in labs, in, and across

5. 20:02 – 23:30

   Token maxing

   1. DPDianne Penn20:02

      research.

   2. LRLenny Rachitsky20:03

      This makes me think about something Garry Tan's been talking about, uh, president of YC, I don't know what his title is. Uh, he's, he had this interesting point that if you're willing to spend $100,000 a year right now in tokens, you are living the way somebody in 2028 is gonna live, because by then it'll be really cheap, everyone can work this way. But if you, there's this alpha opportunity right now to just live in the future, go crazy on token spend. Uh, and so there's a big opportunity for people to learn what the future's like and also just build much faster. Thoughts on this idea and the value of token maxing, let's call it.

   3. DPDianne Penn20:37

      Yeah. I think I, I take more of, like, a almost product lens. It's almost like token spend is more the input, and really the output is what you described of experimentation. And I think if we were orienting, like, goals around experimentation, I feel like that, that might be the better framing of the outcomes, and therefore there might be different ways of achieving that outcome. I will say internally, some of the most creative thinkers, the best, like, prototypers, do spend a lot of time with Claude, with every new version of a research model that we have. And so there is something around you have to be, like, using the models to then come up with good, then great, then better ideas, and there's no substitute for that. Um, it's very hard to come up with a perfect strategy without touching the technology when it's moving this quickly. At the same time, I think there's other things that we could be doing. Like, so one thing that we, um, do a lot is actually working in public at An- internally within Anthropic. And so in the early days when we had less product surfaces, there was a Slack channel where everyone, almost the entire company, was testing early versions of Claude and trying different use cases. Like, people were not calling them use cases, but you might be asking it to edit an essay or, uh, to come up with the right way to send this email. Like, they were all different use cases, but we all worked in public, and then what you would see magically is different users or different, different folks on the team coming up with an idea, and then other people trying different variations of that idea. And then within maybe 10 or so requests, there was something magical or potentially a new use case that emerges. And I think there's a lot in not just individuals figuring out by themselves how to use this technology. I think we could be doing more to actually bring, like, that communal discovery, uh, when we do experimentation. Like, experimentation is not always necessarily a individual sport.

   4. LRLenny Rachitsky22:55

      It's so interesting. Yeah, this idea [laughs] that we're just, we're not sure what this is capable of or what we could do with it, and it takes all this poking around and people trying things, hearing what other people are trying to figure out what's possible. Such an interesting, uh, I don't know, technology. We're just like, "Okay, here's what... Oh, I figured out it could do this thing. What are you gonna do with that?"

   5. DPDianne Penn23:13

      I think at a broad theme we know, right? We know that the models can write great essays or can write long-form writing, but individual pain points of what can you actually solve with that and bring it to, like, a user level that people can use, um, I think is something that is more exploration

6. 23:30 – 27:30

   Anthropic Labs and the incubation model

   1. DPDianne Penn23:30

      or experimentation, uh, based.

   2. LRLenny Rachitsky23:33

      So following this thread, you, uh, you oversee product for the Labs team, which, uh, is extremely cool. We've had Ben Mann on the podcast, Mike Krieger, who both work on Labs now. Talk about Labs. What is Labs? What's come out of Labs? Many people have heard of these things. And w- how do they work that enables them to create such innovative ideas outside of even the core Anthropic product team?

   3. DPDianne Penn23:58

      The thesis of Labs in many ways is identifying and pulling the thread on the thread of Discontinuous large bets that might not be in the core roadmap, and figuring out is there a there there, and also what is a 10X, 100X, 1,000X of the there there. And so for example, uh, things like Claude Code, um, I think-

   4. LRLenny Rachitsky24:26

      I've heard of it

   5. DPDianne Penn24:27

      [laughs] Uh, things like Claude Code, uh, things like, uh, Skills, and most recently, Claude Design, MCP. The thing that we really try to emphasize within the teams is, especially right now, there are so many things that could be built. What does it mean then to have a discontinuous bet? And I think one approach that we're taking this year is it can be very strongly held opinion about the theme or the area, and then more weakly held about the exact prototype. And so, like, there is a culture of experimentation. Um, there's a lot of the bottoms up, like engineers on the team are very self-enabled, um, self-driven to test out different ideas. And sometimes, uh, we have a thesis, and it, it might not work yet, and so we then might revisit it in one to two model generations. And so this idea of like these prototypes that actually end up just helping us learn, like that's also valuable, even if it doesn't lead to something immediately shipping. And so I think that allows the incubation and, like, the charter of Labs to really accelerate and see around corners more broadly for Anthropic.

   6. LRLenny Rachitsky25:44

      It's so funny to think about a Labs within an Anthropic, which was already so innovative and, and creative and just, you know, shipping like crazy, that there's value to still creating a Labs team within Anthropic. What enables Labs to work as well as it has? Because you listed all these products, and it's in... It's like, what else has Anthropic shipped? [laughs] This, like, feels like all the biggest wins almost. I'm sure there are many that I'm not thinking about right now. What's, what's kind of core to creating a successful Labs org within, within a larger company?

   7. DPDianne Penn26:12

      I think the team culture, like similar to broadly at Anthropic, I think the team culture is very valuable. I think Ben sets an, uh, incredible vision and pushes people to think about the 10X, 100X of the idea. And, you know, our- the teams, the pods within Labs is small. Sometimes these ideas start with one engineer, right? And I think, uh, sometimes when there's almost really large teams pursuing very ambiguous, large ideas, you end up actually being s-slowed down because of that. Um, so I think it's culture. I think, you know, we actually also select for folks who actually want to do that zero-to-one experimentation, and it's not easy. There's a lot of bets that we end up turning down or turning off, um, and maybe, you know, we revisit them in the future. Uh, but that's hard. That's hard when you pour your heart and soul. You're acting as a founder for a bet, and it's not working yet. Um, so I think it's like that type, selecting for that type of personality, folks who are really passionate and deep about the zero to one.

7. 27:30 – 31:35

   How the research role works

   1. LRLenny Rachitsky27:30

      So you lead product for the research team. You work with the researchers at Anthropic. A lot of people kind of get a n- sense of what is research, what are research- what researchers do. I think a lot of people don't totally understand these very valuable people, uh, at all the AI labs. Uh, the way I think about it, and I wanna under- help people understand, help me understand just what are the researchers doing all day, what I imagine is they have a hypothesis for how to improve the model. They find data, they tweak some algorithms, they check, adjust how it's trained, and they test it, see how it did, keep iterating, and keep trying to find ways to improve the model. Is that roughly right, slash help us understand what researchers are doing all day?

   2. DPDianne Penn28:08

      That's really... I, I think that's a lot of, uh, maybe the, the, like, the more day-to-day. I think one piece around, uh, researchers and, like, research organizations like at Anthropic is there's also a vision of the future, like more broadly. So for example, things like, uh, uh, I think even at the founding of the company, researchers were talking about how do we get Claude to u-use a computer? How do we get AI to, like, navigate a screen, right? So there's a lot of actually very founder-like energy is how I describe it within researchers, our really bold and ambitious researchers. Um, and we have a ton of those at, 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, like, I think you're describing a really good sense of, uh, how do we make iterative improvements on different versions of Claude. The way that, like, my team works with researchers is kind of being very integrated and embedded in, 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, uh, bring the user feedback and ground it in a level that is understandable for user, uh, 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, like, consented, and so we, we, we look at, okay, what, should Claude have called tools in that moment? Or from its current knowledge, or it called the right, 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. On the second case, it would've been a failure on, let's say, search or, uh, knowledge and search and search synthesis. Or it could be something around alignment. And so bring that level of detail to researchers, coming up with, like, is this a big enough problem, figuring out things like evals to then describe how we've improved it. Like, those are the l- levels of actionability, and it's the day-to-day language of the researchers. And so we try to stay very close to how to bring that in an actionable manner, uh, between users to, to the core model training and the research development

8. 31:35 – 35:18

   How to become a top researcher

   1. DPDianne Penn31:35

      loop.

