至少以我们目前走在的这条道路来看，AI 最能发挥力量的方式，是与真正理解自己领域（或清楚要解决的问题）的人结合。因此，AI 最终将是增强我们的工作方式，而不是取代我们。 即便 AI 的幻觉率或错误率已经很低、而且还在持续下降， 你仍然会受到以下限制： 你是否能给予 AI 代理（agent）正确的上下文、 你是否能有效审阅 AI 的输出、 以及你是否能将它的产出融入更大的任务链条中。 在这些方面，深谙本领域、并具备高度判断力的人，天然比非专家更有优势。 一个非专家当然可以： 学习新解决方案、 更快做出原型、 快速得到问题答案、 或者如果足够有动力，也能更快成为专家。 但真正的专家能更快产出完整的工作成果，原因在于： 他们知道如何让 AI agent 转向正确方向、 知道哪些地方错了、 知道如何修复、 也知道如何将 AI 的结果整合进现实世界的流程中。 这也是为什么现在就断言未来哪些行业会被彻底颠覆，为时过早。目前来看，最好的路径仍然是：在一个领域深入下去，成为真正的专家。 未来最好的律师、工程师、科学家、市场人、设计师—— 将是那些能够最充分利用这些 AI 工具、并从中获得最大杠杆的人。 At least based on the path we’re on right now, AI will be best harnessed by people who understand the domain they’re working in (or problem they’re trying to solve). And thus, AI will ultimately augment how we work vs. replace it. With even a small and decreasing hallucination or error rate, you are ultimately bottlenecked by your ability to give AI agents the right context to work with, review the output of AI, and incorporate its work into a broader set of tasks. The people who understand their field the best, and have high judgment, are in the best position to do this over non-experts. A non-expert can learn about new solutions to problems, prototype ideas faster, get quick answers to questions, or become an expert far faster if motivated. But the experts will be able to actually produce full work faster because they will know how to veer an agent on the right track, how to review what work is wrong and how to fix it, and so on. This is also why it’s too early to claim what job sectors will be overwhelming different in the future. It’s still much better to go very deep in a field and become an expert at a category of work. The best lawyers, engineers, scientists, marketers, or designers will be the ones that get the most leverage from these tools in the future.
Aaron LevieCo-founder & CEO of Box
观点原文
至少以我们目前走在的这条道路来看，AI 最能发挥力量的方式，是与真正理解自己领域（或清楚要解决的问题）的人结合。因此，AI 最终将是增强我们的工作方式，而不是取代我们。
即便 AI 的幻觉率或错误率已经很低、而且还在持续下降，
你仍然会受到以下限制：
你是否能给予 AI 代理（agent）正确的上下文、
你是否能有效审阅 AI 的输出、
以及你是否能将它的产出融入更大的任务链条中。
在这些方面，深谙本领域、并具备高度判断力的人，天然比非专家更有优势。
一个非专家当然可以：
学习新解决方案、
更快做出原型、
快速得到问题答案、
或者如果足够有动力，也能更快成为专家。
但真正的专家能更快产出完整的工作成果，原因在于：
他们知道如何让 AI agent 转向正确方向、
知道哪些地方错了、
知道如何修复、
也知道如何将 AI 的结果整合进现实世界的流程中。
这也是为什么现在就断言未来哪些行业会被彻底颠覆，为时过早。目前来看，最好的路径仍然是：在一个领域深入下去，成为真正的专家。
未来最好的律师、工程师、科学家、市场人、设计师——
将是那些能够最充分利用这些 AI 工具、并从中获得最大杠杆的人。
At least based on the path we’re on right now, AI will be best harnessed by people who understand the domain they’re working in (or problem they’re trying to solve). And thus, AI will ultimately augment how we work vs. replace it.
With even a small and decreasing hallucination or error rate, you are ultimately bottlenecked by your ability to give AI agents the right context to work with, review the output of AI, and incorporate its work into a broader set of tasks. The people who understand their field the best, and have high judgment, are in the best position to do this over non-experts.
