Anthropic 第一位技术 PM：在能力跃迁之前构建产品

Diane Penn In 2023 when I started, nobody said anthropic and claude and coding in the same sentence. I want to go back to the beginning of anthropic. I remember dealing, man, these guys have no chance. OpenAI is so far ahead. At the time, I saw people were starting to use these models not just for code autocomplete, but actually writing long form code and sat an opportunity for us to train Opus 3 to be better at. That was the inflection. 2023 年我刚加入时，没有人会把 Anthropic、Claude 和 coding 放在同一句话里。主持人：我想回到 Anthropic 的起点。我记得当时心想：这些人根本没机会，OpenAI 已经领先太多了。 Dianne：那时我看到人们开始使用这些模型，不只做 code autocomplete，而是真正在写长篇代码。我意识到，我们有机会把 Opus 3 训练得更擅长这件事。

I always think about Opus 45 a year later during winter break when everyone was home able to code. What was magical about Opus 45 is we also now not just had a model but a vehicle a great product experience like cloud code. Opus 45 wouldn't have had that moment without a product like cloud code and cloud code wouldn't have had that type of adoption accelerated without opus 45. I want to talk about how the product role is changing 那就是转折点。我总会想到一年后的寒假，当时 Opus 4.5 发布，大家都在家里，可以尽情 coding。 Opus 4.5 的神奇之处是，我们不只有一个 model，也有了一个载体，也就是像 Claude Code 这样优秀的产品体验。没有 Claude Code 这样的产品，Opus 4.5 不会迎来那个时刻；没有 Opus 4.5，Claude Code 的采用也不会加速到那种程度。主持人：我想谈谈 product role 正在怎样变化。

主持人 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. Something Gary Tan's been talking about. If you are willing to spend $100,000 a year right now in tokens, you are living the way somebody in 2028 is going to live. 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 and great and better ideas. 对我的团队来说，创造用户价值的方法是找到正确的用户反馈，也就是 evals。团队里甚至有句话：“evals are the new PRDs。 ”Gary Tan 一直在谈一个观点：如果你现在愿意每年花 10 万美元购买 token，你过的就是 2028 年某个人会过的生活。你必须像打磨 pixels 一样认真对待 tokens。

And there's no substitute for that. People need to be more ambitious with AI tools these days because they're just capable of so much. 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? Today my guest is Diane Penn, head of product for the AI research and labs teams at Anthropic. 你必须使用模型，才能想出好主意、很好的主意和更好的主意。这里没有替代方案。如今人们使用 AI tools 时需要更有野心，因为这些工具能做到的事情实在太多。我常问团队：假设 Claude 8 出现，用户的行为会发生什么变化？这对你今天的构建方式意味着什么？

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 adropic from claw 2 through fable. She's also helped incubate and launch claw code, MCP, skills, claw design, and also core capabilities like computer use, tool use, and reasoning. 主持人：今天的嘉宾是 Dianne Penn，她负责 Anthropic 的 AI research 与 Labs 团队产品工作。三年多以前，她以第一位 technical product manager 的身份加入 Anthropic；按 AI 的时间尺度，这几乎是一辈子，当时产品团队只有五位工程师。

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 anthropics research and labs teams. Before we get into it, don't forget to check out lenniesproass.com for a year free of the hottest and most beautifully crafted AI products in the world available exclusively to Lenny's newsletter subscribers. 她参与发布了 Anthropic 从 Claude 2 到 Fable 的每一代模型，也协助孵化并推出 Claude Code、MCP、skills、Claude Design，以及 computer use、tool use、reasoning 等核心能力。能与处在 AI 和 product management 最中心位置的人交流，总是令人眼界大开。

With that, I bring you Diane Penn. Diane, thank you so much for being here and welcome to the podcast. Thank you, Lenny. It's so nice to see you again. I want to 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. It was three years. I remember just like feeling that man these guys have no chance. 很难想象还有谁比 Anthropic research 与 Labs 产品负责人更清楚未来正走向哪里。正式开始之前，别忘了访问 lenniesproass.com。 Lenny's Newsletter 订阅者可独享一年免费使用当下最热门、制作最精良的 AI 产品。下面欢迎 Dianne Penn。

Open AAI is so far ahead every like how what are they thinking? How is this possible? Open AI 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 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. Dianne，非常感谢你来做客，欢迎来到节目。 Dianne：谢谢你，Lenny，很高兴再次见到你。主持人：我想回到 Anthropic 刚开始的早期。我记得它首次推出模型，大概是三年前。当时我只是觉得：这些人没有机会，OpenAI 已经领先太多。他们在想什么？这怎么可能？ OpenAI 已经赢了，一切都太迟了。

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 at the beginning? 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. 如今情况截然不同。我看到的最新数字是，Anthropic 的 ARR 大约达到 500 亿美元。过去成功公司上市时估值才会到 500 亿美元，而据报道，Anthropic 现在每年就能产生这么多收入。你是最早加入的一批 PM 之一，当时大概只有五位工程师，而且你入职时模型甚至还没有发布。 Anthropic 的早期是什么样？

There was one engineer for the entirety of our API business 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. 有什么会让大家感到意外？ Dianne：我认为，成就今日 Anthropic 的很大一部分东西，其实从早期开始就是核心。正如你所说，我在 2023 年加入，当时有五位产品工程师。如果你相信的话，当时整个 API 业务只有一位工程师。我认为很重要的一点是，文化非常强。这也是我总会向对公司感兴趣的人强调的：我们确实会践行自己的使命、文化与价值观。

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 cloud.ai I like another chat 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. 同时，整体氛围非常像 startup。你说得对，我们在早期一直试图找到自己的身份。技术本身是一部分，但这种技术怎样为用户、为社会创造价值？它可能成为什么？早些年，我们用不同方式探索这些问题。我们确实从 Claude.ai 开始，它就像另一个 chat assistant，后来逐渐发展出 tool use 等能力。

I don't know if you like remember that. No. 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. 我认为，真正开始找到节奏的一个时刻，是推出 Golden Gate Claude。不知道你是否记得？主持人：不记得。 Dianne：它实际上只上线了大约 24 小时。当时是 2024 年初，我们刚发表一项早期 interpretability research，其中一个例子涉及 model 各层中的 features，它们会表达某类主题。

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. 研究人员识别出的主题之一是 bullet point writing，另一个是 people and places。还有一个频繁出现、引发共鸣的主题是 Golden Gate Bridge。当你把这个 feature 的强度调高时，Claude 就会痴迷于 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 bring bring it to the masses and bring it to people who are starting to use claude and uh so the entire uh experience actually we spun up on our cloud.ai I website within 24 hours and that took like engineering, product,design, uh our like research teams all working together and we were really proud of it. 也就是说，它会在每条回复里都绕回 Golden Gate Bridge。如果你说：“给我一份意大利面的食谱”，它会回答：“这是食谱，其中橙色就像 Golden Gate Bridge 使用的 International Orange。 ”这非常古怪。我们当时很想把这种体验直接带给大众，带给刚开始使用 Claude 的人。于是整个体验在 24 小时内就在 Claude.ai 网站上线了，工程、产品、设计和 research teams 全都参与其中。

I think it maybe reach only 2,000 people 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 startupy like pace. That to me was like one of those like maybe hidden inflection points of we were starting to find our identity that we could build products, build experiences that were different for what our competitors had seen, what was already out there. 我们为此非常自豪。说实话，它可能只触达了 2,000 人，但它让我们觉得：原来我们真的可以创造新的用户体验，用一种与众不同、也符合自身气质的方式展示 research，而且能以非常 startup 的速度完成。对我来说，这也许是一个隐藏的转折点：我们开始找到自己的身份，能够构建与竞争对手和现有产品不同的 products 与 experiences。

And I think that obviously labs, clog code, etc. Like we then started to identify ourselves as what 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. 后来通过 Labs、Claude Code 等，我们进一步明确了自己如何理解 AI、如何把它带近公众。但这是一种非常 bottoms-up 的文化。整个项目都是自下而上发生的。我看到 engineers、designers 主动贡献时间。

Um, and so I I like to always use that as example of like what the day early days were like, but the culture and and and the values have very much I think stayed the same since those early days. This episode is brought to you by our season's presenting sponsor work OS. 所以我总拿这个例子说明早期是什么样；而我认为，这种文化与价值观从早期一直保留到今天。主持人：本期节目由本季 presenting sponsor WorkOS 赞助。

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主持人 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? 主持人：回顾 Anthropic 从一个试图挑战 OpenAI 这个巨头的 lab 走到今天，还有哪些重要转折点？哪些时刻让你觉得“哇，这真的改变了一切”？ Dianne：在训练和测试 Opus 3 时，绝对是一个重要时刻。那时公司应该还不到 200 人。我们很清楚，自己需要、也想要创造一个 frontier model。

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. 这对触达用户和消费者、展示我们的 research 都非常重要。我们也在寻找一个理由：为什么有人应该选择 Claude？早期我们一直被问这个核心问题。 Opus 3 大概在 2024 年 3 月初发布，但此前经历了很多个月。 inference、research、fine-tuning、pre-training 等许多团队在不同阶段围绕同一个目标协作。

I remember uh being the PM, us uh the research leagues, 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 was working really hard uh to figure out that like what are we training the model for? Is it showing up the right way? 我认为所有参与者都非常自豪。我记得自己作为 PM，与 research leads 等人一起工作。当时大约是 12 月，大家都在家里。我们各自在父母家中，从视频背景能看到每个人儿时的房间。

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 that 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. 大家都非常努力地解决这些问题：究竟在为什么训练模型？它是否以正确方式表现出来？这对建立信任非常有力量。当时许多 research leads 如今已经在负责 reinforcement learning、character 与 alignment 工作。早期一起在一线深度协作建立的基础信任，也让 product 与 research 如今能围绕任何 production model 顺畅合作。

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 GPT4 at the time was used a bit for coding but it was one of many use cases. 另一个转折是识别出 coding 的重要性。 2023 年我加入时，没有人把 Anthropic、Claude 和 coding 放进同一句话。

And one thing that for example I saw was people are 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 cla enthusiasts and developers because we were uh providing a value that they didn't really think was possible at the time. 当时 GPT-4 等竞争模型已经多少被用于 coding，但那只是众多 use case 之一。我观察到，人们开始用这些模型做的不只是 code autocomplete，而是真正撰写长篇代码。那是否意味着我们可以把 Opus 3 训练得更擅长这件事？从训练角度看，这最终只是相对较小的改动，却帮助我们在早期面向用户实现竞争差异化，也吸引了很多最早期的 Claude 爱好者和 developers，因为我们提供了一种他们当时认为不可能的价值。