   2. LRLenny Rachitsky31:36

      I was talking to someone the other day about how it feels like research, AI research is, uh, 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 what you can tell? Uh, you know, not everyone can get in, not everyone's brain is gonna work this way, but just say people are like, "Hey, I wanna explore this career path." From what you've seen, what does it take to, to make it there?

   3. DPDianne Penn31:59

      Researchers generally are research and product managers working with research, or both.

   4. LRLenny Rachitsky32:03

      Let's do both. But, uh-

   5. DPDianne Penn32:05

      Yeah

   6. LRLenny Rachitsky32:05

      ... uh, the researchers, like, you know, PMs working with researchers also gonna be very successful.

   7. DPDianne Penn32:09

      Mm-hmm.

   8. LRLenny Rachitsky32:09

      But it feels like everyone's trying to, you know, poach all the top researchers across every company. So just... And I know you're not an AI researcher, but just what, from what you've seen, just, like, what does it take to make it in that, in that career path?

   9. DPDianne Penn32:21

      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, like they reason through problems really well, um, 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, uh, you know, our, like, leadership, our chief scientists, our heads of, like, fine-tuning and, like, 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, like, actually staying really close and be excited to be in the details, I think have been, like, a sign of, like, really strong researchers and developing taste. And I think s- like, another piece is just, like, their ability to think big-

   10. LRLenny Rachitsky33:25

       Mm

   11. DPDianne Penn33:25

       ... over time and be, like, very ambitious, right? Like the Dario, like, we can transform software engineering. And, and, and the, and I think, uh, going in that direction, you learn so much. You get, uh, you have to shoot for the stars in, in, in many ways across, uh, your ideas, uh, I think in order to be a, um, a successful researcher.

   12. LRLenny Rachitsky33:49

       I, I love just this meme of just be more ambitious comes up so often now, which is so hard. Like, it's, it's easy to say that. It's hard to actually... Just, like, how big can you think? And how that's so much-

   13. DPDianne Penn33:59

       Yeah

   14. LRLenny Rachitsky33:59

       ... of what AI now unlocks, just be more ambitious. Um-

   15. DPDianne Penn34:01

       Yeah.

   16. LRLenny Rachitsky34:02

       Yeah.

   17. DPDianne Penn34:03

       Uh, I think it's thinking through it once or twice end to end, and then being, I think, stubborn about the, uh, area and maybe more, uh, loose around the exact, like, approach. Um, it, it is a question we challenge ourselves with, uh, but the technology is moving so quickly, and so how do you make sure what you're building is actually, uh, forward compatible? And so it's also actually part of, like, I think the core product development loop to think bigger, right? Um, one thing I ask the team frequently, or, uh, how I think about when we're building a product is, let's say Claude 8 comes around. What do, what changes in what users do? And then what should, what does that mean for how you're building today? Is it gonna be forward compatible to that experience, right? So, like, just grounding. It's, I think, um, being ambitious is very broad, and so trying to, like, ground it in, in some ways of describing, describing that.

   18. LRLenny Rachitsky35:12

       And also, yeah, everything heading in a direction that all is cohesive and makes sense versus just ambitious in a completely

9. 35:18 – 39:38

   Frontier model safeguards

   1. LRLenny Rachitsky35:18

      different direction.

   2. DPDianne Penn35:19

      Yeah.

   3. LRLenny Rachitsky35:19

      Speaking of ambition and Claude 8, uh, Fable/Mythos recently feels like hit this very new kind of 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. It got blocked. There was a lot of scrutiny, a lot of concern about what it was capable of. Uh, the, all the companies had to go make sure it wasn't gonna hack into all their systems. S- And it feels like now every model, because they continue to get better, will now have a lot more scrutiny, and there will 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?

   4. DPDianne Penn36:02

      I'm going to maybe leave the policy and the export control side to, to folks that, um, own that and work on that. Um, I think the product question and how we interact with these internally is- I think as you mentioned, as frontier models become more capable, the safeguards and the ways of red teaming and testing and the pre-release process, uh, also needs to evolve and adapt quickly to, to address that. And so one example is, you know, before Fable models, we h- didn't have as strong of, let's say, fallback UXs and systems because our, our, our goal was to make sure that, like, there is asymmetrical benefit for this technology and to minimize, like, the downside or, like, a, a severe risk of, of it. And so we ended up building, like, fallback systems so that users will still get a great response from Opus 4.8 immediately. And so I think there's a piece around, uh, as we evolve and, like, improve safety systems, how do we s- continue to develop and deliver great user experiences? I think there's more that we can do on both sides, and so you'll see us innovating, improving on what we call now the model safeguards package, uh, more and more in the coming, coming weeks and months.

   5. LRLenny Rachitsky37:29

      What's really interesting and just, like, unexpected here is it creates this really interesting advantage for Anthropic, where you have access to the latest stuff, and this is gonna happen at every lab. Everyone's gonna keep improving, and it's... It creates this unfair advantage within the labs to have access to the best stuff that other people can't yet, outside of your control. You'd prefer everyone use it. So it's a really interesting, this new feedback loop that's gonna start, where models that are so advanced are only accessible to certain companies, and that's gonna be a whole new unexpect- It's like a second-order effect of, of all these restrictions.

   6. DPDianne Penn37:59

      Our goal is to be, uh, to develop these systems and the models to be as inclusive as possible. Um, I think our goal is to not have that h- happen, uh, for the general purpose, general use, like, technologies and to make it more accessible. I think, you know, it, this is, like, one of our top priorities right now to kind of reduce what, what we're seeing there.

   7. LRLenny Rachitsky38:20

      Yeah. That makes sense.

   8. DPDianne Penn38:22

      Yeah.

   9. LRLenny Rachitsky38:22

      I would imagine you'd want as many customers and people using this thing as possible. This episode is brought to you by Mercury. Radically different banking loved by over 300,000 entrepreneurs, and now with Command. I've been a customer of Mercury's for over six years. I have never once thought about leaving. Mercury is basically what happens when banking is built by product people, not by bankers. They make it so easy, dare I say fun, to send invoices, move money around, set up virtual cards for folks on my team. Does your bank have an API, a terminal native CLI, or an AI-ready MCP server? I don't think so. And just recently, they launched Command, a conversational interface built directly into Mercury, which acts as your financial operator. I've been using Command to transfer money around, to figure out what categories I've been spending the most money in, analyze my cash flows, and just today I used it to find out how much I've made from a specific sponsor over the past year. I just ask, "How much have I made from X over the past year?" 10 seconds later, I have an answer. It is so freaking cool. Visit mercury.com to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column NA, members FDIC.

