AI 的第三个时代：持久化 AI 同事崛起 | OpenAI 产品负责人 Tara Seshan

简要描述Brief Description

OpenAI 负责 Codex 和 ChatGPT Work 的产品负责人 Tara Seshan 做客播客，讨论从简单的聊天界面转向持久化 AI 协作者这一转变。她分享了对 OpenAI 去中心化、创始人主导的运营文化的见解，解释了为什么产品经理必须从理论规划转向实证实验，以及团队为何必须大幅提升目标和抱负。Seshan 还介绍了 ChatGPT 工作模式和编程模式背后的产品架构，对比了软件开发与知识工作中的反馈循环，详细阐述了她关于写作是思考而非汇报的理念，并分享了自己从 Stripe 和 Sutter Hill Ventures 获得的关键职业经验。Tara Seshan, product lead at OpenAI for Codex and ChatGPT Work, joins the podcast to discuss the transition from simple chat interfaces to persistent AI coworkers. She shares insights into OpenAI’s decentralized and founder-led operating culture, why product managers must shift from theoretical planning to empirical experimentation, and how teams must drastically elevate their ambition. Seshan also explains the product architecture behind ChatGPT's work and coding modes, contrasts the feedback loops in software development with knowledge work, details her philosophy on writing as thinking versus reporting, and shares key career lessons from Stripe and Sutter Hill Ventures.

目录Table of Contents

• OpenAI 的介绍与文化
  Introduction and Culture at OpenAI
• 实证型产品经理
  The Empirical Product Manager
• 使用 AI 代理进行操舵与划船
  Steering vs. Rowing with AI Agents
• 提升雄心与内部运营梗
  Elevating Ambition and Internal Operating Memes
• 揭秘 ChatGPT、Codex 和工作模式
  Demystifying ChatGPT, Codex, and Work Mode
• PM 的不断演变角色与人类匠心
  The Evolving Role of PMs and Human Craft
• 战术工作流：网站与可视化内容
  Tactical Workflows: Sites and Visualizations
• 将写作视为思考 vs. 将写作视为报告
  Writing as Thinking vs. Writing as Reporting
• 来自 Sutter Hill Ventures 的产品营销经验教训
  Product Marketing Lessons from Sutter Hill Ventures
• 为知识工作调整 AI 界面
  Adapting AI Interfaces for Knowledge Work
• 快速问答与最终建议
  Lightning Round and Final Advice

OpenAI 简介与文化Introduction and Culture at OpenAI

主持人：欢迎收听本期播客。今天的嘉宾是塔拉·塞尚。塔拉负责 OpenAI 的 Codex 和 ChatGPT Work 两款产品的产品工作；可以说，ChatGPT Work 是如今面向知识工作者增长最快、也最重要的 AI 产品之一。在加入 OpenAI 之前，塔拉曾在 Stripe 工作六年，是最早加入的五位产品经理之一；她还曾负责 Watershed 的产品，并曾是蒂尔学者。Host: Welcome to the podcast. Today, my guest is Tara Seshan. Tara leads product for both Codex and ChatGPT Work at OpenAI, which is arguably the fastest-growing and most important AI product for knowledge workers today. Prior to OpenAI, Tara spent six years at Stripe as one of the first five product managers, led product at Watershed, and was a Thiel Fellow.

Tara，你加入 OpenAI 已经大约一年了。在 AI 时代，这感觉就像过了一辈子。刚加入时，你可能对在一家前沿实验室工作会是什么样有一定的预想。OpenAI 实际的日常文化中，最让你感到意外的是什么？Tara, you have been at OpenAI for about a year now. In AI time, that feels like a lifetime. When you joined, you probably had a sense of what working at a frontier lab would be like. What has most surprised you about the actual day-to-day culture at OpenAI?

Tara Seshan：许多方面都让我感到熟悉，因为我之前曾在高速增长、高强度、超大规模扩张的公司工作过。人才水平和紧迫感都非常熟悉。最令人意外的是，我之前工作过的每家公司都是由创始人领导的，而 OpenAI 本质上是由“众多创始人领导”的。公司内部的每个人都在各自负责的产品领域内以创始人的方式开展工作。Tara Seshan: Many aspects felt familiar because I had worked at high-growth, high-intensity, hyperscaling companies before. The talent caliber and the urgency felt very recognizable. The most surprising part is that every company I previously worked for was founder-led, whereas OpenAI is essentially "founders-led." Everyone inside the company operates as a founder within their specific product area.