It's so interesting you talk about Opus 3 like 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 you all built that wow we could really ship a frontier model which is now today so not great if you compare it to what we've got today. 主持人：很有意思，你谈到 Opus 3，听起来已经是很久以前的事情。很难想象它曾是一个重大转折，所以得知这在内部是个重要 milestone 很有意思。那似乎让你们建立了信心：“哇，我们真的能发布 frontier model。 ”尽管和今天的模型相比，它现在已经没那么强。

What I always think about is Opus 45 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? 我总会想到 Opus 4.5；有趣的是，一年后的又一个寒假，大家都在家、都可以 coding。那也是重大 milestone 吗？

Diane Penn Yeah. Um Opus 45 was definitely another large moment. I think what was magic about magical about Opus 45 is we also now not just had a model but a vehicle which is like a great product experience like cloud 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 cloud code for very for for uh for many months before that. Dianne：对，Opus 4.5 绝对是另一个重要时刻。它的神奇之处在于，我们现在不只拥有 model，还拥有一个载体，也就是像 Claude Code 这样出色的产品体验。团队里常说，要让人们感受到 frontier model 的魔力，就需要 frontier product。在那之前的很多个月里，我们已经感受到 Claude Code 的魔力。

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 agent manner. I think that was the inflection. It was actually both. 但当模型的智能达到一定水平后，用户就能在非常广泛的新 use case 中体验 frontier intelligence，并让它以 agent 方式端到端执行任务。

I I think Opus 45 wouldn't have had that moment without a product like Cloud Code and Cloud Code I think wouldn't have had that type of adoption accelerated without Opus45. So kind of speaking on on this on this thread uh Daario interestingly if you look back at all his predictions he's just like okay coding is going to be solved it 100% in like a year something like that. He kept talking about how we're going to do code like AI is going to do all our code. 我认为这才是转折点，实际上两者缺一不可：没有 Claude Code 这样的产品，Opus 4.5 不会迎来那个时刻；没有 Opus 4.5，Claude Code 的采用也不会加速到那种程度。主持人：沿着这个话题说，Dario 过去的预测很有意思。回头看，他一直在说 coding 会在大约一年内被 100% 解决，AI 会写所有代码。

And I remember everyone uh being like, "There's no way. This is way too complicated. How is how is AI ever going to get really good at this very complex thing that humans do? No, this is going to 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 that we're on. That's the way he describes it now. We're like, we're on the exponential curve. 我记得大家都说：“不可能，这太复杂了。 AI 怎么可能真正擅长人类所做的这种复杂工作？ coding 在很长时间内仍会由人完成。 ”结果他完全说对了。他还经常谈到我们现在所处的 exponential curve。我记得不久前新模型发布时，所有人都说：“好，结束了，已经没有上升空间。

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 a massive jump because we're like on that hockey stick part. 增长在趋平，一切到头了。 ”如今情况恰恰相反。设想 exponential curve，我们现在已经进入它的陡峭部分；从定义上说，每次改进都会是巨大跃升，因为我们正处在 hockey stick 那一段。身处这个疯狂的历史时刻内部是什么感觉？ AI 改进如此迅速，解锁了这么多可能。人们应该怎样为接下来持续加速的 AI 进步做准备？

Diane Penn 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? Dianne：我常对团队说，我们多数人在互联网从新奇事物转变为所有人都能使用的工具时，还没有真正开始工作。类比有助于人类理解。我认为从这个类比能得到几件事。

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 have evals. 第一，adaptability 变得非常重要。我们有 evals；安全侧有 safety testing 与 red teaming；capabilities 和 product 侧有新 prototype，以及 Claude Code、TAG 等产品。

We have you know on the safety side safety testing red teaming on the capabilities and product side new prototypes products like cloud 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. 但很难预测确切时刻或确切模型。因此，当面对新信息时，能否据此做出更好决策，而不是坚持原计划，这种 adaptability 很关键。那种 agility 非常重要。另一部分是，怎样真正从 first principles 出发，推理下一步是什么、它意味着什么。我们该怎样投资新产品？怎样向用户解释差异？

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? 所以，置身 exponential 很大程度上意味着理解这种 pace、理解自己如何运营并做出更好决策。

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 were expecting a few months from now but now the model can actually do uh and work on and actually bring that to user. 然后把 first-principles thinking 用于实际行动：也许某个原计划几个月后才做的项目，现在模型已经具备能力，我们就提前推进，并真正带给用户。例如 Co-work、skills、TAG 等。随着这些能力发展，会形成一种非常积极的 self-reinforcing loop。

So this is things like co-work skills tag, you know, as the it's a very positive self-reinforcing loop. And 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? 我认为其中一个关键也只是相互信任：确保我们围绕正确的决策方式思考，并让所有人共同前进。有些团队比另一些团队更早看到、感受到 exponential；我们怎样给予足够耐心，把不断扩大的组织和公司一起带上？主持人：所以我的理解是，每次模型发布时，你几乎都不知道哪些事情会变得可能。

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 catch up to what is possible. To your point 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. 因此重要的是保持适应性。产品本身也必须追上能力：模型能做很多，但人们未必理解怎样用、也未必能真正做到，所以产品要降低门槛，甚至直接告诉你“这里有件事可以做”。你大致是在描述这个吗？ Dianne：我认为是。原始 scaling law papers 中有一些很有意思的图。

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? 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. 大家熟悉的 scaling laws 视角是：随着加入更多 compute 和 data，所谓 loss，也就是 next-token prediction 的 loss，会下降。它是一条非常平滑的线性曲线，表示模型随规模扩大而变得更智能。论文中另一个同样有意思的部分，是一些截然不同的 emerging capability graphs。例如，随着增加 data 并用更多 compute 训练，某些能力会以不连续方式突然跃升。

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. 比如模型会从连 1+1 都无法计算，突然变成能够可靠计算。这些 emerging capabilities 带有某种不可预测性：并非每个人都知道它出现的确切时刻，你需要 evals 才能评估。

So the models go from 1 + one being a thing that it can't calculate to a thing that it can reliably calculate. 这一直是该技术工作方式的一部分，也让 safety 更困难；如果没有 eval、没有测试系统，这些跃升可能已经发生，而你并不知道。

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 ebells 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 eval unless you have the systems to test um these jumps might actually happen and you don't know M 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 we just got really good at this thing what can we do with that 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 temple and that discovery is actually another part of what's been in the early days of anthropics DNA and I think is also continuing to be a big part of how we operate in product in labs and and across research. 主持人：这太有意思了。你们可能已经开发出一个擅长某件事的 AI brain，自己却还没有意识到。因此工作的一部分就是发现：“哇，我们突然很擅长这件事了，可以拿它做什么？ ”Dianne：用 PM 语言说，即使对今天的模型，也存在 product overhang 和 user overhang。围绕当前 Opus、尤其是 Fable 等模型，我们还有很多可以探索。 discoverability 从 Anthropic 早期就写在 DNA 里，今天仍是 product、Labs 和 research 各处运营方式的重要部分。主持人：这让我想到 YC 总裁 Gary Tan 一直在谈的观点。他说，如果你现在愿意每年花 10 万美元购买 tokens，你过的就是 2028 年某个人会过的生活，因为到那时成本会非常低，所有人都能这样工作。但眼下存在一种 alpha opportunity。

Diane Penn This makes me think about something Gary 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 going to 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. 你可以直接生活在未来，尽情花 token。人们因此有很大机会了解未来是什么样，也能快得多地构建。你怎样看待这种我们姑且称为 token maxing 的做法及其价值？ Dianne：我会更多从 product 视角理解。 token spend 更像 input，而真正的 output 是你描述的 experimentation。

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 of and the value of token maxing, let's call it. Yeah, I think I I take more of like a almost product lens. It's almost like token spin 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. 如果围绕 experimentation 来设定 goals，或许能更好地表达我们要的 outcome；而实现同一个 outcome 可能有不同方法。我要说的是，在内部，一些最有创造力的 thinkers、最优秀的 prototypers，确实会花大量时间使用 Claude，也会使用每个新版本的 research model。

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. 你必须真正使用这些模型，才能想出好主意、很好的主意和更好的主意，这里没有替代方案。当技术变化如此迅速时，不亲手接触技术就很难制定出完美 strategy。

And so there is something around you have to be like using the models to then come up with good then great than 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 internally within anthropic. 但与此同时，我们还可以做其他事情，比如… …我们常做的一件事，是在 Anthropic 内部公开工作。早期 product surface 较少时，有一个 Slack channel，几乎全公司的人都在那里测试 Claude 的早期版本，尝试不同 use case。当时大家不会称它们为 use case，但可能会让它编辑 essay。

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. 或者想出发送某封 email 的合适表达。它们其实都是不同 use case，但所有人都公开尝试。随后你会神奇地看到，团队里不同的人提出一个 idea，其他人再尝试不同 variation；大概十次 request 之后，就会出现某个神奇的结果，甚至涌现一个新 use case。

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. 所以我认为，探索这项技术的使用方式不应只由每个人单独进行。进行 experimentation 时，我们可以做得更多，把这种 communal discovery 带进来。 experiment 并不总是一项个人运动。

I think we could be doing more to actually bring like that communal discovery when we do experimentation. Like experimentation is not always necessarily a individual sport. It's so interesting. 主持人：这很有意思。我们并不确定它能做什么、可以用来做什么，需要反复探索，也要听别人尝试了什么，才能发现可能性。这是一种很有意思的技术状态：“好，我发现它能做这件事，你打算拿它做什么？

Yeah. This idea 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 I don't know technology or just like okay here's what oh I figured out it could do this thing what are you gonna do with that I think at a broad theme we know right we know that the models to write great essays or you 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 or experimentation uh based following this thread you uh you oversee product for the labs team which uh is extremely cool. ”Dianne：从宽泛主题看，我们知道模型能写优秀 essay 或 long-form writing；但具体可以解决哪些个人 pain point，并把能力带到用户实际能用的层面，则更依赖 exploration 和 experimentation。主持人：沿着这个话题，你负责 Labs team 的产品工作，这非常酷。 Ben Mann 和 Mike Krieger 都来过节目，并谈过 Labs。 Labs 到底是什么？它孵化过哪些东西？许多人听说过这些项目。它的工作方式为何能让团队在 Anthropic core product team 之外创造如此创新的 ideas？

We've had Ben man on the podcast, Mike Griger who whom 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 how do they work that enables them to create such innovative ideas outside of even the core anthropic product team. Dianne：Labs 的命题在很大程度上是识别并持续追踪那些不在 core roadmap 中、却可能带来非连续跃升的大型 bets。

