Anthropic 如何构建产品：Mike Krieger 谈智能体时代的工程、组织与决策

简要描述Brief Description

Instagram 联合创始人、Anthropic 技术团队成员 Mike Krieger 探讨了能力日益增强的模型如何改变团队构建产品的方式。在 AI Engineer 的对话中，他谈到了如何委托成果而非任务、将智能体系统用作主动型队友、规模化与评审实践、Anthropic Labs 的运营模式、初创公司和垂直 AI 的机遇，以及如何在快速发展的行业中保持清醒的判断。Mike Krieger, co-founder of Instagram and a technical staff member at Anthropic, discusses how increasingly capable models are changing the way teams build products. In conversation at AI Engineer, he covers delegating outcomes rather than tasks, using agentic systems as proactive teammates, scaling and review practices, the operating model of Anthropic Labs, opportunities for startups and vertical AI, and how to maintain perspective in a fast-moving industry.

目录Table of Contents

• 从产品领导力到与模型共建
  From product leadership to building with models
• 要求不合理的结果
  Asking for unreasonable outcomes
• 发布、迁移和运营控制
  Shipping, migration, and operational control
• 将智能体视为队友，并将代码审查视为意图审查
  Agents as teammates and code review as intent review
• Anthropic Labs 如何组织各项押注
  How Anthropic Labs organizes bets
• 产品简化、初创企业与垂直领域 AI
  Product simplification, startups, and vertical AI
• 在快速发展的领域中保持可持续地工作
  Working sustainably in a fast-moving field

从产品领导力到与模型共建From Product Leadership to Building with Models

主持人：Krieger 共同创办了 Instagram，如今在 Anthropic 工作。随着内部模型不断改进，他对模型的使用方式发生了怎样的变化？Host: Krieger co-founded Instagram and now works at Anthropic. How has his model usage changed as internal models have improved?

Mike Krieger：这种变化既来自模型，也来自他自身角色的转变。他在 Anthropic 任职的头两年担任首席产品官。他尽可能多地使用模型——例如，撰写一份产品战略文档，然后让 Claude 对其进行批评——但这与直接使用模型进行构建有所不同。他利用周末进行实验，最终决定，当下的时机太有意思了，不能置身于这项工作之外。他还在其他地方看到过类似的趋势：曾担任 CTO 的人正在选择在 Anthropic 和其他公司担任个人贡献者职位。Mike Krieger: The change came from both the models and his own role. For his first two years at Anthropic he was Chief Product Officer. He used models as much as possible—for example, writing a product-strategy document and asking Claude to critique it—but that was different from building directly with the models. He spent weekends experimenting and eventually decided that the moment was too interesting to stay away from the work. He has seen a related pattern elsewhere: people who had been CTOs are choosing individual-contributor roles at Anthropic and other companies.

他的转变发生在 Anthropic 开始获得后来成为 Mythos 和 Fable 的内部快照之际。重要的变化在于：不再是在脑中拆解一个想法，按照工程步骤逐一执行并迭代，而是描述期望达成的目标，然后让模型朝着这一目标努力。这样一来，人类和模型便可以讨论权衡、提出问题，并评估最终完成的工作。His move happened around the period when Anthropic began getting internal snapshots of what became Mythos and Fable. The important shift was from decomposing an idea in his head, following the engineering steps, and iterating through each one, to describing the desired goal and asking the model to work toward it. The human and model can then discuss trade-offs, surface questions, and evaluate where the work landed.

这需要养成一种新的委派习惯。Fable 可以在交付工作时说明它所做取舍，而 Krieger 有时必须让它用更简单的方式解释这些选择。核心变化在于明确最终状态，然后让系统自行解决问题，而不是指挥每一项具体任务。That requires a new delegation habit. Fable can return work with an account of the trade-offs it made, and Krieger sometimes has to ask it to explain those choices more simply. The central change is to specify the end state, then let the system work on the problem rather than directing every individual task.