A non-expert can learn about new solutions to problems, prototype ideas faster, get quick answers to questions, or become an expert far faster if motivated. But the experts will be able to actually produce full work faster because they will know how to veer an agent on the right track, how to review what work is wrong and how to fix it, and so on.
This is also why it’s too early to claim what job sectors will be overwhelming different in the future. It’s still much better to go very deep in a field and become an expert at a category of work. The best lawyers, engineers, scientists, marketers, or designers will be the ones that get the most leverage from these tools in the future.
ABAB AI 解读
这段话的核心不是“AI 不会替代工作”，而是：
真正能驯服 AI 的，是拥有专业知识 + 高判断力的人。
AI 的杠杆最终会被“专家阶层”垄断，而非大多数普通用户。
让我们拆解。
① AI 不是“自动替代人类”，而是“需要懂行的人类才能驾驭”
作者开门见山：AI 最强的使用方式，是与深度理解领域的人结合。
这句话推翻了一种常见幻想：
- “AI 会取代医生”
- “AI 会取代律师”
- “AI 会取代工程师”
作者的意思不是这些岗位永不消失，而是：AI 本身没有“判断力”，它只是在执行你给的上下文。你越懂领域，你越能让 AI 变得强。
换句话说：AI 的真正力量来自“人与 AI 的协作质量”，而不是 AI 的原始能力。
② AI 的瓶颈不是模型，而是“你给它的上下文”
关键句：“你最终会受制于——你给 AI 的上下文是否正确、
你能否审阅 AI 的结果、以及你能否将它融入更大的任务链条。”
这句话非常重要。
AI 的限制：
- 它不知道真实世界
- 它不知道对错是什么
- 它无法验证自己
- 它不知道业务逻辑
- 它不知道语境与隐含规则
- 它不知道你真正想要什么
- 它不知道哪些细节至关重要
人类的限制：
- 如果你不懂业务，就给不出正确指令
- 如果你不会判断，就无法审查结果
- 如果你不懂系统，就无法整合输出
所以：AI 的效能由人类输入的质量决定。
而输入质量取决于：
- 领域知识
- 判断力
- 问题定义能力
- 审查能力
③ 非专家确实“进步更快”，但专家保持“产出更强”
作者说：“非专家可以更快学习、原型更快、问答更快、变成专家速度更快。”
这是：AI = 拉平学习门槛。
但另一句才是重点：“真正的专家能更快地产出完整结果。”
原因：
- 专家知道“方向”
- 专家知道“关键变量”
- 专家知道“常见错误”
- 专家知道“正确成果长什么样”
- 专家能立刻识破 AI 的错误并修正
AI 可以让普通人进步，但无法让普通人立刻拥有专家的判断力。
判断力比知识更难学，而判断力正是 AI 无法替代的部分。
④ 为什么现在还不能说“哪些行业会被完全替代”？
一句关键话：“现在判断哪些行业会被彻底重塑，为时过早。”
原因：
- AI 的实际落地需要产业配套
- 行业结构变化需要监管
- 企业流程需要重写
- 人类组织方式需要适应
- 判断力无法简单替代
- 从 Steam Engine 到互联网，所有技术革命都需要 10–30 年。
因此：AI 的影响深远，但不是立刻改变所有行业。个人的深度专业能力依然极其重要。
⑤ 最终的赢家：在自己专业领域能把 AI 当“倍增器”的专家
作者结尾说：“最佳律师、工程师、科学家、市场人、设计师将获得最大 AI 杠杆。”
意思是：AI 会让“高手变更强”，而不是让“普通人成为高手”。
重要洞察：
- AI 会提高专家 10× 的产出
- 但不会让新手跨越 10 年经验差距
- 最终 AI 会加剧“顶尖人才的优势”，而不是减少差距
这是未来就业市场一个关键趋势：AI 不是平均化，而是放大化。放大判断力、放大经验、放大专业深度。
ABAB AI 总结（精华版）
这段话的核心结论：
- AI 的最大瓶颈不是 AI，而是使用者的专业判断力。
- AI 会让专家更强，不会马上消灭专家。
- 非专家学习更快，但生产完整成果仍比不上专家。
- 现在预测哪些行业消失太早，因为 AI 落地需要时间。
- 最终赢家不是“懂 AI 的人”，而是“在专业领域能驾驭 AI 的人”。
一句话总结：
AI 不会淘汰专家，AI 会淘汰不会用 AI 的专家。
AI 会增强顶级人才，而不是替代他们。
AISharing
相关观点
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With all the examples we’ve seen most recently of AI coding agents being able to handle longer and more complex tasks, this has created a sort of existential dread in some around what the role of humans and existing skills will be in a world where AI can do almost anything. Even Boris Cherny, the creator of Claude Code, noted that 100% of his contributed code was written by Claude in the past 30 days in late December. And we’re seeing countless examples of engineers that are running up to dozens of agents in parallel, all handling different parts of a project, swiftly jumping between the work of each one, doing the equivalent of weeks of engineering work in hours or a couple of days. If AI can do all the coding for us, this begs the question, is there even a role for skilled engineers in the future? And more broadly, is there a role for anyone with deep domain expertise -whether it’s legal, marketing, architecture, or medical fields- in a future where AI can instantly do so much of what today’s jobs look like? The answer, emphatically, is yes. In fact, I’d argue, there has never been a time in history where it’s been better to be a highly skilled in a particular field. When you assess what an engineer does today, the process is far more complex than just writing code. You’re taking in requirements from the customer (or from a product manager, etc.), testing your code, integrating it with a broader project, ensuring that it ships properly, fixing future bugs and defining new features, handling issues as they