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 road map and figuring out is there a there there and also what is the 10x 100x a thousandx of the there there and so for example uh things like cloud code um I think I've heard of uh things like cloud code uh things like uh skills and most recently cloud 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 you can be very strongly held opinion about the theme or the area and then more weekly held about the exact prototype. 我们要弄清这里是否真的存在机会，还要问：这个机会的 10 倍、100 倍、1,000 倍形态是什么？例如 Claude Code——主持人：这个我听说过。 Dianne：还有 skills，最近则有 Claude Design、MCP。我们在团队中特别强调：尤其在现在，能够构建的东西太多了，那么什么才算 discontinuous bet？今年我们采用的一种方式，是对 theme 或 area 持非常坚定的观点，但对具体 prototype 只保留较弱的执着。因此团队有 experimentation culture，也有很强的 bottoms-up 特征。

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 selfable um self-driven to test out different ideas and sometimes uh we have a thesis and 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. 团队里的 engineers 非常自驱，会测试不同 ideas。有时我们有一个 thesis，但它目前还行不通，于是会在一两代模型之后重新审视。即使某个 prototype 没有立刻变成发布产品，只要帮助我们学习，也有价值。我认为，这让 Labs 的 incubation 与 charter 能加速推进，也帮助 Anthropic 更广泛地预见拐点。

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. It's so funny to think about a labs within an anthropic which is 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. 主持人：Anthropic 本身已经如此创新、有创造力，而且发布速度极快，却仍值得在内部设立 Labs，想起来很有意思。你列出的几乎都是 Anthropic 最大的成功项目。是什么让 Labs 运作得这么好？当然，我大概还漏掉了很多。

Diane Penn What enables labs to work as well as it has because you listed all these products and it's let's like what else has anthropic shipped it 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 or within within a larger company? I think that team culture like similar to broadly at anthropic I think that team culture is very valuable. 要在更大的公司内部建立成功 Labs，核心是什么？ Dianne：与整个 Anthropic 类似，team culture 非常重要。 Ben 提出了不可思议的 vision，也会推动大家思考一个 idea 的 10 倍、100 倍形态。 Labs 内的 teams 和 pods 都很小。

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 slowed down because of that. Um so I think it's culture. 有时一个 idea 由一位 engineer 发起。当很大的 team 追逐非常模糊的大型 idea 时，反而可能因此变慢。所以我认为，第一是 culture。我们也会主动选择真正想做 zero-to-one experimentation 的人。这并不容易。很多 bet 最后会被否决或停止，也许未来会重新审视。

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. 但这很难，因为你把全部心力投入其中，像 founder 一样负责一项 bet，结果它目前还无法奏效。因此，需要选择具备这种人格特征、真正热爱并能深入 zero-to-one 的人。主持人：你负责 research team 的产品工作，与 Anthropic 的 researchers 合作。

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. So you lead product for the research team. You work with the researchers at anthropic. A lot of people kind of get an 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. 很多人对 research 和 researcher 做什么有一点概念，却并不真正了解各家 AI lab 里这些非常重要的人。我想帮助大家理解：researchers 每天到底在做什么？

Uh the way I think about it and I want to help people understand help me understand just what are 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? 我想象中，他们会提出一个改进模型的 hypothesis，寻找 data、调整 algorithms 和训练方式，再测试结果、持续迭代，寻找提高模型的方法。大致正确吗？请帮我们理解 researchers 每天的工作。 Dianne：你描述的确实覆盖了很多 day-to-day 工作。但 Anthropic 的 researchers 和 research organization 还有一个部分：对未来的 broader vision。

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. 比如公司创立之初，researchers 就在讨论如何让 Claude 使用 computer、如何让 AI 在 screen 上导航。

So for example things like uh I think even at the founding of the company researchers were talking about how do we get cla to you use a computer how do we get AI to like navigate a screen right so there's a lot of actually very founderlike energy is how I describe it within researchers or 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 cloud 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 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. 所以 researchers 身上有很多我称为 founder-like energy 的东西，尤其是有胆识、有雄心的 researchers；Anthropic 有很多这样的人。一层是设想技术可以走到哪里；另一层则是，当 Claude 已经进入用户手中时，今天怎样让它变得更好。这同时涉及 medium/long term 的未来视角，也涉及 immediate/short term 的改进方向。你描述的很好地反映了如何对 Claude 的不同版本做 iterative improvement。我的团队与 researchers 的合作方式，是高度 integrated 并嵌入这些 loop，尤其关注对用户影响很大的领域。

So this is things like vision, computer use, coding, agent 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 cla.ai. 例如 vision、computer use、coding、agent coding、tool use、test-time compute，都是对用户有直接影响的领域。接下来要弄清楚怎样引入 user feedback，并把它落实成 researchers 能理解、也能采取行动的层次。我认为这是工作的第二部分，而且有时也是困难的一部分。比如我们可能在 Claude.ai 上收到反馈：“Claude hallucinated。

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 it looked at the right document but it looked at the wrong facts. 这非常模糊。如果把它交给 researcher，说“请修复 Claude 的 hallucination”，根本无法采取行动。所以团队需要理解用户为什么给出这条反馈的 trajectory。在获得用户许可后，我们会检查：Claude 当时是否应该调用 tools？以其 current knowledge 来看又如何？或者它确实打开了正确 document，却看错了 facts。

In the first case that would have been a failure on tool use. On the second case it would have been a failure on let's say search or 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 figure out things like evals to then describe how we've improved it like those are the levels of actionability and it's the day-to-day language of the researchers. 第一种情况属于 tool use failure；第二种可能是 search、knowledge/search synthesis 的 failure，也可能涉及 alignment。把问题细化到这种层次，researchers 才能理解它是否足够重要。再设计 evals，用它描述我们是否已经改进，这才达到 day-to-day research language 所需的 actionability。

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 loop. I was talking to someone the other day about how feels like research AI research is uh the place to be now if you want to be very successful in life. 我们努力紧贴这条路径，把 users 的反馈以可操作方式带入 core model training 和 research development loop。

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 going to work this way but just say people are like hey I want to explore this career path from what you've seen what does it take to to make it there researchers generally are research and product managers working with research or both let's do both but uh the researchers like you know PM's working researchers also going to be very successful but it feels like everyone's trying to you know poach all the top researchers across every company so just I I know you're not an AI researcher, but just from what you've seen, just like what does it take to make it in that in that career path? 主持人：我前几天和人聊到，AI research 似乎成了如今想获得巨大成功最值得进入的领域。依你观察，要成为真正成功的 researcher 需要什么？并非所有人都能进入，人的思维方式也未必适合；但如果有人想探索这条 career path，你看到哪些条件？ Dianne：你问的是 researchers、与 research 合作的 product managers，还是两者？主持人：两者都谈，但先说 researchers。当然，和 researchers 合作的 PM 也会非常成功，不过各家公司似乎都在争抢顶尖 researchers。我知道你不是 AI researcher，但依你观察，要在这条路径上取得成功，需要什么？

Diane Penn 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. Dianne：Anthropic 最成功的 researchers 与 research leaders 中，很多都是很强的 first-principles thinkers。

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 eval looking at the underlying data. 他们很善于推理问题，对自己的 research area 充满热情，并能大胆描述它未来的形态；同时他们又非常贴近细节。我们的 leadership、chief scientists，以及 fine-tuning、RL 等方向的负责人，都会非常靠近 training runs。

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 like another piece is just like their ability to think big over time and be like very ambitious, right? 他们会亲自查看 training run 在 eval 上的表现，也会检查 underlying data。始终贴近细节、对细节感到兴奋，是优秀 researcher 和培养 taste 的一个标志。

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 you had to shoot for the stars in in in many ways across u your ideas I think in order to be a a successful researcher 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 and how that's so much of what AI now unlocks. 另一部分是他们能随时间持续从大处思考。也就是保持非常有雄心。比如 Dario 说，我们能改变 software engineering。向这样的方向前进会学到非常多。想成为成功 researcher，很多时候你的 idea 必须瞄准星辰。主持人：我很喜欢“更有雄心”这个 meme，现在出现得太频繁了。它很难做到，说起来很容易。 AI 解锁的很大一部分，就是你究竟敢把目标想得多大：更有雄心。

Just be more ambitious. Yeah. I think it's thinking through it once or twice and to end and then being I think stubborn about the uh area and maybe more uh loose around the exact like approach. Dianne：我认为需要把问题从头到尾想过一两遍，然后对所在 area 保持执着，对具体 approach 则更灵活。

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 uh one thing I ask the team frequently or how I think about when we're building a product is let's say claude 8 comes around what do what changes in what users do and then what should what does that mean for how you're building today? 这也是我们不断挑战自己的问题。技术变化如此之快，怎样确保正在构建的东西 forward compatible？因此，从更大尺度思考其实也属于 core product development loop。构建 product 时，我经常问团队——或者自己会这样思考——假设 Claude 8 到来，users 的行为会怎样变化？这对今天的构建方式意味着什么？它能否与未来体验兼容？ “有雄心”本身很宽泛，所以要尝试用这种具体描述让它落地。

Is it going to 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 and also yeah everything heading in a direction that all is cohesive and makes sense versus just ambitious in a completely different direction. 主持人：而且所有方向还需要彼此连贯、形成整体，而不是往完全不同的方向各自“有雄心”。说到 ambition 和 Claude 8，Fable Mythos 最近似乎让模型进入了一种全新的临界点。过去有了优秀 model 就直接发布：“欢迎大家，Opus 4.5 上线，所有人都能用。

Speaking of ambition and cloud8, 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. Opus45 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, all the companies had to go make sure it wasn't going to hack into all their systems. ”Mythos 却走上完全不同的路径：它遭到阻挡，引发大量审查与担忧。各家公司必须确保它不会侵入自己的 systems。现在感觉每个新 model 都会因为能力持续增强而接受更多审查，也会对使用者施加更多限制。这似乎意义重大。你怎样看待它？它如何改变你们的工作方式？ Dianne：policy 与 export control 部分，我会留给负责这些工作的同事。

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? I'm going to maybe leave the policy and the export control side to to folks that um own that and work on that. 从 product 问题以及我们内部如何与这些模型互动的角度，正如你提到的，随着 frontier models 变得更强，safeguards、red teaming、testing 和 pre-release process 也必须迅速演进并适应。

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. 例如，在 Fable models 之前，我们没有那么强的 fallback UX 和 systems。我们的目标是让技术带来不对称的正向收益，同时尽量减少 downside 或 severe risk。

And so one example is you know before fable models we didn't have as strong of let's say fallback UX's 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 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. And so I think there's a piece around uh as we evolve and like improve safety systems. 因此我们构建了 fallback systems，让 users 仍可从 Opus 4 获得优秀回复。我认为，随着 safety systems 演进和改善，我们还要继续思考怎样开发并交付良好的 user experience。两边都有更多可做。接下来的几周和几个月，你会看到我们持续创新、改进现在所谓的 model safeguards package。