要求不合理的结果Asking for Unreasonable Outcomes

主持人：前一位发言者敦促人们“要不讲理”。Krieger 在提示词方面在哪些地方变得更加雄心勃勃？Host: A previous speaker urged people to “be unreasonable.” Where has Krieger become more ambitious with prompting?

Mike Krieger：他喜欢这种表述方式。Labs 的一项计划涉及一个没有按照某人的期望运行的内部产品。他没有把这一请求视为只能由开发人员单独处理的事情，而是意识到可以教会那位非技术人员直接向 Claude 提问。产品团队和整个行业都需要教人们，在使用这些系统时要更加“不讲理”。Mike Krieger: He likes that framing. One Labs initiative involved an internal product that was not working as someone wanted. Rather than treating the request as something a developer alone should handle, he realized the non-technical person could be taught to ask Claude directly. Product teams and the industry need to teach people to be more unreasonable in their use of these systems.

第一代 AI 产品对模型的限制过多：它们能够使用的工具受限，自主发挥的空间也很有限。这使得一些雄心勃勃的请求更难实现。模型可能能够编写代码，却无法运行代码；或者只能对其环境进行有限的检查。功能更强大的产品界面改变了这种权衡。知识工作者可能看起来并不需要一台能够编写 Bash 的虚拟机，但当内置方法失效时，它就会变得很有用——例如，当 PDF 无法解析，而系统可以编写另一个脚本来处理它时。The first generation of AI products put models too much in a box: they had constrained tool access and limited freedom. That made ambitious requests harder to fulfill. A model might be able to write code but not run it, or inspect its environment only partially. More capable product surfaces change the calculation. A knowledge worker may not seem to need a virtual machine that can write Bash, but it becomes useful when the built-in method fails—for example, when a PDF cannot be parsed and the system can write another script to handle it.

Krieger 最不合理的请求涉及一个用 Python 编写的 Labs 项目。Python 对他个人而言意义非凡，因为 Instagram 就是用 Python 构建的。Claude Code 使用 Bun 找到了更好的部署方案，于是他要求 Claude Code 将整个项目移植到 TypeScript。在他职业生涯早期，移植几十万行代码似乎会是个糟糕的主意。他搭建了一个动态工作流，让系统在一个周末内完成代码移植和验证，反复比较结果，并于周一回来时得到一个已完成、可部署的版本。Krieger’s most unreasonable request involved a Labs project written in Python, a language that is personally meaningful to him because Instagram was built in Python. Claude Code found a better deployment approach using Bun, so he asked it to port the whole project to TypeScript. Earlier in his career, porting a few hundred thousand lines of code would have seemed like a bad idea. He set up a dynamic workflow, let the system port and verify the code over a weekend, repeatedly compared the results, and returned on Monday to a completed, deployable version.

发布、迁移和运营控制Shipping, Migration, and Operational Control

主持人：同样的方法能否迁移像 Instagram 这样的产品，而不仅仅是运行时或编译器？Host: Could the same approach port a product such as Instagram, not just a runtime or compiler?

Mike Krieger：这个产品案例可能既不能明确说更容易，也不能说更难。Python 3 发布后，Instagram 得以添加类型注解，公司也曾讨论 Python 最终是否会成为自身发展的限制。Krieger 认为 Python 还能走得更远，而类型则有助于团队避免自我掣肘。Instagram 构建了 MonkeyType，用于捕获生产环境中实际使用的运行时类型，并将其映射回代码库。Mike Krieger: The product case may be neither clearly easier nor harder. When Python 3 arrived, Instagram was able to add type annotations, and the company debated whether Python would eventually hold it back. Krieger believed it could go much further, while types would help the team avoid getting in its own way. Instagram built MonkeyType to capture the runtime types actually used in production and map them back to the codebase.