10. 39:38 – 44:16

    Hiring in the AI era

    1. LRLenny Rachitsky39:38

       I want to talk a little bit about how the product role is changing and who, who is doing well in this new world, uh, now that AI is such a core part of, uh, of our life. When you're hiring PMs, product people, when you're looking at people that do well in today's world, what are some things that you notice? What are you looking for more most? What are you looking for more? What's kind of, like, trending up in what you find is important, and what's kind of trending down?

    2. DPDianne Penn40:04

       We actually, on my team, have not changed our hiring loop, uh, for three years now. Um, [laughs] so what we actually look for and the traits and how we evaluate, uh, generalists like PMs, generalists like research product managers, have actually been the same. Um, so I think some of those traits, number one is first principles thinking, and this is really, uh, rather than pattern matching what you used to do in, let's say, consumer product or B2B SaaS, um, but actually figuring out in this moment for this user group with this technology, what, what is the user value?

    3. LRLenny Rachitsky40:50

       Is there an example that... A lot of people hear first principles thinking, they're like, "Yes, I got it. I'm good at this." What is, what's an example of someone having really demonstrated really good first principles thinking?

    4. DPDianne Penn40:59

       I think one example is, I think you think of a product manager as I own product strategy and delivering user value as, but I demonstrate day to day by writing a PRD or dri- writing a product vision doc. And for, for my team, as, like, research product managers, the way to drive user value is to figure out the right user feedback, the evals, right, that then can be a personification of that user need. So, like, we do write some product documents and PRDs, but we actually have a saying on the team of, "Evals are the new PRDs," right? 'Cause in order to deliver that user value, uh, it's not that exact artifact that people used to write in the last, like, one to two decades. It's a new way of working. And so the first think- principles thinking would be, "Let me figure out what is the thing I should do to achieve my goals," rather than, "Here is a set of activities that I've done and therefore I will continue to do."

    5. LRLenny Rachitsky42:10

       So the idea here is, used to be have kind of an idea, create a PRD, talk to people about it, align on the plan, design it, build it, ship it, see how it goes, iterate. What I'm hearing here is it's like, okay, here's some feedback about something that's wrong or an opportunity. Step one is the eval is now how you define what the work is versus a PRD

    6. DPDianne Penn42:32

       Maybe, maybe step one would be, uh, understanding the user pain point, and so y- the way to even access a user pain point is different, right? In the past, we might do a user interview. Um, I think if you go, like, deep enough, you, you might have the user walk you through their user flow, the pixels. Here, you have to sweat the tokens as much as you sweat the pixels. And so one activity we have on the team is reading the transcripts and understanding, uh, what was the trajectories that failed very deeply to then say, "Was this, like, a hallucination? Was this Claude being overconfident?" So, like, the theme of the failure actually has a lot of nuance, and then that allows you to build a description, a, like, sustained description of that pain point. Uh, so that could be essentially in a new eval. And is the eval on distribution, right? Is it capturing both the positive situations where this is failing and also areas when it should actually not fail? And then bring that back to, let's say, like, research so that we can make the improvements and actually measure the quality of, okay, when we have Opus 5.5, is this area improving or not? Is Claude now able to, uh, identify the right places in the document, uh, and pull the right synthesis out? So it's just the actionability, like, and, and shortening the distance to actionability, um, for, for our c- stakeholders and partner teams like researchers, um, to take action

11. 44:16 – 47:48

    Building an eval set

    1. DPDianne Penn44:16

       on.

    2. LRLenny Rachitsky44:16

       Is there an example of something like this where you found an issue or opportunity and then wrote the eval? And what is, what is the eval looking like in, in most cases? Uh, what d- when people want to picture an eval, what is that? What is, what do they picture?

    3. DPDianne Penn44:29

       We actually, uh, pioneered this concept within Anthropic. So, uh, one of the early examples is the early Claude models were not very good at following specific schemas, so, like, things like outputs in JSON. And, uh, now that is fundamental to Claude being able to be a good agent, right? If you can't output a certain format, you don't know how to, like, access APIs, you can't call tools, et cetera. And so the initial, uh, end-to-end was I was hearing feedback around, you know, Claude 2 days. Claude was not very good at following instructions. So then digging in with users, "What do you mean by Claude is not good at following instructions? Give me, uh, what situations this was happening. Like, what's the exact, like, paragraph? What did you ask? What was Claude's response?" Going to, like, that level of detail. And what I saw was something like 80% of what people meant in the early days for this failure was Claude would not write the right JSON. And so then, okay, let's generate maybe to start just 30 to 40 examples of when Claude was not doing this thing correctly, and then that actually is your eval set, and you can have essentially, uh, a prompt and a response. And if that is not working, uh, in the right golden answer that you might have, then that means that the, the eval essentially, uh, it, it's beneficial 'cause it's identifying a pain point consistently. And so then we added that to our, um, repositories for evals, and when we have, uh, versions of Claude, we actually run that eval and just check. I think at this point it's always 100% or, like, 99.9, and so it's no longer a pain point. Uh, but in the early days it was taking the user feedback, figuring out actually what they mean, can we reproduce it? Is it consistent? Is it a big issue? And then figuring out how to, uh, standardize it in a way that can be consumable for researchers.

    4. LRLenny Rachitsky46:42

       It's basically test-driven development for PMs is, is the world we're living now, uh, where you write the test first. So is this just a core part of the product management job now at Anthropic, writing evals?

    5. DPDianne Penn46:53

       I think so. I, I also think it's, um, something, uh, I've talked to others, PMs at other companies about, and I think it's also more and more of the skill set more broadly. 'Cause a lot of the products that we're building is at the intersection of models with harnesses, with a set of context for a set of users. And so having things like evals actually is an, is a way not just for, uh, folks working on models, but generally within product, uh, to, to get to better user experiences. 'Cause you can't improve what you can't measure, and a lot of this is very still tactile-based. It's still very judgment-based, and so you have to stay close to the details.

    6. LRLenny Rachitsky47:38

       And also very non-deterministic, which is a big part of this, just like, it's not gonna give you the same answer every time, so you gotta-

    7. DPDianne Penn47:44

       Yeah

    8. LRLenny Rachitsky47:44

       ... describe it kind of more broadly. It's not gonna be, yeah, an exact match.

12. 47:48 – 49:55

    Evals vs PRDs

    1. LRLenny Rachitsky47:48

       So this is a really interesting change in the way product happens and will happen is evals. Writing evals versus PRDs is, is a big part of this. Do you guys still do PRDs? Is there still, like, a one-pager describing a problem, or is it replay? Okay. Now you're shaking your head yes.

    2. DPDianne Penn48:02

       We, we, we are. We do. I think, um, when there's a very defined problem, I think things like evals might be almost a shorthand.

    3. LRLenny Rachitsky48:09

       Mm.

    4. DPDianne Penn48:10

       I think there's other cases where PRDs are really valuable. Um, PRDs are great vehicles for getting a very large group of people aligned on a set of sources of truth about experience and set of goals. So when we do have a model, we actually, for every model, we do have a PRD, less necessarily for our researchers, but more for our- Growing product surfaces for our engineering teams, for our, um, g- stakeholders like, uh, legal and safety and others as just a source of truth of putting together what we're aiming to achieve so that a big group of people can row in the same direction. The other place where I do think PRDs are valuable are on the more ambiguous problems and opportunities, right? So we- if we haven't shipped a thing like computer use, we don't necessarily have a set of, like, user-specific pain points always, and I think there's value in the product vision portions of a PRD to explore what could... Even if a technology is not yet ready to work for everyone, how do you get it to work well for some group so you can explore the value, you can actually bring something that is, uh, coherent to a user group.