How do we 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. What's really interesting and just like unexpected here is creates this really interesting advantage for anthropic where you have access to the latest stuff and this is going to happen at every lab. 主持人：这里真正有意思、也出人意料的一点，是它为 Anthropic 创造了特别的优势：你们能访问最新能力。每个 lab 都会遇到这种情况；模型持续改进，于是 lab 内部能使用外界因限制暂时无法获得的最佳技术。这会形成一种不公平优势，尽管你们本来更希望所有人都能使用。这将开启一个新的 feedback loop：极先进模型只对某些公司开放，是所有限制带来的意外 second-order effect。

Everyone's going to 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 going to start where models that are so advanced are only accessible to certain companies and that's going to be a whole new unexpect it's like a second order effect of of all these restrictions. Dianne：我们的目标是开发这些 systems 和 models，让它们尽可能 inclusive。对于 general-purpose、general-use technology，我们希望避免这种局面，并提高可访问性。减少眼前的限制，是目前最高优先级之一。主持人：这很合理。我也想象你们会希望尽可能多的 customers 和 users 使用它。

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 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 we're seeing there. Yeah, that makes sense. I would imagine you'd want as many customers if people using this thing as possible. 本期节目由 Mercury 赞助，它提供一种截然不同的 banking experience，受到超过 30 万 entrepreneurs 喜爱，现在还推出了 Command。我使用 Mercury 已超过六年，从未考虑离开。它就像 banking 由 product people 而不是 bankers 构建后的样子。

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 6 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. 它让发送 invoices、转账、为团队成员设置 virtual cards 都变得如此简单，甚至可以说很有趣。你的银行有 API、terminal-native CLI 或 AI-ready MCP server 吗？我猜没有。最近它们推出了 Command，这是一种直接构建在 Mercury 中的 conversational interface，充当你的 financial operator。

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. 我一直用 Command 转账。我还用它找出自己在哪些 categories 花钱最多、分析 cash flow。就在今天，我用它查询过去一年从某个 sponsor 获得了多少收入。我只需问：“过去一年我从 X 赚了多少钱？ ”十秒后就得到答案，真的太酷了。访问 mercury.com 了解更多，只需几分钟即可在线申请。

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. Mercury 是 fintech company，不是 FDIC-insured bank；banking services 由 Choice Financial Group 和 Column N.A. 提供，两者均为 FDIC members。

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 like trending up in what you find is important and what's kind of trending down? 主持人：我想谈谈 product role 正在怎样变化，以及在 AI 成为生活核心的新世界里，谁表现得好。招聘 PM 和 product people 时，你会观察到什么？最看重什么？哪些能力的重要性正在上升，哪些在下降？ Dianne：我的团队三年来一直没有改变 hiring loop。

We actually on my team have not changed our hiring loop uh for three years now. Um so what we actually look for and the traits and how we evaluate uh generalists like PM's generalist like research product managers have actually been the same. 我们寻找的 traits，以及评估 PM 这类 generalists、research product managers 的方式都没有改变。第一项特质是 first-principles thinking。

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 SAS um but actually figuring out in this moment for this user group with this technology what what is the user value is there an example that a lot of people hear first principles thinking they're like yes I about it. I'm good at this. 它不是对自己在 consumer product 或 B2B SaaS 中做过的事进行 pattern matching，而是弄清楚：在这个时刻、针对这群 users、凭借这项 technology，真正的 user value 是什么。主持人：很多人听到 first-principles thinking 都会说“对，我理解，我很擅长”。

What is what's an example of someone having really demonstrated really good first principles thinking? 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 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? 一个真正展示出优秀 first-principles thinking 的例子是什么？ Dianne：通常你会把 product manager 理解为负责 product strategy 与交付 user value，而日常体现则是写 PRD 或 product vision doc。但对我们团队的 research product managers 来说，创造 user value 的方式是找到正确的 user feedback。

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? 也就是找到能代表 user need 的 evals。

because 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 principal 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 so the idea here is used to be have kind of an idea create a PRD talk to people about it. 我们也会写一些 product documents 和 PRDs，但团队里有句话：“evals are the new PRDs。 ”因为要交付那种 user value… …所需的已经不是过去一二十年里人们写的那个固定 artifact，而是一种新的工作方式。 first-principles thinking 意味着：“让我弄清实现目标真正应该做什么”，而不是：“这是我过去做过的一组 activities，所以我会继续做。

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. Maybe maybe step one would be uh understanding the user painoint. And so the way to even access that user painpoint is different, right? ”主持人：所以过去的流程是有一个 idea、写 PRD、与人讨论、对齐 plan、设计、构建、发布、观察结果并迭代。而现在是：“这里有一条关于问题或机会的 feedback。 ”第一步用 eval 来定义工作，而不再是 PRD。 Dianne：也许第一步应该是理解 user pain point。

In the past, we might do a user interview and I think if you go like deep enough, 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 claw being overconfident. 甚至获取 user pain point 的方式都不同。过去我们可能做 user interview；如果足够深入，会让 user 展示自己的 flow 和 pixels。现在，你必须像打磨 pixels 一样认真打磨 tokens。团队的一项活动就是阅读 transcripts。我们深入理解失败 trajectory：这究竟是 hallucination，还是 Claude 表现得过度自信？

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 painoint. failure 的主题中有很多 nuance；把这些搞清楚，才能形成对 pain point 持续、准确的描述。

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 research so then 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. 这个问题随后可以转化成一项新 eval。接着要判断 eval 是否 on-distribution：它是否同时覆盖了问题确实会失败的正向案例，也覆盖了本不应该失败的区域？再把结果交回 research，以便改进，并真正衡量质量：当 Opus 5.5 到来时，这一领域是否改善？ Claude 现在能否识别 document 中正确的位置，并提取正确 synthesis？

So it's just the actionability like and and shortening the distance to actionability um for for our stakeholders and partner teams like researchers um to take action on. 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 most cases? uh what when people want to picture an eval what is that what is what do they picture? We actually uh pioneered this concept within anthropic. 核心就是 actionability，以及缩短 stakeholders 和 partner teams——例如 researchers——到达可行动状态的距离。主持人：有没有一个具体例子，是你发现 issue 或 opportunity 后写成 eval？多数情况下 eval 是什么样？人们应该怎样想象 eval？ Dianne：我们实际上在 Anthropic 内部率先发展了这个概念。

So uh one of the early examples is the early cloud models were not very good at following specific schemas. So like things like outputs and 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, etc. 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. 一个早期例子是，早期 Claude models 不太擅长遵循特定 schema，比如输出 JSON。而如今，这已经是 Claude 成为优秀 agent 的基础。如果不能输出某种 format，就无法访问 API、调用 tools 等。最初的端到端过程是：在 Claude 2 时代，我收到反馈说 Claude 不善于遵循 instructions。

So then digging in with users, what do you mean by claude is not good at following instructions? Give me 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. 于是我们和 users 深入交流：“你说 Claude 不会遵循 instructions，具体是什么意思？发生在哪些 situation？准确的 paragraph 是什么？你问了什么？ Claude 回答了什么？ ”一直追到这种细节。我发现早期大约 80% 的此类 failure，实际是 Claude 没有写出正确 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 could 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 is beneficial because it's identifying a painoint consistently. And so then we added that to our um repositories for evals. 接下来先生成大约 30 到 40 个 Claude 无法正确完成此事的 examples，这就成为 eval set。每个案例可以包含 prompt 和 response。如果输出不符合设定的 golden answer，就说明这项 eval 有价值，因为它能稳定识别 pain point。我们随后把它加入 eval repository。每出现新版 Claude，就运行这项 eval 并检查。

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 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. 如今它大概始终达到 100% 或 99.9%，所以已经不再是 pain point。但在早期，过程就是拿到 user feedback、弄清实际含义、确认能否复现、是否一致、是否足够重要。再把它标准化成 researchers 可以使用的形式。主持人：我们现在生活的世界，基本就是面向 PM 的 test-driven development：先写 test。

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 Enthropic writing bells? 那么，编写 evals 现在是 Anthropic product management 工作的核心部分吗？

主持人 I think so. I I also think it's um something I've talked to other Piana other companies about and I think it's also more and more of the skill set more broadly because a lot of the products that we're building is at the intersection of models with harnesses with a set of contexts for a set of users. Dianne：我认为是。我也和其他公司的 PM 谈过，觉得它正越来越成为一种普遍 skill set。我们构建的许多产品都处在 models、harnesses、特定 contexts 与特定 users 的交叉点。

And so having things like eval isn't is a way not just for uh folks working on models but generally within product uh to to get to better user experiences because 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 and also very non-deterministic which is a big part of this just like it's not going to give you the same answer every time. 拥有 eval… …不只帮助从事 model 的人，也能让整个 product 团队获得更好的 user experience，因为无法衡量的东西就无法改进。其中很多判断仍然很 tactile，也非常依赖 judgment，所以必须贴近细节。主持人：而且它非常 non-deterministic，不会每次都给出相同答案，所以需要更宽泛地描述，不能只做 exact match。

So you got to describe it kind of more broadly. It's not going to be yeah an exact match. So this is a really interesting change in the way product happens and will happen is eval 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 play? Okay, now you're shaking your head. Yes,we we we are we do I think um when there's a very defined problem I think things like eval might be almost a shorthand. 这是 product 发生方式的一个有趣变化：eval 会成为重要组成部分。你们还写 PRD 吗？仍会用 one-pager 描述问题，还是… …好，你在点头。 Dianne：会写。我认为，对于定义非常清楚的问题，eval 几乎可以成为 shorthand；其他情况下，PRD 仍然很有价值。

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 setup goals. PRD 是让一大群人围绕 experience 的共同 source of truth 和一组 goals 达成一致的优秀载体。

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 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. 每一代 model 我们都会有 PRD，不过它未必主要写给 researchers，而更多面向不断增加的 product surfaces。它也面向 engineering teams，以及 legal、safety 等 stakeholders，作为汇总目标的 source of truth，让一大群人朝同一方向划船。

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. 我认为 PRD 另一个有价值的地方，是处理更模糊的 problems 和 opportunities。如果还没有发布过 computer use 之类的东西，就不一定拥有一组具体 user pain points。此时 PRD 的 product vision 部分有价值，可以探索：即使技术还不能对所有人正常工作，怎样先让它对某一群人有效？

主持人 So you can explore the value, you can actually bring something that is uh coherent to a user group. So we do have PRDS. Um I think the application is a little different now. Okay, this is great. There's I just had a uh Andrew for he's the head of the codeex app at OpenAI and he's you guys are aligned. Uh PD is not dead. Still very useful for specific projects and ideas. Uh great. Okay, we've closed closed the book on purity is still kicking. 这样就能探索 value，也能为某个 user group 带来连贯体验。所以我们确实还写 PRD，只是应用方式有些不同。主持人：很好。 OpenAI Codex app 负责人 Andrew 最近也来过节目，你们观点一致：PRD 没有死，对特定 projects 和 ideas 仍然很有用。