这种模式为借助语言模型进行转换或交叉编译提供了有益的方法。团队可以更多地依赖生产数据，并开展分段测试。困难之处在于找到一个边界，使迁移能够逐步开始，而不是试图在一夜之间替换所有内容。That pattern suggests useful approaches for conversion or cross-compilation with language models. Teams can lean more heavily on production data and run segmented tests. The difficult part is finding a boundary where migration can begin incrementally instead of attempting to replace everything overnight.

主持人：用户最终会成为测试对象，而发布基础设施使实验成为可能。Host: Users ultimately become tests, and rollout infrastructure makes experimentation possible.

Mike Krieger：Instagram 上线时，后端在第一周举步维艰，因为团队当时还不了解其扩展所需的一切。在一次由投资人安排的基础设施午餐会上，团队吸收了两条令他一直铭记的建议。第一，在发生故障之前，测量所有可能涉及的指标；否则，一旦出现问题，团队就无法判断某项指标是正常还是偏高，因为他们才刚刚添加了这项指标。第二，精心设计各种可调参数和功能开关。Mike Krieger: At Instagram’s launch, the backend struggled during the first week because the team did not yet understand all of its scaling needs. At an infrastructure lunch scheduled by an investor, the team absorbed two pieces of advice that stayed with him. First, measure everything that might conceivably matter before an outage; otherwise, when something breaks, teams cannot tell whether a metric is normal or high because they only just added it. Second, build thoughtful knobs and feature flags.

即使是早期的 Instagram，也具备简单但有效的扩容、发布和动态配置机制。运行时设置有时必须在几秒钟内完成更改，以应对负载。将这种能力作为一等公民至关重要。Krieger 认为，AI 领域也有同样的需求：团队不断在不同方面进行权衡，因此需要通过运行时配置来管理这些权衡。Even early Instagram had simple but effective ramp-up, rollout, and dynamic-configuration mechanisms. Runtime settings sometimes had to change within seconds to handle load. Making that a first-class capability was essential. Krieger sees the same need in AI, where teams are constantly making different trade-offs and need runtime configuration to manage them.

将代理视为队友，将代码审查视为意图审查Agents as Teammates and Code Review as Intent Review

主持人：Claude Code 及类似工具让软件开发变得高度动态：团队会为一个应用发布数千个功能开关。这对日常工作有何影响？Host: Claude Code and similar tools make software development highly dynamic: teams ship one application with thousands of flags. How does that affect everyday work?

迈克·克里格：Anthropic 采用这种方式已经有一段时间了。当有人针对特定问题进行密切迭代，并希望实现高带宽的双向交流时，Claude Code 这样的交互式工具非常有用。但许多工作是通过 Tags 这样的系统委派完成的。它们的多人协作特性很重要：了解其他人如何使用一个系统，就像人们在 Discord 上观察彼此使用 Midjourney 一样，会鼓励更加大胆的使用方式。Mike Krieger: Anthropic has been working this way for some time. Interactive tools such as Claude Code are useful when someone is iterating closely on a specific problem and wants high-bandwidth back-and-forth. But much of the work is delegated through systems such as Tags. Their multiplayer nature is important: seeing how other people use a system, much as people observed each other’s Midjourney use on Discord, encourages more ambitious use.

看到有人告诉 Claude 不只是修复一个 bug，而是要负责代码库的一部分、监控反馈渠道、主动处理任务，并在 API 发生变化时作出响应，这展现了一种更先进的使用模式。这个代理不再只是 Slack 中一个加强版的 Claude Code，而是一个拥有上下文和记忆、能够主动采取行动的队友。这使得内部工作变得更加多人协作、异步且主动，而不再是一群人各自在自己的 CLI 中工作。Seeing someone tell Claude not merely to fix a bug, but to take responsibility for part of a codebase, monitor a feedback channel, proactively address tasks, and respond when an API changes, reveals a more advanced model of use. The agent becomes more than a glorified version of Claude Code in Slack. It is a teammate with context and memory that can act proactively. That has made internal work more multiplayer, asynchronous, and proactive rather than a collection of people working separately in their CLIs.