    5. LRLenny Rachitsky49:35

       Awesome.

    6. DPDianne Penn49:35

       So we do have PRDs. Um, I think the application's a little different now.

    7. LRLenny Rachitsky49:39

       Okay. This is great. There's- [chuckles] I just had a, uh, Andrew for... He's the head of the Codex app at OpenAI, and he's... You guys are aligned. Uh, PRD's not dead. Still very useful for specific projects and ideas. Uh, great.

    8. DPDianne Penn49:51

       Yeah.

    9. LRLenny Rachitsky49:51

       Okay. We've closed, closed the book on PRD. Still kicking.

13. 49:55 – 52:46

    The importance of hands-on leadership

    1. LRLenny Rachitsky49:55

       Okay. So we've been talking a bit about just what kind of skills are kind of emerging for product people. Um, is there anything else that you find is shifted in what patterns, uh, are common across people that are doing well in this new AI world in terms of product managers and folks on the product teams? Is there anything else that you're like, "Okay, this is something you gotta shift," or something you look for more in people?

    2. DPDianne Penn50:19

       I think maybe specifically, uh, for folks who might be mid-career or folks who have been more in a managerial, like, product, like, leadership seat, um, one thing that I think I feel pretty strongly about is in order to be good managers of teams and PMs working with this technology, you have to be really hands-on yourself and have spent not just time tinkering, but actually shipping with this technology and, and, and again, being in the details and sweating the tokens along with your PMs and your engineers and your teams. And so even for folks that I hire who have more tenured PM experience, the onboarding plans are exactly the same as somebody who is, like, more, uh, early career, and it's around understanding users, reading, like, consented user feedback, talking to customers. I think there's something around, uh, being able to, like, understand what to do with this, what, what good looks like, and having developed that in a very hands-on manner that's important. Um, it's not necessarily easy for someone to, uh, agree or be able to see what a, what a good or great AI product or AI feature could look like if they haven't kind of experienced building themselves. Um, so I think, I think there is a... I, I, I do feel pretty strongly that, like, you know, if you're a manager, you have to be hands-on. You have to spend a portion of your time actually shipping. You, you, you have to kind of walk in the shoes of your teams. Uh, and, and that's... I, I always try to carve out a portion of time, uh, to, to actually, like, own one to two work streams when we have models i- in order to ke- like, keep my theory of mind, keep my sense of how the models are moving, how quickly it's improving, uh, uh, so I can help the team make, make decisions and, and make better decisions.

    3. LRLenny Rachitsky52:35

       So what I'm hearing here is if you're not, no matter where you are in the ladder of hierarchy at a company, if you're not building yourself, if you're not actually talking to Claude, talking to Codex, building stuff, you're not gonna

14. 52:46 – 58:10

    Finding joy in AI

    1. LRLenny Rachitsky52:46

       make it.

    2. DPDianne Penn52:46

       And you should have fun working with this technology. I think that's the other piece. I think the folks w- that will be most successful, regardless of their level, are people who love working with AI and, and are exploring and experimenting, and carving out the time, not just for the experimentation, but actually hands-on shipping end-to-end, getting the user feedback, I think has to be fundamental for everyone.

    3. LRLenny Rachitsky53:12

       I 100% know what you mean there. Just, like, me sitting on my newsletter and this podcast just talking about stuff and like, "Yeah, yeah, that sounds great." Like, every time I actually build something and I tinker with all kinds of little projects, you're just like, "Okay, I see what's happening here," and you just get so much more. It's, like, hard to exactly describe what you're, what you, what you experience actually working with the models and building stuff, but it's like a whole different world of like, "Okay, I see. Here's what they're loo- here's what they're talking about computer use. Here's what they're talking about with this limitation of this UX situation."

    4. DPDianne Penn53:39

       Yeah.

    5. LRLenny Rachitsky53:40

       So, yeah. So it's just like... And, uh, you made this really interesting point that you have to have fun with it, which is not easy for a lot of people because they're pushed to use AI or they just don't know exactly what to do with it. For people that are just like, "Eh, I don't know. It's just so annoying. I just have to do this. I don't know what... It's just so, like, I hate this frigging thing. Why do I have to work with this? Things are changing so much. I'm tired." Uh, advice for helping people find that, find that joy in this work.

    6. DPDianne Penn54:06

       I think maybe I'll reemphasize something I said earlier around just that experimentation is not an individual sport. Like, some of the moments where I think I've touched practically every version of research models across 20 vers- plus versions of production Claude at this point, and I think part of the joy comes from seeing other people discover use cases too. And so maybe one idea here would be pairing with somebody who is excited. And seeing what, uh, uh, on a use case that you care about and, and working together versus, um, uh, identifying or trying to figure out the perfect use case yourself, 'cause that might feel like work. Working with others feels like joy a lot of the time. And is there more that we could do to bring that, bring other people along?

    7. LRLenny Rachitsky55:00

       Mm-hmm.

    8. DPDianne Penn55:00

       That's something, like, a lot of times internally we have somebody who is, like, very curious, and them sharing an idea of a new prototype actually brings a ton more people who are like, "Oh, I didn't know this could work now with Claude." And so there's just some virtuous cycles here, um, and, and ways of, yeah, bring- continuing to have joy with, with this technology.

    9. LRLenny Rachitsky55:22

       That's such a good point. I think that's also why Twitter's so useful for a lot of this, is you see other people sharing what they've done.

    10. DPDianne Penn55:29

        Yeah.

    11. LRLenny Rachitsky55:30

        And it inspires you to come up with your own little ideas, and also it's just, like, fun to share your own thing that you've done. So that's a really good point, just, like, find other people to kind of play around with and look for use cases. The thing I've also heard a lot is just find, like, a problem you wanna solve in your life or at work, and just open up Claude, Claude Code, and tell it, "Here's what I wanna do," and it's incredible how far you can get just with, like, a g- vague idea of a problem you wanna solve.

    12. DPDianne Penn55:52

        Yeah. I think it gets hard in that there are so many different things that you could try.

    13. LRLenny Rachitsky55:57

        Yeah.

    14. DPDianne Penn55:57

        And so you just, like, narrowing in on either pairing with someone, working with somebody who, who is, who have a lot of joy about this technology, or figuring out something that you could immediately find value. Like, either of thing, those things allow you to go deeper rather than, like, more high level about too many things. Um, I, I find it hard to keep pace with the number of prototypes or products that are out there. And so my lens has been, how do I go deep in one to two of them-

    15. LRLenny Rachitsky56:29

        Mm

    16. DPDianne Penn56:30

        ... myself?

    17. LRLenny Rachitsky56:31

        That's, uh, that's so interesting you say that, because that's exactly it. We just had this survey, uh, that I, I ran with, uh, my colleague Noam, uh, asking my readers just how they're feeling about all the things going on in the tech right now in AI. And, uh, one of the most interesting takeaways we had was, uh, to find that happiness is exactly what you said, is go deep in a couple things versus trying a just ton of little things. Find a f- couple things to really solve well and then go deep in that as a source. Because a lot of the happiness people feel is when they finally unlocked a way for AI to actually make their lives better-

    18. DPDianne Penn57:04

        Yeah

    19. LRLenny Rachitsky57:04

        ... versus just, like, a couple messed up, broken, half-working things.