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, does something you got to shift or something you look for more people? 这个问题可以结案了，PRD 还活着。我们一直在谈 product people 正在出现的新 skills。除此之外，在这个新 AI 世界里表现出色的 product managers 和 product teams 还有哪些共同 pattern？

I think maybe specifically uh for folks who might be midc career or folks who have been more in a managerial like product like leadership seat. 有没有什么是必须改变的，或你现在更看重的？

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 engineer. and your teams. Dianne：尤其对 mid-career、或更多处于 managerial product leadership 位置的人，我有一个非常强烈的看法：要成为优秀的 team manager，以及与这项技术合作的 PM… …自己必须真正 hands-on。

And so even for folks that I hire who have more tenure 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. 不只是花时间 tinkering，而是确实用这项技术发布过产品，贴近细节，与 PM、engineer 和 team 一起认真打磨 tokens。即使我招聘的是经验更丰富的 PM… …他们的 onboarding plan 也与 early-career 人员完全相同：理解 users、阅读经过许可的 user feedback、与 customers 交流。

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. 你必须 hands-on 地理解怎样使用技术、什么才算 good。如果一个人自己从未体验过构建，就很难对优秀 AI product 或 AI feature 的形态形成判断，也不容易认同它。

You you have to kind of walk in the shoes of your teams. uh and and that's 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 in order to keep 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. 因此我认为… …如果你是 manager，就必须 hands-on，也必须把一部分时间用于真正 shipping，站在 teams 的处境里思考。我总会在 model 项目中留出时间，亲自负责一到两个 workstream，以便保持自己的 theory of mind。

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 going to make it. And you should have fun working with his technology. I think that's the other piece. 我要保持对模型变化方式、改进速度的感知，才能帮助团队做出更好决定。主持人：所以，无论在 company hierarchy 的哪一层，如果不亲自构建、不真的与 Claude 或 Codex 对话并做东西，就无法走下去。

I think the folks that would 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. I 100% know what you mean there. Just like me sitting on my newsletter and this podcast just talking about stuff and like yeah that sounds great. Dianne：而且你应该享受使用这项技术，这是另一部分。无论 level 高低，最可能成功的是那些热爱与 AI 合作、不断 exploration 和 experimentation 的人。

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 where the here's what they're talking about computer use. 他们不仅为 experiment 留时间，也亲手做端到端 shipping 并获取 user feedback；这对每个人都应该是基本要求。主持人：我完全明白你的意思。如果我只是坐着写 newsletter、录 podcast、谈论这些东西，当然会觉得听起来不错。但每次真正构建东西、摆弄各种小项目时，你才会说：“好，我明白现在发生什么了。

Here's what they're talking about with this limitation, this UX situation." Yeah. So, yeah. So, it's just like, and 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, I don't know, it's just so annoying. I just have to do this. I don't know what's so like, I hate this freaking thing. ”很难准确描述亲自与 models 合作、构建产品时的体验；你会进入一个完全不同的世界，真正理解 computer use、某个 limitation、某种 UX situation 指的是什么。你还提出一个很有意思的点：必须从中获得乐趣。

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. I think maybe I'll reemphasize something I said earlier around just that experimentation is not an individual sport. 但很多人做不到，因为他们被要求使用 AI，或根本不知道该拿它做什么，会觉得：“太烦了，我为什么非得用这个？一切变化得太快，我累了。 ”对于怎样找到这份工作的 joy，你有什么建议？

Like some of the moments where I think I've touched practically every version of research models across 20 versions of production clause at this point and I think part of the joy comes from seeing other people discover use cases too. Dianne：我想再次强调前面说过的，experimentation 不是个人运动。我大概接触过每个版本的 research model，也用过迄今 20 个版本的 production Claude。

And so maybe one idea here would be pairing with somebody who is excited and seeing what on a use case that you care about and working together versus um uh identifying or trying to figure out the perfect use case yourself because 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? 部分乐趣来自看到别人也发现 use cases。所以一个可行想法，是和真正兴奋的人结对。选择一个你在乎的 use case，一起动手，而不是独自寻找所谓完美 use case，后者可能会感觉像工作。

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 continue to have joy with with this technology. That's such a good point. 与别人合作往往更像乐趣。我们还能怎样把更多人带进来？内部经常有某个非常好奇的人分享新 prototype 的 idea。这会吸引许多人说：“哦，我不知道 Claude 现在已经能做到这个。 ”于是形成一些 virtuous cycles，让大家继续享受这项技术。

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 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. 主持人：这是个很好的观点。 Twitter 之所以在这方面有用，也因为你能看到别人分享自己的成果。它会启发你产生自己的小 ideas，而且分享自己做出的东西也很有趣。所以应该找些人一起尝试、寻找 use cases。

The thing I've also heard a lot is just find like a problem you want to solve in your life or work and just open up cloud cloud code tell it here's what I want to do and it's incredible how far you can get just with like a vague idea of a problem you want to solve. Yeah. I think it gets hard in that there's so many different things that you could try. Yeah. 我也常听到另一种建议：从生活或工作中找一个想解决的问题，打开 Claude 或 Claude Code，告诉它“这是我想做的事”。即使只有一个模糊问题，能走到的地方也非常惊人。 Dianne：难点在于可尝试的事情太多。

Diane Penn 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 them those things allow you to go deeper rather than like more high level about too many things. 所以需要缩小范围：要么和真正享受这项技术的人结对，要么找到一件可以立刻获得价值的事情。这两种方式都能让你深入，而不是停留在太多事情的表面。我发现自己很难跟上所有 prototypes 或 products 的速度，所以选择深入其中一两个。

Diane Penn 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 myself. That's uh that's so interesting you say that because that's exactly it. 主持人：你这么说很有意思，因为这正是我们刚完成的一项调查结论。

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 and 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 to just ton of little things. find a couple things to really solve well and then go deep and that is 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 versus just like a couple messed up broken half working things. 我和同事 Noam 调查读者面对当前科技与 AI 变化的感受，其中最有意思的发现之一，就是你说的：深入少数几件事，而不是浅尝大量事物。找几个真正想解决好的问题，然后深入，这是快乐的一种来源。很多人的快乐来自终于解锁一种让 AI 真正改善生活的方式，而不是留下几个出错、残缺、半可用的东西。 Dianne：对。关键是怎样让它不再只是 check the box。对 product people 来说，这会变成对时间和精力进行 product prioritization 的练习。

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. 如果 goal 是带着 joy 做 experiment，那需要哪些 inputs？我认为 Anthropic 的 secret sauce 很大程度上是 culture，以及人们 bottoms-up 的工作方式。还有这种 experimenting in public 的方式。

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. 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 going to 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 the actual product to lead in. 这样做本身就在把其他人带进来，最终非常有价值。主持人：我从各个 lab 都反复听到这点：没有人完全确定这些技术会怎样被使用，所以很多工作就是尽早推出、观察人们怎样用、看看哪些事情可行，再利用这些信息构建真正的产品。

Yeah. Yeah. I'm curious how kind of on this thread of finding ways AI for AI to help you in your work in life. Are there any interesting ways you've been using Claude lately in your work as a as a PM? I think there's a lot of things with um you know fable and things like tag. Dianne：对。主持人：沿着寻找 AI 改善工作与生活的方法这个话题，你最近作为 PM 有没有用 Claude 做什么有意思的事？ Dianne：Fable 和 TAG 等方面有很多例子。我认为 TAG 还处在非常早期。

So there there 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 and 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. 这里涉及一种不同工作范式：让 agent 自行离开一段时间去工作，再把 products 和 experiences 带回给你。另一个并非最近才出现、但我经常和团队谈、也认为 AI 可以做得更多的领域，是怎样借助它改善彼此对话、成为更好的 managers。它不只是在提高我们构建的 experience 的 IQ。

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 and 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. 我也常用它准备怎样进行更好的对话，尤其在 crucial conversation 前后。我很喜欢《Crucial Conversations》这本书，所以做了一个 skill，帮助我判断面对当前 situation 时，自己是否进入了恰当的 detail level。它帮助我成为更好的 manager，也更好地支持团队。

Um, so for for like managers on the team, that's actually a thing that I've been sharing more with with a uh with our managers of okay, how how do you actually use use claude to to to make you a better coach because it's hard sometimes to find the right perfect words and the models have a lot of perfect and right words and uh I think there is something about how it can actually augment us from like an ET perspective in addition to you. 我最近也把它更多分享给 team managers：怎样真正用 Claude 让自己成为更好的 coach？因为有时很难找到完全合适的措辞，而 models 拥有很多准确、恰当的表达。我认为它不仅能提升 IQ，也可以从 EQ 角度增强我们。

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 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 tips on how to approach it. 主持人：这里有太多有意思的内容。确认一下你的做法：你让 Claude 构建一个 skill，融入《Crucial Conversations》的 lessons；它对书已有足够了解，你无需提供正文。然后用这个 skill 与 Claude 交流：“我将和同事进行一次非常困难的谈话。

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? ” “给我一些建议，告诉我该怎样处理。 ”Dianne：对。这几乎是一种 individualized、personalized coaching。我们每天经历大量 context switching，Claude 可以与我结对并提供帮助。也许有时我最终不会采纳它的建议。

How do I actually uh go a bit deeper faster? Build trust faster, uh be more direct. Yeah, man. I have so many questions here. This so interesting. Uh one is just like there's concern people are going to start talking the way AI writes because they're talking AI so much and it's going to be like Diane, 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? 但它对 brainstorming 非常有帮助：我是否以正确方式考虑 reactions？怎样更快深入、尽快建立 trust、表达得更直接？主持人：这太有意思了。我有很多问题。其中一个担忧是，人们与 AI 对话太多以后，会开始模仿 AI 的写作方式。当然你不是这样，但这是人们的担心。还有所谓 brain rot、能力萎缩：我们太依赖 AI，于是不再学习、不再思考。

There's this, you know, brain rot atrophy stuff people talk about it. We're just so reliant on AI now and we stop learning and thinking and, you know, overly AI thoughts on that being so close to it and being so integrated with with AI constantly. 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 claw to augment my thinking. 你如此贴近、也一直与 AI 深度整合，对此怎么看？ Dianne：对我个人来说，thinking process 和 writing process 很大程度上结合在一起。我会用 Claude 增强思考，但要确保它不会替我接管全部 thinking。

But what I want to 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 want to 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. 根据 situation，以及其中需要多少 personal judgment，我可能会先形成自己的 POV，再与 Claude 一起推演。同时确保自己的 sense 和 tone 始终保留。

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 cris 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 a 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. 另一些事情，比如 monthly business review，我更希望它标准化，也希望信息能以正确方式 crystallize。我有一个 skill，并且一直增强、改进它。最终我希望 monthly business review 的 writing 可以完全委派给 Claude，因为写作本身可能比 thinking 的价值低得多；我则更多作为信息的 reviewer 和 verifier。所以这取决于你用 Claude 做什么、想传达什么，以及进一步委派是否带来不对称价值。