arise, coming up with the next set of features to build based on customer problems, upgrading various libraries and packages for security issues and new dependencies, and dozens of other individual tasks that deliver real, working software. AI increasingly can do any single one of these tasks individually, but job of an engineer is putting all the work together, successfully. And those that understand how those tasks all come together are in the best position to deliver the most value, and get the greatest upside, with AI. This is why many of the best engineers you know are having the time of their life right now with AI coding. Because instead of having to spend a uniform amount of time on both the parts of coding that are value-add and differentiating from the stuff that’s rote, you can deploy tasks to agents that offload to the drudgery parts, freeing up more time for the work that is fun and delivers the real value. History doesn’t repeat itself, but it often rhymes Now, you may be wondering, this must fundamentally change the nature of work in a way that leaves less to be done by people, ultimately rendering skills useless, right? In fact, there’s already plenty of historical precedent for this evolution of work. If you look at the processes of any high-skill job starting from the pre-PC era to the AI era, nearly every single job would look fundamentally different today than it did just a few decades ago. There's a scene in the 1982 movie The Verdict where Paul Newman and Jack Warden's characters are combing through volumes of legal books for hours while doing research for an upcoming trial. You can just feel from watching the scene how long and tedious this process once was. Just a few years later, a lawyer would have pulled up a computer and retrieved the same data nearly instantly. Just as Sam Altman said a subsistence farmer might look at today’s jobs as way too easy and trivial, the lawyer from 1982 might imagine how easy law has become and devoid of critical thinking or research. And yet, the nature of the legal profession has only become more complex and sophisticated over time with a growing range of regulatory challenges, legal risks companies face, and so on. The foundational skills of the legal profession have been largely unchanged, and counterintuitively, despite many efficiency gains over time, the ABA pegs active attorneys having gone from roughly 400,000 in 1975 to roughly 1,375,000 in 2025. Or take that modern engineering job, right before the moment AI coding went mainstream. Many things have gotten simpler in the past few decades of coding: there are open source libraries you just pull down instantly for almost every problem some other engineer has once dealt with in the wild, and you no longer have to think about managing infrastructure, sharding databases, handling identity services, doing billing, or an array of tasks that every engineer had to do themselves just a couple decades back. And yet, we now have more software than ever before to manage and more complexity in our systems than ever before; and as a result, no less of a need for experts to help with all this work. What we know from history is that the jobs simply morph over time. We make one thing easier through automation or abundance, and as a result our expectations begin to grow on what we can now do in that space, and the skills become just as important. The expectations of what an engineer must deliver on today has just grown in scope, the demands of a lawyer’s advisory capabilities just increase, and the presentation and market knowledge you expect from your banker or consultant has gone up tenfold. Skills as leverage Max Levchin said it best just this week: “just as today we think of codegen AI as a 10x productivity multiplier for CS degrees (or equivalent), in a year or