    20. DPDianne Penn57:08

        Yeah. It's, it's, um, how do you go from this being a check the box, right? And so, like us as product people, it's then a exercise of product prioritization of your time and your energy. And, and if the goal is to experiment with joy, then how do you, what are the inputs that you need for that? Um, but yeah, I, I, I think a lot of the, um, I think the secret sauce of Anthropic is the culture and the bottoms of nature of how people work, and this, like, experimenting in public. Um, and by doing that, it's very much about how to bring other people along, um, that ends up being, I think, really valuable.

    21. LRLenny Rachitsky57:55

        Yeah, I've heard this so many times from all the labs, just like no, no one's exactly sure how some of this is gonna be used, and a lot of it is just putting stuff out early, seeing how people use it, seeing what, it's, what's possible, and then using that information to build-

    22. DPDianne Penn58:09

        Yeah

    23. LRLenny Rachitsky58:09

        ... the actual product to lean

15. 58:10 – 1:01:05

    How Dianne uses Claude

    1. LRLenny Rachitsky58:10

       in.

    2. DPDianne Penn58:10

       Yeah. Yeah.

    3. LRLenny Rachitsky58:11

       I'm curious how, kind of on this thread of finding ways AI, for AI to help you in your work and life, are there any interesting ways you've been using Claude lately in your work as a, as a PM?

    4. DPDianne Penn58:22

       I think there's a lot of things with, uh, you know, Fable and things like Tag. So they're, they're... I, I think Tag is, um, in, in the very, like, early days, I think there's something around how you work in a different paradigm of allowing this, an agent to go off and work and then bring back, uh, product experiences to you. I think one area that, it's not very recent, but one that, um, I bring up a lot with the team and I think we could do more on using AI, is just, like, how to use it to also be more, uh, to have better conversations with each other, to be better managers. Uh, I don't think it's necessarily, uh, just about raising the IQ of, like, experiences we build, but also I used it a lot in actually, like, prepping for how to have better conversations, um, in the moment during, like, crucial conversations. So I love that book, and so I actually have a skill that helps me figure out, am I having, am I going in the right level of detail given the, the situation at hand, and actually helping me be a better manager and better supporter for the team. Um, so for, for, like, managers on the team, that's actually a thing that I've been sharing more with, with the t- uh, w- with our managers of, okay, how, how do you actually use, use Claude to, to, to make you a better coach? 'Cause it's hard sometimes to find the right perfect words.

    5. LRLenny Rachitsky59:53

       Mm-hmm. Yeah.

    6. DPDianne Penn59:53

       And the models have a lot of perfect and right words, and, uh, I think there is something about how, how it can actually augment us from, like, an EQ perspective in addition-

    7. LRLenny Rachitsky1:00:04

       Mm

    8. DPDianne Penn1:00:04

       ... to IQ.

    9. LRLenny Rachitsky1:00:04

       Oh man, there's so much interesting stuff there. So just to understand what you're doing there, so you built a skill. You're just like, "Claude, build a skill pulling in, uh, lessons from Crucial Conversations," the book, which it knows enough about. You don't have to even give it the content. And then you use that skill to talk to Claude, "Hey, I have this very difficult conversation coming up with a colleague. Give me some-

    10. DPDianne Penn1:00:24

        Yeah

    11. LRLenny Rachitsky1:00:24

        ... tips on how to approach it."

    12. DPDianne Penn1:00:25

        Yeah. And it's, it's a great, uh... It's almost like, uh, coaching, like individualized, personalized coaching of just how to make you... And, and there's so much context switching that we do all day, and having, like, Claude help me pair and help me- And maybe there are times where I end up not using suggestions from Claude, uh, but it actually is, uh, ends up being very helpful for, for just coming up and brainstorming. Am I thinking about reactions in the right way? How do I actually, uh, go a bit deeper faster, build trust faster, uh, be more direct?

16. 1:01:05 – 1:03:50

    Avoiding overreliance on AI

    1. LRLenny Rachitsky1:01:05

       Yeah. Man, I have so many questions here. This is so interesting. Uh, one is just, like, there's concern people are gonna start talking the way AI writes because they're talking AI so much, and it's gonna be like, "Dianne, it's not this, but it's that." Uh, I know that you're not doing that, but that's a, you know, a concern people have. Let me just ask about that, I guess. Do you fear this... There's this, you know, brain rot, uh, atrophy stuff people talk about where we're just so reliant on AI now, and we stop learning and thinking and, you know, overreliant on AI. Thoughts on that, being so close to it and being so integrated with, with AI constantly?

    2. DPDianne Penn1:01:38

       A lot of actually thinking process and writing process are tied together for me personally. And so I think there are ways where I use Claude to augment my thinking, but what I wanna make sure, and maybe this is what you're describing, is Claude doesn't take over all of my thinking for me. And so I think depending on the situation, depending on how much more personal judgment I wanna have in a situation, I might, uh, um, come up with my own POV first, and then work with Claude through that. Um, and making sure that, like, I maintain my sense and tone throughout. I think there are then other things like updates, right? We have, like, monthly business reviews, and then in those cases, it's much more I want, actually want it to be standard, and I want it to be much more like it gets a cryst- crystallized information in the right way. And maybe, and I have a skill, and, like, we're augmenting and improving our skill for that. But I want to get, uh, to a place where, like, the monthly business review, the writing of that is potentially asymmetrically less valuable than the thinking. And so how do I get that piece delegated to Claude fully, and I'm more of a reviewer and a verifier of that information? So I think it depends on, like, what you're using Claude for and what you're trying to convey and, like, is there, is there a asymmetrical value in, in delegating more to Claude?

    3. LRLenny Rachitsky1:03:11

       What I'm also hearing, the first tip is really great, which was think first, have a point of view, and then kind of use Claude as a, a sparring partner almost to evolve the idea, push back on the idea.

    4. DPDianne Penn1:03:22

       Yeah. Yeah. And I think this is where things like actually our alignment research and safety research is helpful because it-- What you don't want is, like, a AI that just agrees with you, right? What you want is this technology to actually augment and grow and, like, get to a better outcome. And so sometimes it's having Claude push back makes me better, and so that's great. Like a coworker, I want somebody to push

17. 1:03:50 – 1:07:11

    The constitution that makes Claude better

    1. DPDianne Penn1:03:50

       back when, uh, my ideas are not fully formed.

    2. LRLenny Rachitsky1:03:53

       I wanna hear more about that. I've heard that when Ben Mann was on the podcast, he talked about the constitution that is built into Claude and how unintuitively the work and the focus on safety and alignment, as you said, and this constitution that describes how Claude should think and operate, that act- You would think that would limit the abilities of Claude and make it less fun and interesting. It's exactly the opposite. Claude is the most interesting personality. I hear that constantly. It's just like, I much prefer talking to a Cl- like, OpenClaude famously [laughs] was built on Claude, and then people were s- forced to s- we won't get into it. [laughs] Were forced to switch to ChatGPT, and they're like, "This is so bad. This is not who I'm used to talking to." Uh, so that is, I think, a really interesting point I just wanna make sure we spend a little time on. W- why is it? Why is that the case, just this focus on alignment, safety, having this clear constitution? Why does that make Claude better and, and more interesting to talk to also?