与我曾经工作过的其他地方相比，自上而下的指导极其有限。一方面，这令人愉悦，因为你与市场之间的距离近得几乎不存在。你不会与用户反馈隔绝；为了找到产品与市场的契合点，你会不惜一切代价。The level of top-down direction is extremely limited compared to anywhere else I have worked. On one hand, that was delightful because the distance between you and the market is razor-thin. You are not insulated from user feedback; you do whatever it takes to find product-market fit.

另一方面，我原本以为这里会有一个存放宏大战略文件的秘密金库，就像一家成熟公司可能拥有一份权威的运营手册一样。实际上，OpenAI 确实是开放的。关于模型和产品应当走向何方的想法与理念，很快就会直接进入面向公众的产品。不存在某个秘密后室，里面运行着一个 AGI 来执行总体计划；构建出来的东西会立即交到用户面前，供他们亲自接触和测试。On the other hand, I came in expecting a secret vault of grand strategy documents, similar to how an established company might have an authoritative operational manual. In reality, OpenAI is genuinely open. The thoughts and ideas about where models and products should go very quickly make their way directly into the public product. There is no secret backroom with an AGI running a master plan; what gets built is immediately put in front of users to touch and test.

实证型产品经理The Empirical Product Manager

主持人：鉴于您作为产品管理领导者拥有丰富的经验，在这个瞬息万变的新环境中开展工作时，会发生哪些转变或变化？Host: Given your extensive background as a product management leader, what shifts or changes when operating in this new fast-moving environment?

塔拉·塞尚：在静态或变化较慢的市场中，你可以执行宏大且可预测的战略。例如，在支付领域，你可以从第一性原理出发，严谨地推断竞争对手将如何应对。严谨的理论思考是必需的；如果不这样做，就会显得粗心大意，因为其中的变量都已得到充分理解。Tara Seshan: In static or slower-moving markets, you can execute grand, predictable strategies. In payments, for example, you can reason rigorously from first principles about how competitors will respond. Rigorous theoretical thinking is required, and failing to do so comes across as careless because the variables are well understood.

在这个市场中，一切都在不断涌现和变化。与研究保持紧密联系至关重要。因此，高产且注重实证远比学术化或理论化更有价值。In this market, everything is emergent and dynamic. Staying closely tied to research is essential. Because of that, being prolific and empirical is far more valuable than being academic or theoretical.

不再撰写冗长的、论文式的推理文档，转而问自己“我能多快做出一个东西来让用户测试？”，这是一次重大的调整。起初，我觉得自己没有进行足够的尽职调查。但如今，真正的严谨性在于提出一个明确而尖锐的假设——决定产品能否成功的核心问题——并尽可能快速地对其进行测试。It was a major adjustment to stop writing long, thesis-style reasoning documents and instead ask, "How quickly can I build something to test with users?" At first, it felt like I was not doing enough due diligence. But the real rigor now lies in formulating a sharp, pointed hypothesis—the core question that determines whether a product succeeds—and testing it as fast as possible.

主持人：感觉那个核心的测试循环就是产品管理经久不变的本质，尽管其他一切都在变化。Host: It feels like that core testing loop is the enduring essence of product management, even as everything else shifts.

塔拉·塞尚：没错。这个角色所有外围的事务——跟踪日程、制作状态汇报演示文稿，以及撰写冗长的演示文稿——都不再需要了。Tara Seshan: Exactly. All the peripheral mechanics of the role—tracking schedules, producing status presentations, and writing long decks—fall away.

核心工作始终是识别关键问题、理解用户和技术限制、形成清晰的假设，并在快速反馈循环中分析结果。如今，工程经理、设计师和数据科学家都共同关注这些方面，这使产品经理能够更加专注于最重要的事情。The core job has always been identifying the essential question, understanding user and technology constraints, forming a crisp hypothesis, and analyzing the results in a rapid feedback loop. Today, engineering managers, designers, and data scientists all share that focus, which allows PMs to double down on what matters most.

使用 AI 代理进行掌舵与划船Steering vs. Rowing with AI Agents

主持人：围绕自主循环已经有了大量讨论：智能体被赋予一个目标，然后不断迭代，直到成功。您如何看待这一循环在一般知识工作中的扩展？Host: There has been significant discussion around autonomous loops where an agent is given an objective and iterates until it succeeds. How do you see that loop expanding across general knowledge work?