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 asymmetrical value in in delegating more to Claude. 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 sparring partner almost to evolve the idea push back on the idea. 主持人：我听到的第一条建议非常好：先自己思考并形成观点，再把 Claude 当作 sparring partner，发展 idea、挑战 idea。

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 co-orker, I want somebody to push back when my ideas are not fully formed. Dianne：对。这也说明 alignment research 和 safety research 为什么有帮助。你并不想要一个只会同意你的 AI，而是希望这项技术真正增强并推动你走向更好的 outcome。有时 Claude 的反驳会让我变得更好。就像 coworker 一样，当 idea 还不成熟时，我希望有人提出反对意见。

I want to hear more about that. But I've heard that when Ben man 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 actually 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. 主持人：我想深入听听。 Ben Mann 来做客时谈过 Claude 内置的 constitution。反直觉的是，对 safety、alignment 以及描述 Claude 应如何思考和行动的 constitution 的投入，看起来会限制 Claude，让它没那么有趣，结果恰恰相反。

I hear that constantly. It's just like I much prefer talking to a like open claw famously was built on claude and then people were forced to we won't get into it were forced to switch to 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 want to make sure we spend a little time on. Why is it why is that the case just this focus on alignment safety having this clear constitution? Claude 反而拥有最有意思的 personality。我经常听人这么说。比如 OpenClaw 众所周知是基于 Claude 构建的；后来人们被迫切换到其他模型时会说：“这太糟了，它已经不是我习惯交谈的那个对象。 ”这点很值得花时间。为什么专注 alignment、safety 并拥有清晰 constitution，反而让 Claude 更好、更有意思？

Why does that make Claude better and and more interesting to talk to? Also,

主持人 in order to make Claude as like 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? Dianne：为了让 Claude 尽可能 intelligent、capable，它必须能够在正确的地方提出反对意见，再补充自己的判断，而不是只回答 yes 或 no；这样才能真正帮助你得出更好的 conclusion。我曾用 Claude 帮助思考：下一版 Claude 的 pricing decision 是否正确？

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 task, but figuring out is it doing the right thing. That's actually very integrated with knowing when to push back,right? That's part of knowing when you should be proactive. 这有点 meta：使用 research version 的 Opus，询问它自己应该如何定价，并最终得到更好的 outcome。归根结底，这才是 goal。因此，AI 不应只是 assistant、doer 或被委派任务的对象，还要判断自己做的是不是正确的事。这与知道何时提出反对意见高度结合。主持人：对，这也是知道何时应该 proactive 的一部分。

Proactivity is not a 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 Claw 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 of the models. Proactivity 并不只是按 schedule 完成一件事，也包括知道何时提出新 idea。 Dianne：所以，要让 Claude 更有用，整体 approach 就必须使它知道何时反驳。这与模型其他 characteristics 一起，构成核心。

Diane Penn That is so interesting. It's so interesting that that is what a big part of like it be it being less compliant is almost what makes it better and more useful because we need that. Like I've had so many people where they're like, "Hey, like AI told me I was right." and like no I wish I wish to other people. Yeah. And it comes back to our earlier point around thinking, right? How do you protect your thinking? 主持人：这太有意思了。它没那么 compliant，反而正是让它更好、更有用的原因，因为我们需要这种能力。太多人会说：“AI 告诉我，我是对的。 ”我却希望它去问别人。 Dianne：这又回到前面关于 thinking 的观点：怎样保护自己的思考？如果 AI 能成为 thinking partner…

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. … thinking partner 就不应该只同意你，而应当给你增加东西。和 Claude 合作后，你最终应该拥有更好的 ideas。这才应当是 hero goal，而不只是让原有 ideas 改善 10%。

Yeah, I love this since like it used to be think 10x. I 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 thousandx from this idea? What is the most ambitious version of this? I want to come back to something that I I was thinking about as we were talking about uh talking to Claude constantly. Um it's very clear when AI has written something still. 主持人：我很喜欢这点。过去 founders 常用“think 10x”推动团队：“如果把它扩大十倍呢？ ”现在我反复听到的却是：“怎样从这个 idea 走向 1,000x？最有雄心的版本是什么？ ”我想回到刚才谈到的一件事：我们一直与 Claude 交谈，但现在 AI 写的东西依然很容易辨认。

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 它明明是 large language model，按理说最该擅长写作；有趣的是，AI 写作的痕迹却总是很明显。你认为将来会到达无法判断是否由 AI 写成的阶段吗？

主持人 I think it depends on what's the uh goal that you're looking to achieve by knowing 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 you Lenny or me Diane. Dianne：我认为这取决于你想通过“知道作者是谁”实现什么 goal，也就是 eval 是什么。我确实认为，我们还能进一步改善 Claude 的写作。我的团队和 research 侧都在非常积极地努力，让 Claude 写得更好。一般来说，我认为一个 idea 是由你——比如 Lenny 或我 Dianne——主导的，应该清楚可见。

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. Uh, 这取决于 writing 的 goal。对于 monthly business review，我其实很愿意让 Claude 端到端写完。

Diane Penn and obviously and not 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. 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 the output. Right. 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. 主持人：而且不需要假装是人写的。你提出了一个很有意思的问题：也许我们知道它由 AI 写成，反而更好？ Dianne：对。不过观察角度也许更应围绕 verifiability：谁在验证 output？主持人：对，也就是谁签字确认。也许“谁在验证、谁签字确认”比“谁写的”更重要。主持人：你觉得 AI 为什么不擅长写作？

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 just there's only so many ways to write. And so we've just recognized, okay, this is what 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? 我的猜测是，它学习了人类全部最佳写作，找出了所谓最佳表达方式，但写法毕竟有限，于是我们只是识别出：“好，这就是 AI 的写法，它总使用这些 trope。 ”这是核心原因吗？还是另有原因阻碍它成为优秀 writer？讽刺的是，它明明是 large language model，按理说最应擅长 language。

Ironically, being a large language model of all things, you think it'd be really great at language. I think part of it is 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 like the technology is jagged edged 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. Dianne：部分原因是，我们仍需投入更多 training improvements，让 AI 在 writing 等领域持续变强。技术也像前面说的那样具有 jagged edge。以前 models 擅长 writing，却不够 agentic，所以我们的 thesis 是让模型更 agentic、更会调用正确 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 a very active area for us that you mentioned. Okay. I'm glad I'm glad. 现在这方面改善后，其他 areas 又显得像 rough edges。 writing 正处于这样的时刻，我们需要真正聚焦并把它设为训练重点。你提到的 writing 确实是我们非常活跃的工作领域。

And also uh it's going to 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 want to 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 is down and we're just like, "What the hell? The Wi-Fi is not working on this plane. 主持人：很好。我很期待 AI 好到让我们无法判断作者是谁的时刻。不过正如你说的，有时我们确实想知道它由 AI 生成；这个角度我以前从没想过。这里还有一个有趣的类比：有位 comedian 讲过，大家坐在飞机上，Wi-Fi 一断就抱怨：“搞什么，飞机上的 Wi-Fi 竟然不能用。

The 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 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 possible. And so uh basically AI writing has been not the priority and it feels like there's more investment happening there. Yeah, I think like tone and character is a priority. ”可你其实正坐在天空中的金属管里，像鸟一样飞行，却还敢抱怨 Wi-Fi。你的观点也是：技术已经带来如此多进步与力量，我们不可能一次把所有问题都修好、让一切达到最佳。因此 AI writing 过去不是首要任务，现在似乎正在增加投入。 Dianne：对，我认为 tone 和 character 是 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 and I think once we improve let's say writing and like tone and character uh we probably will say like 技术进步仍在进行。我们看到 agentic behaviors 出现一次跃升，这成为 new normal，接下来其他 capabilities 也必须继续改善。我认为，一旦 writing、tone 和 character 提升后，我们大概又会问… …

主持人 how do we have Claude be even more proactive like productivity 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 I I think we're applying it to AI which is the right thing. We should be making it better. I want to ask you a couple questions I'd like to ask folks working at the very center of the future of that is coming. “怎样让 Claude 更 proactive？怎样提高 productivity？ ”人性就是如此：我们想让自己变得更好，也想让技术变得更好。把这种要求应用到 AI 是正确的，我们应该持续改善它。主持人：我想问几个会问身处未来最中心的人才会问的问题。第一，未来几年 human brains 在哪些地方仍最有价值？

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 a GI 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've approached that that timeline? We started to talk about making claude and models better at judgment um especially in the last um year or so. 我知道 Anthropic 的 mission 与 vision 包括达到 AGI 和 superintelligence，也许到了未来答案会是“无处”。但在那之前，随着我们接近那条 timeline，你认为 human brains 在哪里仍最有价值？

主持人 I think judgment is one and is an area where it's an 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. Dianne：我们刚开始谈如何让 Claude 和 models 更擅长 judgment。尤其过去一年左右，我认为 judgment 是一个重要领域。它累积了大量 nuance 与 experience，而这些 systems 的经验还没有人类那么多。

There are so many things AIs can build. which one are the things that you know an or like lab should build right a lot of that requires like human judgment persistence so proactivity these are all traits that 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 the best experiences so I think those types of traits are actually the tactile uh traits that I think will uh continue to be important. 因此，那种来之不易的 judgment… …对 product leaders 和所有人仍会非常关键。 AI 能构建的东西太多，但一个 organization 或 lab 应该构建哪一个？这很大程度上需要 human judgment。 persistence、proactivity 等也都不只是 general capabilities。

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. 它们还涉及人的 behaviors 与 characteristics：如何得到最佳 solutions，如何创造最佳 experiences。我认为这些 tactile traits 仍会重要。此外，capabilities 与 subject-matter expertise 也还有很大价值。

And so um I think you're seeing us ship things like cloud science investing in these areas because those are areas that um I think is just bring the this technology to society and having a positive benefit for society. So I think there's a lot more to go there. Another question I want to ask is um as someone with kids, how do you think about what you are encouraging them to learn? software engineering 已被 AI 深刻改变，但 biology、life sciences 等领域还不同。 software engineering 也许已进入 exponential，而其他一些领域才刚站在 exponential 的起点。

or do you think you're gonna nudge them to be successful in this wild new world that we're entering? I actually think it's a lot of the same traits like you and I probably grew up with, 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 8-year-old. 你会看到我们发布 Claude Science，并在这些领域投资，因为把技术带给社会、产生正向社会收益，正有大量空间。主持人：另一个问题是，作为有孩子的人，你会鼓励他们学习什么？怎样引导他们在这个狂野的新世界里获得成功？ Dianne：我认为，仍然是你我成长时拥有的那些 traits：对学习的 curiosity、persistence、培养并相信自己的 inner voice。