    3. DPDianne Penn1:04:49

       In order to make Claude as, like, im- intelligent and as capable as possible, being able to have Claude actually push back in the right points and then add, it's like a yes or no and, actually helps you come to a better conclusion. So I've used Claude to help with things like, are we making the right pricing decision on the next version of Claude? I've-- It's a little bit meta, but using a research version of Opus, asking it to figure out how it should price. And being able to come out with better outcomes is a goal at the end of the day. And so having AI not just be an assistant, not just be a doer, not-- and being delegated tasks, but figuring out, is it doing the right thing? That's actually very integrated with knowing when to push back.

    4. LRLenny Rachitsky1:05:42

       Mm.

    5. DPDianne Penn1:05:43

       Right? That's part of knowing when you should be proactive. Proactivity is not as necessarily always doing a thing that you are scheduled to do. It is knowing when to come up with a new idea. And so in order for Claude to be more useful, the general approach has to be that it knows when to push back. It's a core part of the characteristics together, uh, uh, of the models.

    6. LRLenny Rachitsky1:06:09

       That is so interesting. It's so interesting that that is what a big part of it, like, it be- it being less compliant is almost what makes it better and more useful because-

    7. DPDianne Penn1:06:17

       Yeah

    8. LRLenny Rachitsky1:06:17

       ... we need that. [laughs] Like, I've had so many people where they're like, "Hey, like, AI told me I was right." And like, no, I wish-

    9. DPDianne Penn1:06:24

       Yeah. And, and, and-

    10. LRLenny Rachitsky1:06:24

        I, I wish it was to other people.

    11. DPDianne Penn1:06:26

        Yeah. And it comes back to our earlier point around thinking, right? How do you protect your thinking?

    12. LRLenny Rachitsky1:06:31

        Mm-hmm.

    13. DPDianne Penn1:06:31

        Um, if you have a AI that can be a thinking partner, a thinking partner doesn't just agree with you. It should add to you, and you should come away at the end of the day having better ideas because you worked with Claude. That should be the hero goal, not just making your ideas 10% better.

    14. LRLenny Rachitsky1:06:52

        Yeah, I love this since, like it used to be think 10X. That used to be the, the way, you know, founders push people. Like, what if we 10X this? And I love what I keep hearing is like, it's like, how do we go 1000X from this idea? What is the most ambitious version of this? I wanna come back to something that I ha- I was thinking about as we were talking about, uh, talking to Claude constantly. Um,

18. 1:07:11 – 1:11:40

    AI writing and verification

    1. LRLenny Rachitsky1:07:11

       it's very clear when AI has written something still. It's funny that it's a large language model. You would think of all things, it would be very good at writing. And interestingly, just no AI is very good at writing. It's always very clear this was AI written. Do you think we'll get to a place where we will not know this was AI?

    2. DPDianne Penn1:07:32

       I think it depends on what's the, uh, goal that you're looking to achieve-

    3. LRLenny Rachitsky1:07:38

       What's the eval?

    4. DPDianne Penn1:07:38

       ... by knowing or not. Yeah. Uh, what's the eval? Um, I actually do think there's more that we could be doing on making Claude write better. There's actually very active efforts, um, on, on my team and on the research side about making Claude write better, just generally. I think it should be clear where an idea is ident- is being led by you or by Yuleni or me, Dianne. I think it really depends on, uh, what's the goal of that writing. Like, for something like a monthly business review, I would actually love to have that end-to-end be written by Claude. [laughs] Uh, and-

    5. LRLenny Rachitsky1:08:21

       And obviously, and not f- make it feel like it was written by a human. It's such an interesting point you're making, like is it actually better for us to know that it's AI versus not?

    6. DPDianne Penn1:08:29

       Yeah. But, but it's, it's, um, but it's also for maybe the lens is more around like verifiability or who's verifying-

    7. LRLenny Rachitsky1:08:39

       Mm.

    8. DPDianne Penn1:08:40

       .. the output.

    9. LRLenny Rachitsky1:08:40

       Right.

    10. DPDianne Penn1:08:41

        Right? Like who's signing off, uh, maybe less around who's writing, but who's verifying, who's signing off. That becomes like more what matters than who's writing it.

    11. LRLenny Rachitsky1:08:52

        Why, why do you think AI is not great at writing? Like my guess is it has studied all of the best writing in all of humanity. It's figured out here's the best way to write, and now that we... And it's, there's, there's only so many ways to, to write, and so we've just recognized, okay, this is what [chuckles] AI does. It has these tropes. Is that the core of it? Is there something else that's keeping it from being a great writer? Ironically, being a large language model of all things, you'd think it'd be really great at language.

    12. DPDianne Penn1:09:21

        I think part of it's also, uh, we need to invest more in training improvements to make AI continuously strong on areas like writing. Um, I think it's also, you know, like the technology's jagged edged, like, like we mentioned. So sometimes when the models were good at writing but not agentic, our, our thesis is how do we make the models more agentic or call the right tools? Now that that's improved a bit, then it's, well now these other areas actually become more of the rough edges. And so I think we're in one of those moments with writing where, uh, we need to actually just focus and prioritize on training the models to be, like, great at this area and, like, that is an active, uh, very active area for us. So finally, like you mentioned.

    13. LRLenny Rachitsky1:10:11

        Okay. I'm glad. [laughs] I'm glad, and also, uh, it's gonna be interesting once AI is so good we're like, "I don't know who wrote that." But, um, to your point, sometimes we actually wanna know that it's AI. That's really interesting. I never thought of it that way. The other interesting part of this is that there's that comedian who was joking that we're like on a plane and the Wi-Fi's down, and we're just like, "What the hell? The Wi-Fi's not working on this plane. This sucks. How dare you?" When you're like in a, in a tube in the sky flying like a bird, and, uh, how dare you complain that the Wi-Fi doesn't work? Like your point is [chuckles] there's so much advancement and so much power, uh, we can't fix it all. We can't make it all work the best, uh, possible, and so, uh, basically AI writing has been not the priority and it feels like there's more investment happening there.

    14. DPDianne Penn1:10:53

        Yeah. I think like tone and character is a priority. I think it's this advancement of the technology is a work in progress. And so we made, we, we see a leap or emergence of like a jump in agentic behaviors, and so that is a new normal, and then these other capabilities needs to continue, like improving.

    15. LRLenny Rachitsky1:11:16

        Yeah, yeah.

    16. DPDianne Penn1:11:16

        And I think once we improve, let's say, writing and like tone and character, uh, we probably will say like, how do we have Claude be even more proactive? Like proactivity is an opportunity, and that's human nature. Like we want to make ourselves better. We want to make this technology better. Um, so yeah, it... I, and I think we're applying it to, to AI, which is the right thing. We should be making

19. 1:11:40 – 1:14:10

    Where human brains will continue to be valuable

    1. DPDianne Penn1:11:40

       it better.

    2. LRLenny Rachitsky1:11:41

       I wanna ask you a couple questions I like to ask folks working at the very center of the future of that is coming. Um, one is, where do you think human brains will continue to be most valuable over the years? I know Anthropic's mission and, and vision is we'll reach AGI, a, a super intelligence, so in the future, maybe nowhere. But before we get there, where do you think human brains will continue to be most valuable as we approach that, that timeline?