塔拉·塞尚：未来的工作将更像掌舵，而不是划船。智能体将负责划船，而人类专业人士则专注于将船驶向正确的方向。Tara Seshan: The future of work will look more like steering than rowing. Agents will handle the rowing, while human professionals focus on steering the ship in the right direction.

随着时间的推移，这种引导会逐步上升到更高的抽象层次。过去，这意味着编写一行代码并按下 Tab 键。如今，它是在目标层面指导任务，而最终，它将上升到更高的战略层面。Over time, that steering moves up layers of abstraction. It used to be writing a line of code and pressing tab. Now, it is directing tasks at the goal level, and eventually, it will be at even higher strategic levels.

数据为其中一部分引导提供了依据，但人的直觉和带有主见的决策仍然至关重要。软件开发不像房地产——投入资本就能保证可预测的回报；它更接近电影制作。艺术视角和鲜明的声音决定了产品能否引发共鸣。此外，工作将变成多人协作的模式，团队共同引导协作的智能体群。Data guides some of that steering, but human intuition and opinionated decision-making remain vital. Building software is not like real estate, where pouring in capital guarantees a predictable return; it is closer to filmmaking. An artistic point of view and a distinct voice determine whether a product resonates. Furthermore, work will become multiplayer, where teams steer collaborative swarms of agents together.

提升雄心与内部运营梗Elevating Ambition and Internal Operating Memes

主持人：如今，雄心似乎成了一个主要的差异化因素。由于基础性任务更容易执行，人们和公司能够尝试的事情，其门槛已经大幅提高。您如何看待这一转变？Host: Ambition seems to be a major differentiator now. Because foundational tasks are easier to execute, the bar for what people and companies can attempt has risen significantly. How do you view this shift?

塔拉·塞尚：AI 最有效的使用者并不只是将日常任务自动化；他们还会大幅拓展自己的能力范围。过去，独角兽人才是指那些具备出色产品判断力，同时还能进行设计和编写代码的人，从而消除了跨职能之间的沟通转换层。Tara Seshan: The most effective users of AI do not merely automate routine tasks; they dramatically expand their range of capabilities. Previously, a unicorn hire was someone with great product sense who could also design and write code, eliminating cross-functional translation layers.

如今，任何人都可以独立完成初始设计、构建原型、建立财务情景模型，并执行复杂的工作流程。由于执行阻力已大幅降低，人们必须主动拓展自己的思维模式，重新认识在短得令人难以置信的时间内可以完成哪些事情。Now, anyone can spin up initial designs, build a prototype, model financial scenarios, and execute complex workflows independently. Because execution friction has dropped so substantially, people must actively expand their mental model of what can be accomplished in an unreasonably short timeframe.

如今，PM 的一项首要职责是提升周围人的目标和抱负。当有人提出一个常规的时间表或范围时，PM 应该询问其 10 倍版本是什么样的，以及是否可以立即尝试。A primary responsibility of a PM today is elevating the ambition of those around them. When someone proposes a standard timeline or scope, the PM should ask what the 10x version looks like and whether it can be attempted immediately.

主持人：OpenAI 内部是否有一些特定的箴言或梗，能够体现这种思维方式？Host: Are there specific internal mantras or memes at OpenAI that capture this mindset?

Tara Seshan：有三个核心理念指导着产品开发：Tara Seshan: There are three central memes that guide product development:

首先是“这是否已经实现了最大限度的加速？”，重点关注执行速度。其次是“我们的目标是否足够宏大？”，它对范围和规模提出了挑战。第三是“你已经把它当作日常必需品了吗？”，通过询问团队是否每天全天候依赖该产品，取代了传统的内部试用。First, "Is this maximally accelerated?" which focuses on execution speed. Second, "Are we being ambitious enough?" which challenges scope and scale. Third, "Are you mainlining it yet?" which replaces traditional dogfooding by asking whether the team relies on the product all day, every day.