It's on us to help uh it's on me to help them develop their indoor voice and whether that's being opinionated and taking a stance to me right and developing that encouraging that uh I think that those types of skill sets are things that um is important in the future and like having their own individual voice. That is so interesting. 我有一个四岁的孩子和一个八岁的孩子。我有责任帮助他们发展 inner voice。无论是坚持观点、甚至对我表明立场，都要发展并鼓励这种能力。

It's so related to the answer you had when I asked about how to avoid a brain rot essentially and overrelying on AI which is just keep focused on your own point of view and your own perspective before you overly 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 this all this kind of connects judgment persistence in a point of view of your own. Yeah. Both for kids and also adults. 我认为，这些 skill set 与拥有个人 voice 在未来都很重要。主持人：这和你回答如何避免 brain rot、过度依赖 AI 时的观点非常相关。重点是先保留自己的 viewpoint 和 perspective，再过度借助 AI。你描述的在孩子身上培养这种能力非常重要。 judgment、persistence 与自己的 point of view 全都连到了一起，无论对孩子还是成年人。

Yeah. Anything we think about um for your Oh man. 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, but you know, how do you get how do you onboard them to this crazy thing? Dianne：对。你会怎样考虑自己孩子的情况？主持人：我在想的问题是，什么时候让他们接触某种 AI。我有一个三岁的孩子，现在还很早，但以后怎样让他们进入这个疯狂世界？

I had I was at an event recently and bunch of parents were talking about how they think about AI in 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 all the answers immediately. thought that was interesting. Like an open source local model,not stable. 我最近参加活动时，一群父母讨论孩子和 AI。有个人提出一种很有意思的做法：让孩子一直使用非常早期的 models，这样他们仍需费力思考，不能立刻得到所有答案。我觉得很有意思，比如使用不太稳定的 open source local model。

Diane Penn Yeah. And curiosity is something uh I I keep mentioning Ben man, but his answer actually to this question has always stuck with me, which is um curiosity and also just like he's a big fan of Monosuri, which is what I'm we're encouraging for our kids. So, there's something there. Maybe a last question just along kind of along these lines, something Fiona Fun actually suggested to ask you uh who's recently on the podcast. Dianne：对。说到 curiosity，我又要提 Ben Mann。他对这个问题的回答一直令我印象深刻：curiosity，而且他非常喜欢 Montessori，我们也在这样引导自己的孩子。所以这里确实有值得思考的东西。主持人：沿着这个话题，也许最后再问一个由最近来过节目的 Fiona Fung 建议的问题：你怎样恢复精力？

How do you stay just recharged and not burn out being in the center of this crazy storm of AI as a mom uh working in, you know, we're seeing the research work at Enthropic. 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. 作为母亲，又身处 AI 风暴中心、负责 Anthropic research 相关工作，你怎样避免 burnout、保持充电和理智？这是前所未有的时代。我只是在 Anthropic 外面都觉得疯狂，无法想象内部是什么样。

Um what have you learned about avoiding burnout, staying recharged, staying sane during the middle of all this? In 2024,we shipped four models for the in the whole year or four series of models and I think we did more than that volume in just Q2 of this year. I think I've been really lucky with uh the team that we grown and built both the stakeholders on the research side and within our research product management team. 你学到了什么？ Dianne：2024 年全年，我们发布了四个 models，或者说四个 model series。而今年只在 Q2，我们的发布量就已经超过那个数字。我非常幸运，我们在 research side stakeholders 与 research product management team 中建立并发展出优秀团队。

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 high performance sport because 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. 面对这一切时，一个神奇之处是，它不是个人运动。这里有 radical ownership 和 team collaboration。有时确实像 high-performance sport，因为你参与的都是非常关键的 decisions。

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, night before a launch, even if they're not the core DRRi on that model, will stay up and help the DRRi, 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. 你会不断得到关于 users 和 training 的新信息，又必须迅速给出 recommendations、judgments 与 decisions。没有人能独自可持续地做到这一切。真正帮助我的，是有一支了不起、彼此照顾的团队。即使在发布前夜，某个人不是该 model 的核心 DRI…

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. Uh 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 mindmelding. We called it like entering the hive mind. …也会熬夜帮助 DRI、review blog post、修改内容、想出更好的 demos，成为彼此额外的一双手。如果独自把所有变化扛在肩上，很容易感到孤立，觉得什么都必须自己做。但 Anthropic 的一个神奇部分… …就是我们能找到帮助彼此的机会，并再进一步实现 mind melding，我们称为“进入 hive mind”。

There was an 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. 曾有文章写过这件事。我认为，这也让 team 能够补充能量。并不是你休 PTO 后回来就面对三倍工作。我六月刚休过 PTO。

It's actually that you can 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 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. 你可以休 PTO，并确信 team 会做出正确判断，成员之间也会彼此照顾。我认为这是很大一部分原因。个人层面我也很幸运，伴侣非常支持。这是我在 AI 工作的第六年，先在 Amazon，后来在 Anthropic。他看到我多么热爱这项技术、相信它能做到什么。这从个人层面也帮了我很多。主持人：我很喜欢这些答案之间的联系。

I love I love how many of these answers connect. So what I'm hearing here is just the having other people, working with other people, relying on other people, helping each other out when things get crazy. Uh which is a similar answer you had for just how to how to find the joy and and and fun in this work. Just get be inspired by other people, see what they're doing,work together. 你的核心是和别人一起工作、依靠别人、在事情疯狂时互相帮助。这与你回答怎样从工作中找到 joy 和 fun 时很相似：从别人那里获得启发、观察他们在做什么。 Dianne：一起工作。主持人：Fiona 最近来节目时，我问 software engineering 世界发生了什么变化。

Yeah. 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 lonier 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. 她指出，现在工作变得孤独得多，因为大家在与 agents 而不是其他 humans 合作；teams 变小，人们不断和自己的 fleets 对话。所以这也提醒我们，身边真实人类的力量仍然很大。

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. Dianne：技术带来更大 scale，因此我们被要求工作并对非常重要的事情做决定。如果有其他人能在某种程度上与你 mind meld，理解你做什么、如何处理——未必掌握每个细节，但理解 first principles 和你采用的 assumptions——他们就能替你补位、挑战决定并磨砺你的 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, hiring like is this person going to care about their own ego and building out a big org or are they going to care about contributing to anthropic and contributing to the like impact of the team and orienting 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. 所以建立、扩展和招聘 team 时，我非常看重这一点：这个人是在乎自己的 ego 和建立庞大 organization，还是在乎为 Anthropic 与 team impact 作贡献？我们更倾向 low-ego、team-oriented 的人。我认为这是 sustainability 的重要部分。

Yeah, just always a lot of it 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 something shipped every day of the month. 主持人：很多事情最终都回到 culture 和 hiring。

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 is so aligned around the mission and the values it allows people to make decisions really quickly before we get to our very exciting lightning round. Is there anything else Dan that you wanted to share? Anything else you wanted to touch on? Anything you want to maybe double down on of things we've talked about? 我多次听说，Anthropic 之所以能行动如此快——我记得有一个月几乎每天都发布东西，甚至有人做了 launch calendar，大家都在问这怎么可能——一个重要原因就是所有人高度认同 mission 和 values，因此能迅速做决定。进入激动人心的 lightning round 前，还有什么你想分享、补充，或对前面观点进一步强调的吗？

This was actually really fun because I feel like your questions actually sharpen some of my thinking around how the dots kind of connect. I'm I'm your real human claude over here. Dianne：这次交流真的很有趣，因为你的问题让我更清楚地思考各个点之间怎样连接。主持人：我是你的真人 Claude。

One thing that I really uh want to like convey or um have people take away is I think one in the ways of working, but also just two that like 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 a core of a product person and I actually think we need more of that. Dianne：我很想传达的一点，首先是工作方式；其次是，眼下存在大量 growth 和 change，使用这项技术时的 joy 很重要。如果此刻你已经没有最初那种 joy… …而这又是你感兴趣、想从事的领域，怎样找到仍拥有 joy 的人？同时也要发展、补充 skill set，比如从 first principles 出发思考要解决什么。你没问这个，但社区里存在一个问题：models 如此 capable、engineers 又在积极使用，我们还需要 PM 吗？我认为，那些以 user 为中心、深入细节理解 users 想实现什么，再把它提炼成可行动形式，并坚持不懈完成这项工作的人，就是 product person 的核心。我其实认为我们需要更多这样的人。

I think 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 as part of the culture and hopefully that's valuable for others as well. Amazing. What an inspiring way to end it. Oh man. Yeah. 现在越来越由 technology layer 驱动，而要把技术真正变成 impact，就必须深入、好奇、极其 hands-on。我认为这些 traits 也帮助 Anthropic 发展 product 与 model，并成为 culture 的一部分。希望它们对其他人也有价值。主持人：太棒了，这是一个鼓舞人心的结尾。

And this is I've been saying this too for a long time just now that building is easy the hard part part becomes as you said what should we build and is the thing we have built correct and good and worth leaning into and to me that's what PMs do and what PMs are good at. Yeah. Yeah. Yeah. And it's getting into the details of the user. 我也说了很久：现在 building 变得容易，困难转向了“应该构建什么”“已经构建的东西是否正确、优秀、值得继续投入”。对我而言，这正是 PM 的工作，也是 PM 擅长的事情。 Dianne：对，还要深入 users 的细节。

主持人 Yeah. Empathy. Okay. Great. PMs are going to make it. Okay. PRD is not dead. All kinds of all kinds of uh important lessons here. Uh Dan, with that we've reached our very exciting lightning round. I've got five questions for you. Are you ready? Yep. First question. What are two or three books that you find yourself recommending most to other people? One personal one I really like how to raise an adult. 主持人：也就是 empathy。很好，PM 不会消失，PRD 也没死。我们得到了各种重要 lessons。Dianne，下面进入激动人心的 lightning round，我有五个问题。准备好了吗？Dianne：好了。主持人：第一，哪两三本书是你最常向别人推荐的？Dianne：一本偏个人生活的书是《How to Raise an Adult》。

So uh I'm a mom. I think a lot about what is the things that I want to instill in in my kids. in 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 want to hone and like harness and foster in our kids? Um the other book that I uh was listening to on Audible recently is Incorable by Eric Reese. 我是母亲，经常思考想在孩子身上培养什么。这本书很有帮助，它指出，我们不是在抚养 children，而是在培养 adults。这个 framing 会让你思考它究竟意味着什么，以及应该磨炼、利用和培育孩子的哪些 characteristics。另一本书是我最近在 Audible 听的 Eric Ries 的《Incorruptible》。