主持人：代码审查和 Git 现在成了瓶颈吗？代码合并之前还需要有人检查代码吗？Host: Is code review and Git now the bottleneck? Does a person still need to inspect the code before it merges?

Mike Krieger：代码审查仍然是瓶颈，尤其是在涉及架构变更时。更深层的限制在于，人类有能力完整构想正在发生的事情。Anthropic 的 Claude Code artifacts 部分就是为了解决这一问题而构建的。团队不必再把一个包含两千行代码的拉取请求交给审查者，并期望他们仅凭代码推断其含义，而是可以分享一个说明变更意图及所作权衡的 artifact。Mike Krieger: Reviews remain a bottleneck, especially for architectural changes. The deeper constraint is human ability to fully conceptualize what is happening. Anthropic’s Claude Code artifacts were partly built to address that. Instead of giving a reviewer a two-thousand-line pull request and expecting them to infer it from the code, teams can share an artifact explaining the intention of the change and the trade-offs that were made.

代码最终可以通过各种方式进行验证，但讨论应越来越聚焦于意图和权衡，随后在生产环境中进行度量。Krieger 并不声称会检查每个拉取请求中的每一行代码。他让 Claude 调查自己会针对代码提出的问题。这就是由 Claude 提供支持、同时仍由人主导的审查。对于重要变更，以及以不同方式处理的外观视觉变更，团队可以做出明智的决定，并在必要时继续修复。Code can ultimately be verified in various ways, but discussion should increasingly focus on intent and trade-offs, followed by measurement in production. Krieger does not claim to inspect every line of every pull request. He asks Claude to investigate the questions he would ask about the code. That is Claude-powered review, while remaining human-driven. For important changes—and, differently, for cosmetic visual changes—teams can make an informed decision and fix forward when necessary.

Anthropic Labs 如何组织下注How Anthropic Labs Organizes Bets

主持人：鉴于 Claude Code 和 Tags 等产品，Anthropic Labs 组织的架构是怎样的？Host: What does the Anthropic Labs organization look like, given products such as Claude Code and Tags?

Mike Krieger：团队必须在灵活性与对员工的支持之间取得平衡。工程管理的重要性并没有消失：辅导、人际协作以及个人发展仍然十分重要。不过，在 Labs 团队中，运营节奏是每两周进行一次评审，在评审中，每个项目都会被考虑是“坚持还是转向”。项目可能会继续、转向，或者被终止。在许多这样的周期中，项目都会被关闭，而反复进行这一过程，会让项目终止不再那么像个人失败，而更像是快速原型设计所预期的结果。Mike Krieger: The team has to balance flexibility with support for people. The importance of engineering management has not disappeared: coaching, interpersonal work, and individual development remain important. In a Labs group, however, the operating cadence is two-week reviews in which every project is considered for “persevere or pivot.” A project may continue, pivot, or be shut down. Projects are closed in many of these cycles, and repeating that process makes shutdown feel less like personal failure and more like the intended consequence of rapid prototyping.

该团队快速制作原型，在内部发布，可能会将工作转入抢先体验阶段；如果项目行不通，则会将其收尾。由于项目变化如此迅速，将组织架构与各个项目过于紧密地绑定在一起，就意味着每两周都要进行一次重组。相反，负责某项“押注”的团队会从各个专业领域抽调人员。团队中可能会有一名押注负责人或直接负责的个人，但此人通常不会管理所有参与该押注项目的人。The group prototypes quickly, ships internally, may move work to early access, and winds it down if it does not work. Because projects change so quickly, aligning the organization chart too tightly with individual projects would mean reorganizing every two weeks. Instead, a team working on a given “bet” draws people from across disciplines. There may be a bet lead or directly responsible individual, but that person usually does not manage everyone working on the bet.