two we will be thinking of CS degrees as 10x productivity multipliers over codegen AI.” You can replace “CS” and “codegen” for any skill and see the same effect. The real leverage from AI comes from your depth of knowledge in the field you’re bringing automation to. One of the most important foundations for getting ahead with AI agents is being able to understand what tasks to give them, how to frame those tasks, how to know when those tasks aren’t being executed successfully, and then figuring out what to do *after* the AI has delivered the task. Critically, being able to hold a full mental model in your head of what the agent is doing, where it should go next, what “good” should look like is the critical judgment necessary to produce real value with agents vs. just use them to take shots in the dark. Without this underlying knowledge you’re going to have a very hard time producing ongoing value at any reasonable scale or frequency. The expert CG animator will know the right ways to get the AI to produce the best scenes, and then stitch them together the best to get emotion out of the audience; the expert lawyer knows when advice for a case would be impractical or when the AI should be overridden due to subtle nuance about a situation; the expert engineer knows when an agent has backed itself into an architectural dead-end that won’t scale for future features; and the expert marketer knows the message that will resonate with their core audience best. By today's standards, all of these jobs will look fundamentally different in the future. We'll barely recognize the things that the experts are typing into their machines to do their work, but all of their underlying knowledge and skills of what they are doing and why will be as necessary as ever. And instead of people and companies simply using AI agents to automate the work that we already do manually, jobs will simply be expanded over time to expect higher, or new types of output. We’ll demand far more from our software, we’ll expect more medical breakthroughs, require better expertise from our healthcare providers, and bar will just rise in essentially every other domain out there. As a minor example of this dynamic, at Box, we now have solutions engineers (the deeply technical counterparts to our sales team) build fully custom, working demos of Box’s APIs embedded into our customer’s software. Instead of a static, generic demo, they can now bring the full platform to life in a way that would have been cost prohibitive to demonstrate for every client before. But now it’s a quick task. As this trend propagates across the market, customers will soon expect similarly customized experiences when they’re pitched by software companies. Just at the same moment when the rote task of pitches or architectures gets automated by AI, a new set of more creative and elaborate tasks become required as a part of a solution engineer’s job. And here’s the amazing thing with AI. Just as it makes the experts far more capable than before, it also makes it so anyone can learn to become an expert much faster. Dylan Field frames this as AI lowering the floor and raising the ceiling. In his case, it means anyone who’s motivated can now learn how to do amazing design if they want. The tools that every young person now has available to them is unprecedented. Anyone, if they so choose, has a complete tutor available to them in every skill on the planet. When I was growing up and getting into building websites, it was only by sheer coincidence that my neighbor (and ultimately a Box cofounder) happened to be a programmer that could help multiply my skills; but now everyone has that neighbor available to them 24/7. Right now, the next Zuckerberg is getting into coding for the first time because of AI. The next George Lucas is someone developing AI worlds that have never been seen before, and producing a level of entertainment that would not have been possible even a couple of years ago without tens of millions of dollars. The same will be true in life sciences, finance, and every other technical field out there. This is the greatest time in history if you’re curious, resourceful, and are fundamentally want to learn and pick up new skills. Skills don’t go away in the 21st century with AI. The leverage you get from them have only gone up.Aaron Levie · 2026/01/11