另一条重要原则是，要着眼于模型在两到三个月后的发展水平来构建产品。如果按照模型当前的能力来构建，你会失败；如果按照你认为模型一年后会达到的水平来构建，你同样会失败。与研究路线图保持紧密一致，能够让我们设计出随着底层能力提升而逐渐退居幕后、减少阻碍的产品界面。Another essential rule is building for where the models will be in two to three months. If you build for where the models are today, you fail. If you build for where you think they will be in a year, you also fail. Tight alignment with research roadmaps allows us to design product surfaces that get out of the way as underlying capabilities advance.

揭开 ChatGPT、Codex 和工作模式的神秘面纱Demystifying ChatGPT, Codex, and Work Mode

主持人：纵观如今的 ChatGPT 生态系统，用户会看到标准聊天、Codex 和“工作”切换开关等不同选项。用户应该如何理解这些区别？Host: Looking at the ChatGPT ecosystem today, users see different options like standard chat, Codex, and the Work toggle. How should users understand these distinctions?

塔拉·塞尚：我们最终的北极星目标是，用户永远不必浏览菜单或理解各种工具套件。你只需说明想要实现什么，系统就会自动将任务路由到合适的模型和运行时环境。Tara Seshan: Our ultimate North Star is that users should never have to navigate menus or understand harnesses. You should simply specify what you want to achieve, and the system will automatically route to the right model and runtime environment.

目前，该界面能够贴合用户的实际情况：Currently, the interface meets users where they are:

聊天模式针对对话、头脑风暴和网页搜索进行了优化。Codex 模式提供了一个明确的开发环境，可从技术层面查看代码树和执行链。Chat mode is optimized for conversations, brainstorming, and web search. Codex mode provides an explicit development environment with technical visibility into the code tree and execution chain.

工作模式在底层使用与 Codex 完全相同的引擎，但去除了偏重开发者的用户界面。它让知识工作者无需与代码终端交互，即可生成复杂的成果物，例如精细的企业财务模型。Work mode operates on the exact same engine under the hood as Codex, but strips away developer-heavy UI. It allows knowledge workers to generate complex artifacts, such as intricate corporate finance models, without needing to interact with a code terminal.

主持人：您如何在管理一个拥有数亿用户的消费级平台的同时，兼顾快速、实验性的智能体开发？Host: How do you balance managing a consumer platform with hundreds of millions of users alongside rapid, experimental agent development?

Tara Seshan：将 Work 引入 ChatGPT 的目标，是把自主代理的强大能力带给超过十亿名主要熟悉聊天界面的用户。Tara Seshan: The objective of introducing Work to ChatGPT is bringing the power of autonomous agents to over a billion users who primarily know chat interfaces.

在这个时代，快速交付变革性的工作流胜过为了表面上的精致而迟迟不发布。让用户尽早获得这些能力，能够揭示他们实际如何与智能体交互，并支持发布后的快速迭代。In this era, shipping a transformative workflow quickly beats holding back for cosmetic polish. Getting capabilities into users' hands reveals how they actually interact with agents and enables rapid post-launch iteration.

产品经理角色与人类匠艺的演变The Evolving Role of PMs and Human Craft

主持人：随着职能角色日益融合，AI 编写的代码越来越多，许多人都想知道自己的日常职业身份会是什么样。你如何看待角色边界与专业技艺？Host: As functional roles blend together and AI writes more code, many people wonder what their daily identity looks like. How do you think about role boundaries and professional craft?

塔拉·谢尚：我的职业生涯大部分时间都在早期阶段的初创公司和 Stripe 度过，因此我更偏好职能边界灵活的工作环境。关键在于明确指定一名直接负责人员（DRI），对产品质量和用户采用率负责。Tara Seshan: Having spent much of my career in early-stage startups and Stripe, I prefer environments where functional boundaries are fluid. What matters is having a clear directly responsible individual (DRI) accountable for product quality and user adoption.

当一门技艺的手工操作——比如工程师亲手编写每一行代码——被自动化取代时，人们自然会感到一种失落。这门技艺正逐渐向更高层次的系统设计、问题构建和问题选择转移。It is natural to feel a sense of loss when the manual mechanics of a craft—like an engineer writing every line of code by hand—get automated away. Craft is shifting upward toward higher-level system design, framing, and problem selection.

人类将继续承担最终责任、主观的艺术品味以及人与人之间的关怀。凝聚团队、达成共识并提升集体抱负，仍然是深具人性的人类事业。Humans will continue to provide ultimate accountability, subjective artistic taste, and interpersonal care. Rallying a team, creating alignment, and elevating collective ambition remain deeply human endeavors.