    3. DPDianne Penn1:12:09

       We started to talk about making Claude and models better at judgment, um, especially in the last, um, year or so. I think judgment is one and is an area where it's accumulation of so much nuance and so much experience, and these systems haven't experienced as much as humans have. And so I think that hard-earned, like judgment is a, a, a area for, for product leaders and just generally, um, will continue to be really critical. There are so many things AIs can build. Which one are the things that, you know, an org like Labs should build, right? A lot of that requires like human judgment, persistence. So proactivity, these are all traits are, are beyond just general capabilities, but just behaviors and characteristics of, like, people at that level of, like, how do you get to the best solutions? How do you create the m- the best experiences? So I think those types of traits are actually the tactile, uh, traits that I think will be, uh, continue to be important. Um, I think there is also, uh, still a lot of, like, capabilities and subject matter expertise as well. I think, you know, software engineering has been really transformed by AI. I think there's areas like, uh, biology, life sciences. These are all things that, um, we're just kind of at, like, the foot of the exponential on. Like, maybe software engineering, we're on the exponential. On some of these area- other areas, we're not quite there yet. And so, um, I think you're seeing us ship things like Claude Science, investing in these areas, because those are areas that, um, I think it's just bring the, this technology to society and having a positive benefit for society. So I think there's a lot

20. 1:14:10 – 1:16:26

    Navigating AI with kids

    1. DPDianne Penn1:14:10

       more to go there.

    2. LRLenny Rachitsky1:14:11

       Another question I wanna ask is, um, as someone with kids, how do you think about what you are encouraging them to learn? Where do you think you're gonna nudge them to be successful in this wild new world that we're entering?

    3. DPDianne Penn1:14:27

       I actually think it's a lot of the same traits like you and I probably grew up with-

    4. LRLenny Rachitsky1:14:32

       Mm

    5. DPDianne Penn1:14:32

       ... which is curiosity for learning, persistence, believing in your own inner voice, developing and then believing in your own inner voice. Like, I have a four-year-old, I have a eight-year-old. It's on us to help, uh, it's on me to help them develop their inner voice, and whether that's being opinionated and taking a stance to me, [chuckles] right, and developing that, encouraging that, uh, I think that, those types of skillsets are things that, um, is important in the future, and, uh, like, having their own individual voice.

    6. LRLenny Rachitsky1:15:10

       That is so interesting. It's so in, related to the answer you had when I asked about how to avoid a brain rot, essentially, and over-relying on AI, which is just keep focused on your own point of view and your own perspective before you over-rely on AI, and just this idea you're describing of building that in kids is, is really important. Uh, that is so interesting, and I love how all this, all this kind of connects, judgment, persistence in a, a s- a point of view of your own.

    7. DPDianne Penn1:15:34

       Yeah. Anything-

    8. LRLenny Rachitsky1:15:35

       Both for kids and also adults

    9. DPDianne Penn1:15:36

       ... you can think about. Yeah.

    10. LRLenny Rachitsky1:15:37

        Anything.

    11. DPDianne Penn1:15:37

        Anything we think about, um, for your-

    12. LRLenny Rachitsky1:15:39

        Oh, man.

    13. DPDianne Penn1:15:40

        Um-

    14. LRLenny Rachitsky1:15:40

        Well, like, the question I'm thinking about is just when to get them on, like, some AI thing, you know? When... I have a three-year-old, so it's pretty early for that [chuckles] . But, you know, how do you get, how do you onboard them to this crazy thing? I had, I was at an event recently, and a bunch of parents were talking about how they think about AI and their kids, and one person had a really interesting approach, which is, uh, keep them on the very early models so that they still have to struggle a bit and not get-

    15. DPDianne Penn1:16:04

        Mm

    16. LRLenny Rachitsky1:16:04

        ... all the answers immediately.

    17. DPDianne Penn1:16:05

        Mm.

    18. LRLenny Rachitsky1:16:06

        Thought that was interesting, like an open source local model. [chuckles]

    19. DPDianne Penn1:16:08

        I like-

    20. LRLenny Rachitsky1:16:08

        Not Fable. Yeah.

    21. DPDianne Penn1:16:10

        Oh.

    22. LRLenny Rachitsky1:16:11

        Yeah. Uh, and curiosity is something, uh, I al- I keep mentioning Ben Mann, but his answer actually to this question has always stuck with me, which was, um, curiosity, and also just, like, he's a big fan of Montessori, which is what I'm, we're encouraging for our kids, so there's something there.

21. 1:16:26 – 1:21:54

    Alignment, the future of the PM role, and burnout

    1. LRLenny Rachitsky1:16:27

       Maybe a last question, just along, kind of along these lines, something Fiona Fung actually suggested I ask you, uh, who was recently on the podcast. How do you stay just recharged and not burn out being in the center of this crazy storm of AI a- as a mom, uh, working in, you know, [chuckles] overseeing the research work at Anthropic? Uh, I just, like, we're living through the most unprecedented time working at just, like, being, you know, being on the outside of Anthropic, it's crazy. I don't even know what it's like to be on the inside. Um, what have you learned about avoiding burnout, staying recharged, staying sane during the middle of all this?

    2. DPDianne Penn1:17:01

       In 2024, we shipped four models for, in the whole year, or four series of models, and I think we did more than that volume in just Q2 of this year. [laughs]

    3. LRLenny Rachitsky1:17:13

       Sure.

    4. DPDianne Penn1:17:14

       I think I've been really lucky with, uh, the team that we've grown and built, both the stakeholders on the research side and within our research product management team. Um, I think that one of the magical parts about approaching all of this is that it's not an individual sport. Um, there's, like, a sense of radical ownership and team collaboration that I think sometimes it does feel like a pr- high-performance sport, 'cause you're in very critical decisions. There's new information about users, about training, and you have to make recommendations and judgments and decisions very quickly, and nobody can do that sustainably by themselves. Um, and so I think what's really helped is having a team that is incredible, who looks out for each other, who, you know, the night before a launch, even if they're not the core DRI on that model, will stay up and help the DRI, who, uh, to review the blog post and make edits and come up with better demos, and knowing to be each other's sort of extra hand. I think it's very easy, if you take all of this change on your own shoulders, to feel like you're alone and to feel like you have to do everything. Um, but I think one of the, like, magical parts of Anthropic is this ability for us to, uh, figure out what are those opportunities to help each other, and actually then taking the next mile of, like, mind-melding. We called it, like, entering the hive mind. There was a article about this, and I think, like, part of that is just that allows, like, the team to replenish. It's not that you- I, I was just on PTO in June. It's not just that you can take PTO and you come back to, like, 3X the amount of things to do. It's actually that you can p- take PTO and know the team can figure out the right things to do, and that we individually can, like, watch out for each other. Um, so I think that's a big part. I'm really lucky, just personally. Um, also my partner is really supportive. Uh, um, this is year six of me working in AI, so Amazon and then Anthropic, and so he sees how much I just love the technology and what this can do. And that really helps, I think, also, um, from, like, a personal perspective as well.

    5. LRLenny Rachitsky1:19:44

       I love, I love how many of these answers connect. So-

    6. DPDianne Penn1:19:47

       [laughs]

    7. LRLenny Rachitsky1:19:48

       ... what I'm hearing here is just the having other pe- working with other people, relying on other people, helping each other out when things get crazy. Uh, it... Which is a similar answer you had for just how to, how to find the joy and, and, and fun in this work. Just gets... And again, be inspired by other people, see what they're doing.