- 当下，对任何具备创业精神的人来说，在自己所在公司里最大的机会之一，就是成为那个帮助组织理解并部署 AI 智能体（AI agents）的人。 部署智能体并不容易。目前真正掌握这项能力的人仍然非常有限。虽然几乎所有最佳实践都能在网上找到，但前提是——你必须持续、高强度地关注这个领域的每一个新进展：包括编程型智能体、记忆机制、文件系统与工具调用、Agent 框架（agent harnesses）、上下文工程（context engineering）等等。 更重要的是，这个领域的最佳实践会不断复利式叠加。这意味着：你领先得越早，就越容易理解并部署下一波新出现的能力。 如果你能做到这一点，那么在你加入的任何一家公司里，你都会看起来像是拥有一台时间机器的人。 这是一个巨大的机会。 One of the biggest opportunities available to anyone entrepreneurial right now in their company is to be the person that helps their organization understand and deploy AI agents. Deploying agents is hard. There is still a relatively narrow universe of people with this skill. While all of the best practices are available online, you have to be paying attention to every new thing that comes out around coding agents, memory, filesystem and tool use, agent harnesses, context engineering, and so on. And the best practices in this space just continue to compound, which means the farther ahead you are the more you understand about how to deploy what comes up next. So if you can do that, you will look like you have a time machine to any company you join. Huge opportunity.Aaron Levie · 2026/01/07
- 上下文时代 彼得·德鲁克（Peter Drucker）——管理与商业领域最重要的思想家之一（也是“知识工作者”一词的提出者）——在 1990 年代就指出：“知识已经成为关键的经济资源，并且是占主导地位、甚至可能是唯一的竞争优势来源。”他对 AI 时代的预判，几乎精准到不可思议。 随着 AI 进步持续不减，AI 模型将越来越能够增强或自动化各种知识工作者的任务。正如我们在编程领域看到的那样，这些模型将演化为 AI 代理：在几乎所有工作领域中，成为专家级律师、医疗专业人士、商业战略家、科学研究者等。 但默认情况下，这些模型并不了解你的具体团队或组织。它们可能瞬间被要求为 福特审查法律合同，下一秒又为 高盛编写新软件代码。它们是完全通用的超级智能系统，可以为任何提出请求的人执行任何任务。其结果是：你的公司与另一家公司拿到的是同一个“专家律师”、同一个“工程师”。 因此，我们必须面对的问题是：在人人都能获得同等智能的世界里，公司如何实现差异化？ 当然，差异化的一部分来自团队和员工如何高效使用 AI 代理；但真正的终极放大器将是代理所获得的上下文（context）。这些上下文包括：要打造的正确产品、服务客户的方式、要进入的市场、客户互动的具体细节、组织内部的“部落知识”，以及随着时间积累、授权或并购而形成的、只属于你公司的大量专有信息。 过去一年，“上下文工程（context engineering）”为解决这一问题而迅速发展，但它并不容易。想象一下：一个默认对你组织一无所知的专家律师或工程师，你却只有一份文档的空间来向他说明其全部工作内容、可使用的系统、所需数据、目标等等。把正确的信息交到代理手中，成为提升生产力的关键前提。 事实上，这个问题在 AI 代理出现之前就困扰着企业。公司一直是各种上下文的集合体，试图从中榨取最大价值：流程、知识产权、独特想法与路线图、决策方式、客户信息等等。然而，随着规模扩大，企业只能利用其中很小一部分知识（无论是否已数字化）。惠普（HP） 90 年代的 CEO 卢·普拉特（Lew Platt） 曾说：“如果 HP 知道 HP 所知道的一切，我们的生产力将提升三倍。” AI 代理终于让这一愿景成为可能。我们第一次能够真正动用沉睡在组织内部的知识与信息。Atlassian 的 迈克·坎农-布鲁克斯（Mike Cannon-Brookes） 将其形容为一种“数据的梅特卡夫定律”：就像网络效应一样，数据越多，整体系统越强。 这意味着，在 21 世纪，最关键的竞争优势之一，将是企业捕获、管理并围绕正确上下文构建流程的能力。 拥有更好市场定价与租金洞察的房地产公司会赢得更多客户；能利用海量数据开发新药的制药公司会创造更多收入；能产出更好营销活动的代理机构会更具差异化。 然而，把正确的数据（上下文）交给代理，在规模化时是个难题。正如 Foundation Capital 的 贾娅·古普塔（Jaya Gupta） 和 阿舒·加格（Ashu Garg） 在其关于上下文图谱的犀利文章中所指出的那样，许多 AI 代理所需的关键决策轨迹，并不存在于任何现有软件中。而这只是代理所需上下文的一种：它们还需要客户数据、企业文档、对话历史、项目时间线、研究数据、营销素材、财务记录、代码库、HR 信息等等。 因此，设计系统以让代理访问这些数据，并确保所有代理能在这些数据上互操作，至关重要。在一些情况下，现有的系统将成为这些数据与代理的合理“归宿”；而在另一些情况下——尤其是当今天还不存在相应软件时——我们将需要全新的工具。即便如此，未来十年里，数据访问控制与权限冲突、治理与合规、以及连接分散的企业系统，都将是持续的工作。 此外，企业还必须推进大量变革管理。我们曾以为 AI 会适应我们的工作方式，但事实证明，鉴于其强大的能力与内在限制，我们反而需要去适应 AI 的工作方式。这意味着必须优化组织与工作流，以便为代理提供成功所需的上下文。其核心原则是：用户现在要负责指挥和引导代理的工作，并确保在过程中提供正确的上下文。 本质上，当下的个人贡献者，将在未来成为“代理的管理者”。他们的新职责包括：监督与升级路径、跨代理的协调，以及在不同代理之间“护送”工作——就像前 AI 时代的团队管理者一样。 总体趋势已经十分清晰：我们正进入“上下文时代”。 能够积累并最好地利用上下文的团队与公司，将实现最高的生产力与产出；做不到的，将越来越难以具有竞争力地服务客户。正如彼得·德鲁克当年所预言的那样。 