战术工作流：网站与可视化Tactical Workflows: Sites and Visualizations

主持人：你会如何将 AI 以一些实用且创新的方式融入日常产品工作流程中？Host: What are some practical, innovative ways you integrate AI into your daily product workflow?

Tara Seshan：最强大的工作流之一，是直接在 Codex 和 Work 模式中构建临时 Web 应用，或称为“站点”。Tara Seshan: One of the most powerful workflows is building ephemeral web applications, or "Sites," directly inside Codex and Work mode.

Alan Kay 最初设想的是一种灵活可变的个人软件，用户可以随时构建它。如今，只需一个提示，我就能快速创建一个功能完备的交互式仪表板，并配备自己的数据库；为背包旅行构建一个实时行程追踪器；或为我的团队创建一款定制的多人游戏。相比分享静态幻灯片或电子表格，分享动态的托管网站已经成为我主要的演示媒介。Alan Kay originally envisioned malleable personal software that users could construct on the fly. Today, with a single prompt, I can spin up a fully functioning interactive dashboard with its own database, build a live itinerary tracker for a backpacking trip, or create a custom multiplayer game for my team. Instead of sharing static slide decks or spreadsheets, sharing dynamic, hosted sites has become my primary presentation medium.

另一个实用功能是在 Codex 中使用 /visualize 命令。通过要求模型将特定数据集或使用历史可视化，它会自动生成简洁、以叙事为驱动的图表，让复杂数据一目了然。Another useful feature is using the /visualize command within Codex. By asking the model to visualize specific datasets or usage histories, it automatically generates clean, narrative-driven charts that make complex data immediately legible.

作为思考的写作 vs. 作为报告的写作Writing as Thinking vs. Writing as Reporting

主持人：众所周知，你撰写的产品简报极其精炼犀利。在模型可以毫不费力地生成文本的时代，你会如何着手写作？Host: You are known for producing exceptionally sharp product briefs. How do you approach writing in an age where models can generate text effortlessly?

塔拉·塞尚：我严格区分两种写作：作为报道的写作和作为思考的写作。Tara Seshan: I draw a strict distinction between two types of writing: writing as reporting and writing as thinking.

作为报告的写作包括状态更新、发布计划和每周总结。我很乐意使用 AI 将这些工作自动化。Writing as reporting includes status updates, launch plans, and weekly summaries. I happily automate those using AI.

将写作作为思考，是梳理产品战略、评估权衡取舍并确定核心假设的过程。我从不把这件事外包给 AI。列提纲、起草文字和编辑修改，都是我完善想法的方式。Writing as thinking is the process of working through product strategy, evaluating trade-offs, and defining core hypotheses. I never outsource that to an AI. Outlining, drafting prose, and editing are how I refine ideas.

在 Stripe，长篇文档曾是思考成果得以持久留存的载体。如今，长篇文字已不再是深度工作的确凿证明，因为任何人都可以生成冗长的文档。模型、原型和实证实验结果如今更具说服力。当我确实撰写简报时，我会争取完成大约 70%，然后与同事分享草稿，以便我们共同对这些想法进行压力测试和打磨。At Stripe, long documents served as durable artifacts of thinking. Today, long text is no longer definitive proof of deep work because anyone can generate a verbose document. Mocks, prototypes, and empirical experiment results are now far more persuasive. When I do write briefs, I aim for roughly 70% completion and then share the draft with colleagues so we can stress-test and polish the ideas together.

Sutter Hill Ventures 的产品营销经验教训Product Marketing Lessons from Sutter Hill Ventures

主持人：您还曾在 Sutter Hill Ventures 担任驻场创业者。您从他们的孵化方法论中获得了哪些关键洞见？Host: You also spent time as an Entrepreneur in Residence at Sutter Hill Ventures. What key insights did you take away from their incubation playbook?

塔拉·谢山：Sutter Hill 证明了，打造价值数十亿美元的企业并非纯粹靠运气，而是有一套可重复的方法论。Tara Seshan: Sutter Hill showed that building multi-billion-dollar enterprise companies is not pure luck; there is a repeatable methodology.

我最大的收获是认识到产品与市场契合度至关重要，而我此前低估了这一点。叙事、市场定位以及面向客户的推介，通常都应先于软件开发。My biggest takeaway was the critical importance of product-marketing fit, which I had previously undervalued. The narrative, market positioning, and customer pitch should often precede building the software.