    8. DPDianne Penn1:20:05

       Yeah.

    9. LRLenny Rachitsky1:20:05

       Work together.

    10. DPDianne Penn1:20:06

        Yeah.

    11. LRLenny Rachitsky1:20:07

        And it's interesting, when Fiona was on the podcast recently, she... I was asking her just, like, what's changed in the world of software engineering, and she pointed out it's a lot lonelier now because now we're working with agents instead of other humans. Teams are smaller. People are have all these fleets they're talking to constantly. And so this is just a reminder of just the power of just actual other humans around you.

    12. DPDianne Penn1:20:26

        We're, we're asked to work and make decisions on really big things because you have more scale from the technology, right? And I think having individuals, having other folks more who can have some level of, like, mind meld with what you work on, how you approach, maybe not exactly every detail, but what are the first principles, what are the assumptions you make, then helps them, uh, you know, back up for you or, uh, push your decision and sharpen your thinking. Um, so I think, you know, we really try to, like, I really try to look for that when, like, building the team, growing the team-

    13. LRLenny Rachitsky1:21:06

        Mm-hmm

    14. DPDianne Penn1:21:06

        ... hiring. Like, is this person going to care about their own ego and building out a big org, or are they gonna care about contributing to Anthropic and contributing to the, like, impact of the team? And orienting f- towards folks who are, like, low ego, team-oriented, um, I think that's... Yeah, it, it's a big part of, I think, the sustainability.

    15. LRLenny Rachitsky1:21:30

        Yeah. Just always, a lot of it's always just comes down back to culture and hiring and... And I know I've heard a lot just the reason Anthropic is able to move so fast. I remember that moment when, like, [laughs] something shipped every day of the month. [laughs] There's, like, a calendar of launches. And people were talking about how is this possible. And what I heard a lot is just because everyone's so aligned around the mission and the values, it allows people to make decisions really quickly.

22. 1:21:54 – 1:33:48

    Lightning round and final thoughts

    1. DPDianne Penn1:21:54

       Yeah.

    2. LRLenny Rachitsky1:21:54

       Before we get to our very exciting lightning round, is there anything else, Dianne, that you wanted to share? Anything else you wanted to touch on? Anything you wanna maybe double down on of things we've talked about?

    3. DPDianne Penn1:22:04

       This was actually really fun, 'cause I feel like your questions actually sharpened some of my thinking around how the thoughts kind of connect. [laughs]

    4. LRLenny Rachitsky1:22:10

       I'm your, I'm your real human Claude over here.

    5. DPDianne Penn1:22:12

       [laughs] One thing that I really, uh, w- want to, like, convey or, um, have people take away is, I think, one, in the ways of working, but also just, two, that if, like, this is a, this is a lot of, like, growth and change, and having the joy in using this technology and, like, if you're feeling like in this moment you don't have as much of that feeling of initial joy, how do you find people who do, uh, if this is an area that, that you're excited and, like, want to work on? And I think developing skill sets, replenishing skill sets in many ways of things like thinking from a first principles manner about what you solve. I think fundamentally, you didn't ask me this, but there is this question in the community of do we still need PMs when the models are so capable, when engineers are leaning in? Um, I think the role of people who are user-centric, who go into the details of understanding what users are trying to accomplish, bubbling that up in an actionable manner, and doing the relentless work to do that, like, that to me is the core of a product person, and I actually think we need more of that. I think w- we are becoming very technology layered driven, and actually to make that impactful, it's... You have to go deep, you have to be curious, you have to be super hands-on. And those are things that I think are also traits that have, I think, helped Anthropic from a product development and model development perspective, and is part of the culture, and hopefully that's valuable for others as well.

    6. LRLenny Rachitsky1:23:59

       Amazing. What an inspiring way to end it.

    7. DPDianne Penn1:24:02

       [laughs]

    8. LRLenny Rachitsky1:24:02

       Oh, man. Yeah, and this is... I've been saying this too for a long time, just now that building is easy, the hard part becomes, as you said, what should we build, and is the thing we have built correct and good and worth leaning into?

    9. DPDianne Penn1:24:14

       Yeah.

    10. LRLenny Rachitsky1:24:14

        And to me, that's what PMs do and what PMs are good at.

    11. DPDianne Penn1:24:17

        Yeah. Yeah. Yeah.

    12. LRLenny Rachitsky1:24:19

        So, yeah.

    13. DPDianne Penn1:24:19

        And it's getting into the details of the user.

    14. LRLenny Rachitsky1:24:22

        Hmm. Yeah. Empathy. Okay, great. PMs are gonna make it. Okay. PRD is not dead. [laughs] All kinds of, all kinds of, uh, important lessons here. Uh, Dianne, with that, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

    15. DPDianne Penn1:24:36

        Yep.

    16. LRLenny Rachitsky1:24:37

        First question, what are two or three books that you find yourself recommending most to other people?

    17. DPDianne Penn1:24:43

        One personal one I really like, How to Raise an Adult. So, uh, uh, I'm a mom. I think a lot about what is the things that I wanna instill in, in, in my kids, and that book is really helpful for describing, we're not trying to raise children, we're trying to raise adults. So just the framing of what does that mean, and what does it mean, what are the characteristics that we wanna hone and, like, harness and foster in our kids? Um, the other book that I, uh, was listening to on Audible recently is Incorrigible by Eric Ries. So the, the author-

    18. LRLenny Rachitsky1:25:22

        Incorruptible. Incorruptible, I think is-

    19. DPDianne Penn1:25:23

        Incorruptible. Yes, yes

    20. LRLenny Rachitsky1:25:24

        ... yeah. His recent podcast guest.

    21. DPDianne Penn1:25:26

        And the, um, yeah, and I, I, I just, I think the question of how to build great companies is important. I personally just been most fascinated with how to keep great teams and great companies going further, and it was very interesting to just kind of see his framing and reframing of the question. Um, I loved some of the examples around having metrics around culture. You, if you can, if you only measure revenue, and then that's kind of how you're goaling against. But if you have other better metrics, that's actually the way, uh, to, to, to sustain the, the values you care about. I've been kind of trying to think about how to actually bring that to the team level of, like, how do we better articulate, right, our norms, a lot of the things we talked about on the team. So I think that's also a really good read.

    22. LRLenny Rachitsky1:26:15

        There you go. Uh, that'll be your next watch, everyone, as you're listening to this, the Eric Ries episode. Uh-

    23. DPDianne Penn1:26:20

        Yeah. Oh

    24. LRLenny Rachitsky1:26:20

        ... such a good episode. Yeah.

    25. DPDianne Penn1:26:21

        Good.

    26. LRLenny Rachitsky1:26:22

        Uh, and his book just came out, Incorruptible.

    27. DPDianne Penn1:26:24

        Yes, yes.

    28. LRLenny Rachitsky1:26:25

        And I think it was, like, a, a New York Times bestseller. Like, it's actually doing incredibly well, which I was really happy to see.

    29. DPDianne Penn1:26:31

        Yeah, exactly.

    30. LRLenny Rachitsky1:26:32

        Next question. Favorite recent movie or TV show you've really enjoyed? Most people at Anthropic don't have time to do wa- to watch things, but [laughs] I'm curious if you have an answer.

Episode duration: 1:33:50