这种结构使得解散一个运作不佳的项目并继续前进变得更加容易。工程经理专注于确保每个人都被分配到自己感兴趣且能够高效完成的工作上。当一款产品证明自己具备持续发展的潜力时，其组织就会变得更加稳固。例如，Claude Design 最初只是一个临时组建的小组；在产品发布、获得发展势头并进行一次重大的第二版发布后，它拥有了更加专门的团队和更完善的组织结构。This structure makes it easier to disband a project that is not working and move on. Engineering managers concentrate on ensuring that each person is assigned to work they are excited about and can do effectively. When a product proves it has legs, it becomes more solid. Claude Design, for example, began as an ad hoc group; after shipping, gaining traction, and receiving a major second release, it acquired a more dedicated team and more structure.

主持人：Claude Design 接下来可以走向何方？Host: Where can Claude Design go next?

Mike Krieger：一个限制因素在于 Anthropic 的产品界面之间能否良好地协同。他希望在围绕设计的对话与交互式设计工作（包括使用 Claude Code 开展的工作）之间建立更加无缝的衔接。各项服务之间如果无法充分互通，就会限制许多创意的实现。另一个发展方向是设计与应用之间日益模糊的界限。尽管 Claude Design 最初并非为此目的而设计，但人们已经用它构建出了功能完备的体验，甚至是游戏。真正有趣的发展路径，是将精心打磨的设计转化为一种能够持久保存数据、与他人共享，并成为后续工作基础的产物。Mike Krieger: One constraint is how well Anthropic’s product surfaces interact. He wants a more seamless path between a conversation about a design and interactive design work, including work with Claude Code. Services that do not communicate as well as they could hold back many ideas. Another direction is the increasingly blurry line between a design and an app. People have built fully functional experiences, even games, with Claude Design, despite that not being its original purpose. The interesting path is from a polished design to an artifact that can persist data, be shared with others, and become a foundation for further work.

产品简化、初创企业与垂直领域 AIProduct Simplification, Startups, and Vertical AI

主持人：Instagram 曾因大幅删减早期产品内容而闻名，从而变得更加专注。Krieger 会从 AI 产品或 Claude 中删掉什么？Host: Instagram famously removed much of its early product to become focused. What would Krieger remove from AI products or Claude?

Mike Krieger：Anthropic 有一个名为 Project Unhip 的 Slack 频道，用来讨论哪些内容应该被移除。在 Instagram，某项功能可能只有百分之四或五的用户使用，看似规模很小，但许多这样的功能对不同的用户群体而言可能各自都很有价值——这就是人们熟悉的 Microsoft Word 式使用情况分散问题。决定移除什么总是很困难。Mike Krieger: Anthropic has a Slack channel called Project Unhip for considering what should be removed. At Instagram, a feature used by four or five percent of people could seem small, but many such features could each be valuable to different user groups—the familiar Microsoft Word problem of disjoint usage. It is always hard to decide what to remove.

Anthropic 仍是较新的产品，可能此类案例较少。它最近移除了 Styles，该功能只有少数用户使用，而且限制性过强；Skills 提供了更好的方法。产品团队需要愿意下线某一代 AI 的基础组件，或用下一代 AI 对其进行补充和替代。Anthropic is still a younger product and may have fewer such cases. It recently removed Styles, which was used by a small share of users and was too prescriptive; Skills offered a better approach. Product teams need to be willing to unship primitives from one generation of AI, or supplement and replace them with the next generation.

更大的问题在于产品复杂性。用户需要在 Claude Code、Cowork 和聊天之间做出区分，尽管这些界面之间的协作并不顺畅，也无法彼此委派任务。普通人可能无法解释它们为何要彼此分开。减少其中一部分复杂性，可以让 Claude 更直接地完成它需要做的事情。不应再需要这样一种工作流：用户先在 Cowork 中规划构建方案，然后还得请求生成一段文字，再将其粘贴到 Claude Code 中。The bigger issue is product complexity. Users are asked to distinguish among Claude Code, Cowork, and chat even though the surfaces do not interoperate well or delegate to one another. An ordinary person may not be able to explain why they are separate. Removing some of that complexity would let Claude do what it needs to do more directly. A workflow where someone maps out a build in Cowork and then has to request a paragraph to paste into Claude Code should no longer be necessary.