So the Incorruptible Incorruptible Yes. Yes. Yeah. His recent podcast guest. 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. 主持人：《Incorruptible》。对。 Dianne：他最近来过节目。怎样建立 great company 是个重要问题，而我个人更着迷于怎样让优秀 teams 和 companies 长久发展。

主持人 Um I loved some of the examples around having metrics around culture. you if you can't if you only measure revenue and then that's kind of how you're going 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. 书中对这个问题的 framing 与 reframing 很有意思。我很喜欢其中关于为 culture 设置 metrics 的例子。如果你只衡量 revenue，组织就只会追逐它；拥有其他更好的 metrics，才能维持自己重视的 values。我一直在思考怎样把它带到 team level：如何更好地表达我们的 norms，以及前面谈到的许多东西。

So I think that's also a really good read. There you go. Uh that'll be your next watch everyone as you're listening to this the Eric Greece episode. Yeah. Such a good episode. Yeah. And his book just came out. Incorruptible. Yes. And I think it was like a New York Times bestseller. Like it's actually doing incredibly well, which I was really happy to see. Yeah, exactly. Next question. Favorite recent movie or TV show you really enjoyed. 所以这也是一本很好的书。主持人：大家收听本期后，下一个就去看 Eric Ries 那集。他的书《Incorruptible》刚出版，而且好像成为了 New York Times bestseller，表现非常好，我很高兴看到。下一个问题：最近最喜欢的 movie 或 TV show 是什么？

Most people at Antropic don't have time to do what to watch things, but I'm curious if you have an answer. I would say um during uh some time off last month, I did get to like binge watch Fallout on Amazon Prime. So that was actually I kind of like um it's kind of uh Have you heard of it? Yeah. Yeah, it's based on the video game. Yes, it's based on the video game. Uh I think it's a it was really um it's witty, it's humorous, it's also like super actionoriented. 大多数 Anthropic 员工大概没时间看。不过我很好奇你是否有答案。 Dianne：上个月休假时，我确实在 Amazon Prime 一口气看完了《Fallout》。我很喜欢它。你听说过吗？主持人：当然，它改编自 video game。 Dianne：对。它很机智，也很幽默，同时还有…

So highly recommend. Okay, next question. Do you have a favorite product you recently discovered that you really love? I really do think like claw tag is very interesting in terms of a product experience. Um, we actually have like different versions of this uh within Anthropic and I I I think it's actually been really uh really really uh powerful tool. Yeah, it feels like I think some people are like what's the big deal? …很强的行动导向，所以非常推荐。主持人：下一个问题。最近发现并非常喜欢的 product 是什么？ Dianne：从 product experience 来看，我确实觉得 Claude TAG 很有意思。 Anthropic 内部实际上有不同版本，我认为它是一项非常强大的 tool。

The fact that everyone at Anthropic is like raving about it tells me something important is going on here. And I'm trying to actually get it working within my Slack community that I have for paid newsletter subscribers. How cool would that be? Yeah. I'm trying to figure out how it works when it's not a company when it's just a bunch of people that don't know each other and how that might work. But we're trying it out. Okay. Uh two more questions. 主持人：对，有些人会问“这有什么大不了”，但 Anthropic 每个人都对它赞不绝口，说明这里正在发生重要事情。我正尝试让它在付费 newsletter subscribers 的 Slack community 中运作，那会多酷？我在研究，当使用场景不是 company，而是一群彼此不认识的人时，它该怎样工作。

Your favorite life motto that you find yourself often coming back to in work or in life. So I was actually raised by my grandparents uh for the first 10 10 years of my life and my parents were immigrant uh college and master students in the US. Oh wow. And um my grandfather always says, "No matter how far you go, there's always another level, which uh um is I think um a really good way though, like a pretty uh intense way of describing uh his his life philosophy. 我们正在尝试。还剩两个问题。你在工作或生活中经常回想的 favorite life motto 是什么？ Dianne：人生最初十年，我其实由祖父母抚养；父母当时是在美国读 college 和 master's 的 immigrants。主持人：哇。 Dianne：我祖父总说：“无论你走得多远，总有更高的一层。

But I go back to that whenever there's something new or unprecedented that we experience. And I think you know first half of this year there was definitely a lot of that like there was a lot of new things that we were learning. I was learning um so just feeling like there's always like another mountain another uh opportunity to not good enough Dan we need to go better ”我认为这是描述他 life philosophy 的一种很好、也相当严格的方式。每当遇到前所未有的新事物，我都会回到这句话。今年上半年确实有很多这样的时刻，我们学到了许多新东西，我自己也在学习。总会有另一座山、另一个机会。主持人：还不够好，Dianne，我们需要做得更好。

Diane Penn we need to go bigger. Uh makes me think about actually another Ben man line from his podcast episode that this is the most normal it's ever going to be. It's only going to get weirder and crazier. Yeah. Yeah. No, we're good. Okay, final question. Uh, I was poking around at your LinkedIn. You were a high yield bond trader, JP Morgan Chase early in your career. Uh, you had like uh you have this like redacted uh hundred million dollar trading portfolio of some kind. 我们需要想得更大。这让我想到 Ben Mann 在节目里的另一句话：“这是未来最正常的时刻，之后只会越来越奇怪、越来越疯狂。 ”Dianne：对。主持人：最后一个问题。我看了你的 LinkedIn，职业早期你曾在 JPMorgan Chase 做 high-yield bond trader，还管理过某种被隐去具体数字的上亿美元 trading portfolio。

Uh what did you learn from that time in your life that has stuck with you and or is there a crazy story from that period? It was four years of your life. I think I learned actually a lot that I uh apply here uh at at Anthropic and other uh jobs thereafter. Um so when I was at JP Morgan um the trading floor you could kind of envision like sort of Waffle Wall Street that's very different. Uh most traders I think are in front of a terminal. 那段四年的经历给你留下了什么一直沿用至今的经验？或者有没有什么疯狂故事？ Dianne：我确实在那里学到很多，后来在 Anthropic 和其他工作中都会应用。 JPMorgan 的 trading floor 也许让人想象成《The Wolf of Wall Street》，但实际非常不同。

They're much more doing analyses uh on their computers. Um, but it's still very, I would say, like male-dominated. And so, uh, I was the only woman. I was the only, uh, um, person with like my background, uh, on the trading desk. 大多数 traders 都坐在 terminal 前，更多是在电脑上做 analyses。不过那里依然非常 male-dominated。

And I learned that the it was a very good environment to kind of building one my sense of authentic self and two uh that even if I was the most junior person, even if I may look different, uh that the best ideas and having conviction in the best ideas uh irregardless of all of those other factors like is the most important thing. And so I think just bringing that sense of um how I show up more at work. Um I'm pretty vulnerable and authentic with my team. 我是 trading desk 上唯一的女性，也是唯一拥有我这种 background 的人。这个环境很好地培养了两点：第一是对 authentic self 的认识；第二是，即使我是资历。最浅的人，即使外表不同，最重要的依然是最好的 ideas，以及对这些 ideas 保持 conviction。所以我把这种做自己的方式带进工作。

Uh I try to really make sure that regardless of people's levels or tenures, if they have a great idea, how to help them pursue that and to do also the same. Um so to like put the idea out there to actually um have conviction in it to do the follow through to do the like nitty-gritty work to make it happen. Um so those were all things that I learned from trading. Um and yeah I think applies to any any job in many ways. That is beautiful. 我对 team 相当坦诚、真实，也努力确保无论一个人的 level 或 tenure 如何，只要他有 great idea，就帮助他推进；我自己也同样去做。也就是把 idea 提出来，真正对它有 conviction，持续 follow through，并完成使它落地所需的琐碎细致工作。这些都是我从 trading 学到的，我认为在很多方面适用于任何 job。

Where can people find you online if they want to follow you and how can listeners be useful to you? 主持人：说得很好。人们想关注你时，可以在哪里找到你？ listeners 怎样能帮到你？

主持人 I don't have a large presence on like uh social. Uh I think the best way to uh find my work uh my team's work is really uh the anthropic blog and when we're publishing new models, new product experiences I think in terms of uh useful uh for me I think the best thing number one is your feedback like we actually if if you thumbs up or thumbs down on any of our product surfaces if you contact your salesperson with feedback back about the model, it will make its way to me. Dianne：我在 social 上并不活跃。找到我和团队工作的最好方式，是关注 Anthropic blog，查看我们发布的新 models 和新 product experiences。至于能帮到我的事情，第一就是你们的 feedback。在任何 product surface 上点 thumbs up 或 thumbs down，或者联系 salesperson 提交对 model 的 feedback，最终都会传到我这里。

Uh we actually with every like research model, I actually get pretty close into understanding favorability and feedback. Um so giving us that feedback, pushing Claude, telling us where it's falling down, um those help us make Claude better. Uh the other the other thing is like if you have folks in your network who seem like this type of profile of person that I just talked about I'm hiring the team is growing. 每一个 research model，我都会很深入地了解 favorability 与 feedback。所以请给我们反馈、挑战 Claude、告诉我们它在哪里出错，这些都会帮助我们让 Claude 变得更好。另一件事是… …如果你的 network 里有人符合我前面描述的 profile，请推荐给我们。我的 team 正在招聘和扩张。

We really will love just people who love this technology who are deeply curious first principles thinkers who are fearless in questioning assumptions um and who have like a tinkering hackery spirit. Wow dream job. So basically open open PM roles add anthropic on the research team. 我们非常欢迎热爱这项 technology、极其好奇、善于 first-principles thinking、敢于质疑 assumptions，并拥有 tinkering、hackery spirit 的人。主持人：梦想工作。也就是说 Anthropic research team 有 open PM roles。

Diane Penn Yes. And they apply I assume on the website the careers page. Yes. Holy moly. All right. Here we go. Enjoy the flood of resumes you're about to receive. Thank you. Uh Dan, thank you so much for being here. Thank you so much for having me. Thank you for um really helpful, thoughtprovoking questions um helping me even connect the dots on how how we work, how how this whole technology is coming together and being product people in it. I really appreciate that. Dianne：对。主持人：我猜通过 website careers page 申请？ Dianne：对。主持人：天啊，准备迎接马上涌来的 resumes 吧。 Dianne：谢谢。主持人：Dianne，非常感谢你来。 Dianne：也非常感谢你的邀请，谢谢这些很有帮助、发人深省的问题。它们甚至帮助我把我们的工作方式、整项技术如何汇合，以及 product people 在其中的位置串联起来。

But thank you Dan for real. Okay. Well, bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcast, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennispodcast.com. See you in the next episode. 主持人：我很感激，也真心感谢你。好，再见，各位。谢谢收听。如果本期内容对你有价值，可以在 Apple Podcasts、Spotify 或喜欢的 podcast app 上订阅。也请考虑评分或留下 review，这会帮助其他 listeners 找到节目。你可以找到所有往期节目，或进一步了解… …节目，请访问 lennispodcast.com。下期再见。