The Era of Context Peter Drucker, one of the most important thinkers on management and business (and coiner of the term knowledge worker), in the 90s said, "knowledge has become the key economic resource and the dominant-and perhaps even the only-source of competitive advantage." He couldn’t have predicted the AI era more accurately. As AI progress continues unabated, AI models will get more and more capable of augmenting or automating a variety of knowledge worker tasks. Just as we've seen in coding, these models will morph into AI agents that are expert lawyers, healthcare professionals, business strategists, scientific researchers, or other roles in almost every domain of work. But these models, by default, don't know anything about your particular team or organization. In an instant, they might be asked to review a legal contract for Ford, and then in the next second be writing code for a new piece of software at Goldman Sachs. They are fully general-purpose superintelligence systems that can take on any task for anyone that’s asking. As a consequence, that means that your company is getting the same expert lawyer as another company, the same engineer, and so on. The question that we will have to wrestle with is, in a world where everyone has access to the same intelligence, how does a company differentiate? Certainly it will be about how teams and employees use AI agents effectively, but the ultimate force-multiplier will be the context that the agents get. Context about the right products to build, the ways to serve customers, the markets to go after, the specific details of client interactions, the tribal knowledge in an organization, and endless amounts of other proprietary information to your company that's developed over time, licensed, or acquired. Context engineering has blown up over the past year to tackle just this, and it's not an easy problem. Just imagine taking an expert lawyer or engineer that by default knows absolutely nothing about your organization, and you only have a single document’s worth of space to describes their entire job, every system they have to leverage, all the data that they are supposed to work with for that particular task, what their objectives are, and so on. Getting the right information to them becomes a critically important requirement to drive their productivity. Now, this problem has already plagued companies well before agents existed. Companies have always been a collection of various forms of context that they try and extract the most value out of: their processes, intellectual property, unique ideas and roadmap, ways they makes decisions, information about their customers, and so on. And companies, as they scale, have only been able to utilize a small portion of this knowledge (digitized or not). Lew Platt, the CEO of HP in the 90s, once said, “if HP knew what HP knows, we would be three times more productive.” AI Agents finally make this vision possible. We can, for the first time, begin to tap into this wealth of knowledge and information sitting inside of our organizations. Mike Cannon-Brookes of Atlassian once framed this as a sort of a Metcalfe's law of data. Like the original theory on network effects, the more data you have, the more powerful the overall system becomes. This means that in the 21st century, one of the most critical forms of competitive advantage will be a company's ability to capture, manage, and build processes around the right context. The real estate firm that has better insights on market pricing and rent dynamics will get more clients, the pharmaceutical company that can develop and design new drugs using reams of data will generate more revenue, the marketing agency that can generate better campaigns will differentiate more. Now, getting the right data (or context) to agents is a hard problem at scale. As Jaya Gupta and Ashu Garg at Foundation Capital pointed out in their provocative essay on context graphs, many of the critical decision traces for AI agents to operate with either aren't in any existing software today. And this is just one example of the kind of context