主持人：如果 Anthropic 可能发布重塑各行业的能力，初创公司为什么还要继续投入建设？Host: Why should startups continue building if Anthropic might release capabilities that reshape their industries?

迈克·克里格：他加入 Anthropic 的一个原因是，更强大的模型将开启新一代初创公司的发展空间。它们无法替创始人解决创意构思或品味问题，但能让实验变得更加简单，并帮助团队更快推进。他对此仍深信不疑。Instagram 曾经面临这样的问题：如果 Google 推出一款照片产品，会发生什么？Google 打造的产品会受到其自身整合能力和优势的塑造。Mike Krieger: One reason he joined Anthropic was that stronger models would unlock a new generation of startups. They do not solve a founder’s ideation or taste, but they make experimentation simpler and help teams move faster. He remains convinced of that. Instagram once faced questions about what would happen when Google launched a photo product. Google would make a product shaped by its own integrations and strengths.

对于那些高度专注于某个垂直领域、行业或一群自己有深入了解的用户的团队来说，仍然有充足的发展空间——通用型实验室无法达到同等程度的了解。这种专注能够赢得用户的采用和喜爱。模型让一些工作变得更难守住竞争壁垒，某些能力也可能从独立产品变成一种技能，但真正艰苦的工作依然存在：理解人们的需求，触达他们，倾听他们，并快速迭代。四五个痴迷于某个问题的人，仍然可以比身处更复杂组织中的同样几个人行动得更快。编写代码从来不是决定一家初创企业成败的唯一因素；对用户的理解以及产品与用户的契合度更为重要。There is still ample room for teams that are intensely focused on a vertical, industry, or group of users they know deeply—knowledge a general-purpose lab will not reach at the same level. That focus can earn adoption and user love. The models make some work harder to defend, and some capabilities may become skills rather than standalone products, but the hard work remains: understanding people’s needs, reaching them, listening, and iterating quickly. Four or five people obsessed with a problem can still move faster than the same people inside a more complex organization. Writing code was never the sole thing that made or broke a startup; user understanding and fit matter more.

主持人：这对医疗和金融等领域的垂直 AI 意味着什么？Host: What does that mean for vertical AI in areas such as healthcare and finance?

Mike Krieger：金融是一个模型能力逐代显著提升的领域，专注于金融的初创公司也开发了自己的评估体系，有助于展示模型是否在这些使用场景中变得更好。一个很有前景的组合是：模型足够灵活，能够即时创建分析、仪表板和工作流，同时结合经过验证的数据集。将一切完全做成自由形式会造成混乱，也无法满足大多数金融服务公司的需求。Mike Krieger: Finance is an area where model capability is visibly improving from generation to generation, and finance-focused startups have developed their own evaluations that help show whether models are getting better for those use cases. The promising mix is a model flexible enough to create just-in-time analyses, dashboards, and workflows, combined with a verified set of data. Making everything completely free-form would create confusion and would not meet the needs of most financial-services companies.

设计上的挑战在于建立可验证性、审计日志记录和数据溯源，同时不限制在其之上构建的应用程序。若能妥善实现，就能兼顾灵活性与信任。现有为可验证性而构建的系统通常对智能体工作负载不够灵活，因此堆栈的两端都存在机会。The design challenge is to establish verifiability, audit logging, and data provenance without constraining the applications that can be built on top. Done well, that provides both flexibility and trust. Existing systems built for verifiability are often not very flexible for agentic workloads, so there is opportunity on both sides of the stack.

在快速发展的领域中实现可持续工作Working Sustainably in a Fast-Moving Field

主持人：从事高强度人工智能工作的人应如何避免职业倦怠？Host: How should people working intensely in AI avoid burnout?

