21 世纪的技能 围绕“在 AI 智能体自动化大量当今任务的世界里，熟练工作者的未来会是什么样子”，网络上已经出现了大量讨论。 随着我们最近看到越来越多的例子——AI 编程智能体已经能够处理更长、更复杂的任务——一些人开始产生一种近乎存在主义的焦虑：在一个 AI 几乎什么都能做的世界里，人类以及我们现有技能的角色还剩下什么？ 甚至连 Claude Code 的创造者 Boris Cherny 都提到，在去年 12 月底的 30 天里，他贡献的代码 100% 都是由 Claude 写的。而我们也看到了无数工程师的案例：他们同时运行几十个 AI 智能体，每个负责项目的不同部分，在多个智能体之间快速切换，在几个小时或一两天内完成过去需要数周才能完成的工程工作量。 如果 AI 可以替我们完成所有编码工作，那问题自然来了：未来还需要熟练的工程师吗？更广泛地说，在 AI 能瞬间完成当今大量工作内容的情况下，那些拥有深厚领域知识的人——无论是法律、市场、建筑，还是医疗领域——还会有位置吗？ 答案是：毫无疑问，有。事实上，我甚至认为：历史上从未有过一个时代，比现在更适合成为某一领域的高度专业人才。 如果你仔细观察一名工程师今天的工作，就会发现它远不只是写代码那么简单。 工程师需要： 理解客户需求（或来自产品经理的需求） 测试代码 将代码整合进更大的系统 确保顺利发布 修复后续出现的 Bug 定义新功能 根据客户问题规划下一阶段功能 为安全和依赖升级各种库和包 以及几十项其他任务，最终交付一个真正可运行的软件系统 AI 正在逐步能够单独完成其中任何一项任务，但工程师的真正工作，是把这一切成功地整合在一起。 而那些真正理解这些任务如何协同运作的人，正是最有能力借助 AI 创造最大价值、获得最大回报的人。 这也是为什么你认识的很多顶尖工程师，正在 AI 编程时代玩得非常开心。因为他们不再需要把同样多的时间，花在“真正创造价值的工作”和“重复、枯燥的工作”上。现在，他们可以把那些苦力活交给智能体，释放时间，专注于更有趣、更有价值的部分。 历史不会简单重复，但往往会押韵。 你可能会想：这难道不会从根本上改变工作的本质，让人类能做的事情越来越少，最终让技能变得毫无意义吗？ 事实上，历史早已给出了答案。 如果你回顾任何一个高技能职业，从 个人电脑出现之前 到 AI 时代，几乎每一份工作今天的样子，都与几十年前完全不同。 在 1982 年的电影《大审判》中，有一个场景：Paul Newman 和 Jack Warden 饰演的角色，为了一场即将到来的审判，花几个小时翻阅一整柜法律书籍做研究。 光是看那个画面，你就能感受到当年这项工作有多么漫长、繁琐。 而仅仅几年之后，一名律师就可以通过电脑，几乎瞬间检索到同样的信息。 正如 Sam Altman 所说，一个自给自足的农民，可能会觉得今天的工作“轻松又琐碎”。同样，1982 年的律师，可能会认为如今的法律工作变得太简单，几乎不需要思考或研究。 但现实恰恰相反：法律行业随着监管复杂度、法律风险的提升，变得更加复杂和精密。法律的核心技能并没有消失，反而需求更高。一个反直觉但真实的数据是： 根据美国律师协会（ABA）的统计，活跃律师人数从 1975 年的约 40 万人，增长到 2025 年的约 137.5 万人。 再看看工程行业。就在 AI 编程真正普及之前，很多事情已经比几十年前简单得多了： * 开源库可以直接下载，解决几乎所有常见问题 * 工程师不再需要亲自管理基础设施 * 不用再自己处理数据库分片、身份系统、计费系统等 但与此同时： * 软件数量前所未有地增长 * 系统复杂度也达到了历史最高 结果是：我们对专家的需求并没有减少，反而更高了。 历史告诉我们的结论是：工作不会消失，只会变形。 当某些事情因为自动化或资源丰富而变得更容易，我们对“能做到什么”的期望就会随之提高，而技能的重要性，并不会下降。 工程师需要交付的内容范围更广了；律师被要求提供的判断和建议更复杂了；银行家和咨询顾问在表达、市场理解上的要求，也提高了数倍。 技能，作为杠杆 Max Levchin 最近说得非常好：“就像我们今天认为代码生成 AI 是 CS 学位的 10 倍生产力放大器一样，再过一两年，我们会反过来认为：CS 学位是代码生成 AI 的 10 倍放大器。” 你可以把“CS”和“代码生成”替换成任何一项技能，效果都是一样的。 AI 带来的真正杠杆，来自你在所自动化领域中的知识深度。 想要真正驾驭 AI 智能体，最重要的基础能力包括： 知道该把什么任务交给 AI 知道如何正确描述这些任务 能判断任务是否被正确执行 知道在 AI 给出结果之后，下一步该做什么 更关键的是，你必须能在脑中完整地理解： * 智能体正在做什么 * 接下来应该往哪里走 * 什么样的结果才算“好” 这种判断力，才是你能否持续、规模化创造价值的关键。否则，你只是在“乱点按钮”，而不是在真正使用 AI。 具体来看： 专业 CG 动画师，知道如何引导 AI 生成最好的画面，并把它们组合成能打动观众的情绪 专业律师，知道什么时候 AI 给出的建议在现实中不可行，或者因细微语境必须推翻 专业工程师，能判断智能体是否走进了未来无法扩展的架构死胡同 专业市场人员，知道哪种信息最能打动核心受众 未来，这些工作的“操作方式”都会彻底不同。你可能已经认不出专家在键盘上输入的内容，但他们理解“自己在做什么、为什么这么做”的能力，将一如既往地重要。 工作不会因为 AI 而被简单“替代”，而是会被不断扩展，被要求交付更高层次、更新类型的成果。 我们会： 对软件要求更高 对医疗突破期待更大 对医疗从业者的专业判断要求更强 几乎所有领域，门槛都会被抬高。 举一个小例子：在 Box，我们的解决方案工程师（销售团队中高度技术化的角色），现在可以为客户构建完全定制、可运行的 API Demo，并直接嵌入客户的软件系统中。 过去，这种展示对每一个客户来说成本极高，几乎不可能。但现在，它成了一件快速完成的事情。 而当这种能力在市场中普及后，客户自然会开始期待同样高度定制化的体验。 就在 AI 自动化了“演示”和“架构”的基础工作时，解决方案工程师的角色，反而被扩展成更具创意、更复杂的工作。 AI 的另一个惊人之处在于：它不仅放大了专家，也让普通人更快成为专家。 Dylan Field 将其形容为：AI 降低了地板，同时抬高了天花板。 如今，每一个有动力的人，都拥有一个 24/7 的全能导师。 我小时候学习做网站，纯属运气好，邻居（后来成为 Box 联合创始人）正好是程序员。而现在，每个人都有这样的“邻居”。 此刻，下一个 Zuckerberg 正在因为 AI 而第一次接触编程。下一个 George Lucas，正在用 AI 构建前所未见的世界，创造过去需要数千万美元才能实现的娱乐体验。 生命科学、金融，以及几乎所有技术领域，都会发生同样的事情。 如果你保持好奇、足够主动、真正渴望学习新技能，现在是人类历史上最好的时代。 在 21 世纪，技能不会因为 AI 而消失。它们所带来的杠杆，只会变得更大。 Skills in the 21st Century There’s been a lot of debate online around what the future of skilled work looks like in a world of AI agents automating much of the tasks that we do today. 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.