that agents will need to operate on. They’ll need customer data, enterprise documents, conversation history, project timelines, research data, marketing assets, financial records, codebases, HR information, and much more. Designing our systems to get agents access to that data, and ensuring that all of our agents can interoperate on that data is going to be incredibly important. In some cases existing systems of record will become the logical houses for that data and the corresponding agents; and in other cases -especially where there is no such software today- we will need all new tools, just as Jaya and Ashu laid out. Even then, managing the myriad conflicts that emerge in data access controls and permissions so agents don’t leak data, governance and compliance challenges around what things an agent can execute on, and connecting our disparate enterprise systems to make this work is going to be an ongoing work for the next decade. Further, companies will have to drive a substantial amount of change management to make this all work. We imagined that AI systems would adapt to how we work, but it turns out due to their extreme power (and inherent limitations) we will instead adapt to how they work. This means we will have to optimize our organizations and workflows to best enable context for agents to be successful. The core tenant of this change is that the user is responsible now for directing and guiding agents on how to do their work, ensuring it gets the right context along the way. In essence, the individual contributor of today becomes the manager of agents in the future. Their new responsibilities will be providing the oversight and escalation paths, a meaningful amount of coordination throughout the work that the agents are doing, and shepherding work between the various agents - just like managers of teams in the pre-AI era. The overall trend is clear, and we are entering the era of context. The teams and companies that can accumulate and best utilize context will drive the greatest productivity and highest output. Those that don’t will find it harder and harder to serve customers competitively. Just as Peter Drucker predicted.Aaron Levie · 2026/01/05
- 这就是为什么上下文工程（context engineering）如此重要。 我们刚刚花了 2 个小时，仅仅围绕一种问题类型，讨论一个智能体究竟应该依赖自身的内部知识，还是尝试在数据中寻找相关上下文。而在数百个测试用例中，我们只讨论完了 2 个。 甚至连参与这次头脑风暴的人，也无法就“在这种情况下，人类会怎么做”达成一致。这里根本不存在唯一正确的答案，而且答案始终是高度依赖具体情境、并且因客户而异的。 上下文工程中的一切，都是在多个维度之间做取舍： 你希望智能体多快给出答案？ 你希望用户需要多少来回交互？ 在尝试回答问题之前，它应该先做多少准备工作？ 它如何判断自己已经拥有足够完整的来源材料来回答问题？ 给出错误答案的风险有多高？等等。 你在其中任何一个维度上做出的决定，都会在另一个维度上产生后果。没有免费的午餐。这正是为什么构建 AI 智能体如此疯狂、也如此困难。 这也凸显了一个事实：在 LLM 之上，仍然蕴藏着巨大的价值空间。把这些决策做对，直接决定了产品价值主张的质量。 Here’s why context engineering is such a big deal. We just spent 2 hours debating when an agent should rely on its internal knowledge vs. trying to find relevant context within data for just one type of question. We got through 2 test cases of hundreds. Even the people involved in the brainstorm couldn’t all agree on what they would expect humans to do in this situation. There truly was no right answer, and it’s always context specific customer by customer. Everything in context engineering is a tradeoff between a variety of factors: how fast do you want the agent to answer a question, how much back and forth interaction do you want to require for the user, how much work should it do before trying to answer a question, how does it know it has the exhaustive source material to answer the question, what’s the risk level of the wrong answer, and so on. Every decision you make on one of these dimensions has a consequence on the other end. There’s no free lunch. This is why building AI agents is so wild. It also highlights how much value there is above the LLM layer. Getting these decisions right directly relates to the quality of the value proposition.Aaron Levie · 2025/12/24