Mike Krieger：这很困难，因为行业发展的速度快得多。在 Instagram，当时的重大问题可能是一年一度的 Apple 发布会，或是每隔几个月才有一次竞争对手推出产品。而在 Anthropic，每周一次的全员会议到了周三，幻灯片中就可能已经包含“周末 AI”一页，因为竞争对手发布了一个模型、出现了一款新产品，或者监管发生了变化。Mike Krieger: It is difficult because the industry is moving much more quickly. At Instagram, the major questions might have been an annual Apple announcement or a competitor launch every few months. At Anthropic, a weekly all-hands can already include a “weekend AI” slide by Wednesday because a competitor has shipped a model, a new product has appeared, or regulation has changed.

第一个答案是有意抽出时间休息。倦怠可能会让人无法继续工作，而且恢复起来可能需要很长时间。没有哪份工作重要到让人无法离线休息几天。如果确实如此，那很可能出了问题，这个人应该和导师谈谈，看看如何解决阻碍。The first answer is to deliberately take time off. Burnout can leave people unable to continue and can take a long time to recover from. No job should be so important that a person cannot be offline for a couple of days. If it is, something is probably wrong, and the person should talk with a mentor about how to unblock it.

他还借鉴了体育运动中的一个道理：人们的表现从来不会一直好到如同自己发挥最好的一场比赛，也不会一直差到如同自己发挥最糟的一场比赛。在人工智能领域，情绪周期可能会迅速摆动，在认为一切都结束了和认为一切都恢复如初之间来回切换。记住这个周期还会再次出现，有助于保持客观视角。创业团队可能会觉得某次挫折无法挽回，就像 Instagram 有时经历的那样，但撑过这些时刻，才是一家公司真正的定义。He also draws on a lesson from sports: people are never as good as their best game or as bad as their worst one. In AI, the emotional cycle can swing quickly between believing everything is over and believing everything is back. Remembering that the cycle will recur provides perspective. Startup teams may feel that a setback is unrecoverable, as Instagram did at times, but working through those moments is what defines a company.

这项工作进展迅速，但也是一场长期博弈。它不仅关乎今天的模型发布、产品发布或即时反应。团队需要相信，自己正在构建能够经受挫折的文化和能力；即使相关视角只需拉长到几个月，也要学会退一步审视全局，并避免让自我认知或成功感被日常起伏所左右。The work is fast-moving but also a long game. It is not only about today’s model launch, product launch, or immediate reaction. Teams need to trust that they are building the culture and capability to get through setbacks, zoom out even if the relevant perspective is only a few months, and avoid letting their sense of self or success be driven by the day-to-day.

主持人：在困难时刻，他会反复提起教练或导师给过的建议吗？Host: Is there advice from a coach or mentor that he repeats during difficult moments?

迈克·克里格：当一个人有某种感受时，团队中的其他人往往也有同样的感受。一位教练鼓励他把情绪说出来：比如说自己压力很大，或者为某项计划被叫停而感到难过。最近，他在一次会议上谈到一个自己曾为之付出大量努力的项目时，开场便说自己很难过，也很沮丧，因为项目最终没有成功。这让其他人也有空间说出自己的沮丧或难过。坦诚和展现脆弱，能让团队说清楚正在发生什么，之后也就更容易讨论下一步该怎么做。Mike Krieger: When one person is feeling something, others on the team are often feeling it too. A coach encouraged him to verbalize emotions: saying that he is stressed, or that he is sad an initiative is being shut down. He recently opened a meeting about a project he had worked hard on by saying that he was sad and frustrated that it had not worked out. That gave others room to say they were upset or sad as well. Openness and vulnerability let a team name what is happening, after which it becomes easier to ask what to do next.

主持人：对话最后鼓励人们谈论自己的感受、管理好心理健康，并持续发布产品。Host: The conversation closes by encouraging people to talk about their feelings, manage their mental health, and keep shipping.