让一百位企业买家检验你的定位，有助于你进一步明确该解决方案为何具有变革性。一旦叙事和买家的痛点都变得清晰无误，你就可以着手打造能够兑现这一承诺的确切产品形态。Testing your positioning against one hundred enterprise buyers allows you to refine why the solution is transformative. Once the narrative and buyer pain points are crystal clear, you commit to building the exact product shape that fulfills that promise.

为知识工作调整 AI 界面Adapting AI Interfaces for Knowledge Work

主持人：将智能体的能力从编码扩展到更广泛的知识工作时，根本性挑战是什么？Host: What is a fundamental challenge in expanding agent capabilities from coding into broader knowledge work?

Tara Seshan：编码的输出具有很高的可验证性。你可以运行自动化测试、编译程序并执行代码，以验证其是否正常运行。Tara Seshan: Coding is highly output-verifiable. You can run automated tests, compile the program, and execute the code to verify that it functions properly.

知识型工作不能仅通过查看最终成果来验证。高管不能只看战略计划的最后一页幻灯片，就在不了解其背后的推理、输入和假设的情况下，认定它是正确的。Knowledge work cannot be validated solely by looking at the final artifact. An executive cannot just look at the final slide of a strategic plan and assume it is correct without understanding the underlying reasoning, inputs, and assumptions.

因此，面向知识工作者的 AI 界面必须高度重视协作、透明的推理步骤以及清晰的来源引用。用户需要能够了解得出结论的过程，从而真正建立起对输出结果的信任。Because of this, AI interfaces for knowledge workers must focus heavily on collaboration, transparent reasoning steps, and clear source citations. Users need to follow along with the journey of how a conclusion was reached to build genuine trust in the output.

闪电问答与最终建议Lightning Round and Final Advice

主持人：我们直接进入快速问答环节吧。你经常推荐的两本书是什么？Host: Let's jump into the lightning round. What are two books you frequently recommend?

塔拉·塞尚：《冲浪年代》（威廉·芬尼根著）探讨了对一门技艺的投入与热爱，无论你是否能达到传统意义上的炉火纯青；以及列夫·托尔斯泰的《安娜·卡列尼娜》，每次重读都会揭示人性与成长的新层面。Tara Seshan: Barbarian Days by William Finnegan, which explores dedication and passion for a craft regardless of whether you achieve conventional mastery, and Leo Tolstoy's Anna Karenina, which reveals new layers of human nature and growth every time you revisit it.

主持人：最近有哪些电影或文化作品给了你灵感？Host: What is a recent movie or cultural work that inspired you?

塔拉·塞尚：克里斯托弗·诺兰的《奥德赛》为审视技术转型提供了引人入胜的视角，而黑泽明的《罗生门》则展示了激进的创造力与历久弥新的叙事如何在严格的制作限制下蓬勃发展。Tara Seshan: Christopher Nolan's The Odyssey, which offers a compelling lens on technological transformation, and Akira Kurosawa's Rashomon, which demonstrates how radical creativity and timeless storytelling can flourish under tight production constraints.

主持人：你的人生座右铭是什么？Host: What is your guiding life motto?

塔拉·塞尚：托妮·莫里森关于工作的四条规则：Tara Seshan: Toni Morrison's four rules on work:

1. 无论做什么工作，都要把它做好，不是为了老板，而是为了自己。
   Whatever the work is, do it well, not for the boss, but for yourself.
2. 是你成就了这份工作，而不是这份工作成就了你。
   You make the job; it doesn't make you.
3. 你的现实生活是和家人在一起的。
   Your real life is with your family.
4. 你不是你所做的工作；你就是你自己。
   You are not the work you do; you are the person that you are.

主持人：对听众还有什么最后的建议或行动呼吁吗？Host: Any final advice or calls to action for listeners?

Tara Seshan：下载桌面应用，在网页端和移动端试用 Work 模式，并尝试构建自定义网站和可视化内容。在手机上启动异步云任务，回来时发现工作已经完成，这是体验向持久化 AI 协作者转变的绝佳方式。Tara Seshan: Download the desktop app, try Work mode on the web and mobile, and experiment with building custom sites and visualizations. Kicking off an asynchronous cloud task on your phone and returning to find completed work is a great way to experience the transition to persistent AI coworkers.