The following article originally appeared on Addy Osmani’s blog site and is being republished here with the author’s permission.本文最初发表于 Addy Osmani 的个人博客，经作者许可在此转载。
If the AI layer gets good at anything, it will be anything that has an answer key. School used to be answer keys all the way down. School is the ultimate anchoring of success, because it’s all about getting the right answer. The thing that makes work durable and ungradable in the age of AI is not getting any better at solving problems. It’s not being able to build systems or understanding people or making cool new things. It’s choosing what to build and judging if it’s good. The rest will all be done better and faster by AI.如果说 AI 层在什么方面表现出色，那就是任何有标准答案的事情。学校教育曾经充斥着各种标准答案，它是成功的终极锚点，因为一切都围绕着“得出正确答案”展开。在 AI 时代，让工作具有持久价值且无法被轻易量化考核的，不是更擅长解决问题，也不是构建系统、理解他人或创造新事物，而是决定“做什么”以及判断“做得好不好”。其余的一切，AI 都能做得更好、更快。
I started in engineering at 16, building a browser in rural Ireland. I was at Google for over 14 years, where I led engineering teams working on Chrome, Gemini, and Cloud AI, and I’ve written a number of O’Reilly books. I’ve turned down offers from frontier labs and FAANG companies when the fit wasn’t right. Good people are always needed, so we each have an obligation to try our hardest and make the best thing we can.我 16 岁开始从事工程工作，在爱尔兰农村编写过浏览器。我在 Google 工作了 14 年多，领导过 Chrome、Gemini 和云端 AI 的工程团队，还写过几本 O'Reilly 的书。当机会不合适时，我也曾拒绝过前沿实验室和 FAANG 公司的邀请。优秀人才总是稀缺的，所以我们每个人都有义务尽己所能，做出最好的东西。
Most career advice still holds up. Get on the rocket ship; don’t overoptimize your seat. The specifics have changed a little because of agentic coding, but here’s what I wish I’d known for ambitious engineers out there now.大多数职业建议依然适用：搭上火箭，别太纠结自己的座位。由于智能体编程（agentic coding）的出现，具体细节发生了一些变化，但以下是我希望现在的雄心勃勃的工程师们能了解的内容。
Optimize for scarce resources. Almost nothing I’m known for came from chasing the highest pay. The years I spent in open source had almost zero direct payoff. But they led to reputation and relationships that very efficiently compounded into opportunities later. I would have spent the comp I got from any single job. My reputation kept paying.优化稀缺资源。我所获得的成就几乎没有一个是靠追求最高薪水得来的。我花在开源项目上的那些年，几乎没有任何直接回报，但它们为我带来了声誉和人脉，并在后来非常高效地转化为机会。任何一份工作的薪水我可能转眼就花掉了，但声誉却一直在为我创造价值。
Many resources are abundant. Capital is abundant. Time is abundant. Real relationships, and especially a track record of doing good work, are still scarce. I can raise money in a couple weeks, but I can’t raise a reputation. So here’s the plan: Do good work, and make sure the people who like good work see it. In a world where vibe coding makes earning a quick buck trivial, I think that quick buck is worth very little. When shipping stuff is so easy, the scarce move is choosing something worth shipping.许多资源是丰富的，比如资本、时间。但真正的人际关系，尤其是良好的工作记录，依然稀缺。我可以在几周内筹集到资金，却无法筹集到声誉。所以计划是这样的：做好工作，并确保那些欣赏优秀工作的人能看到它。在一个“氛围编程”（vibe coding）能轻松赚快钱的世界里，我觉得这种快钱没什么价值。当发布产品变得如此容易时，稀缺的举动是选择值得发布的东西。
Learn to find problems, not just solve them. The first time I ever felt the burden of selection rather than solution, LeetCode seemed a measure of skill. But as agents absorbed all that work, solving problems went cheap while selecting them became scarce. My origin story: I noticed dial-up was slow, created chunked multiconnection fetching, realized I’d never solve that problem in my life, and quickly moved on to whatever absorbingly complex one I could find next. Finding problems predated solving them.学会发现问题，而不仅仅是解决问题。我第一次感受到“选择问题”而非“解决问题”的负担时，LeetCode 似乎是衡量技能的标准。但随着智能体承担了所有这些工作，解决问题变得廉价，而选择问题变得稀缺。我的起源故事是这样的：我注意到拨号上网很慢，于是创建了分块多连接抓取技术，意识到我这辈子不可能彻底解决那个问题，就迅速转向了我能找到的下一个极其复杂的问题。发现问题先于解决问题。
I’ve watched students who were wildly good fall flat on their face when an agent ran through their problem set (like watching the wrong microwave number on the clock). The same agent. The same problem set. Wildly different token and time budgets. Why? Because at the end of the day, the strong ones bring judgment and intuition to the work; the rest bring a prompt.我见过一些极其优秀的学生，当智能体处理他们的习题集时，他们却一败涂地（就像看着时钟上错误的微波炉数字一样）。同样的智能体，同样的习题集，但 Token 和时间预算却大相径庭。为什么？因为归根结底，强者在工作中投入了判断力和直觉，而其他人只是输入了一个提示词。
I used to build that judgment by grinding out boilerplate and fixing bugs. I got to see and deeply feel the worst abstractions humans could devise. I approached each commit with the awe of someone who’d just seen the fever dream of previous authors. Each commit brought hindsight and judgment. The agents automate those reps. Taste is pattern-matching, but all that pattern-matching has to be earned by doing the work.我过去通过处理样板代码和修复 Bug 来建立这种判断力。我见过并深刻体会过人类能设计出的最糟糕的抽象。我以一种“刚刚目睹了前人狂热梦境”的敬畏感来对待每一次提交。每一次提交都带来了反思和判断。现在的智能体自动化了这些重复工作。品味源于模式识别，但所有的模式识别都必须通过实践来获得。
The real risk isn’t agents writing bad code. We’ve been there before. It’s losing the ability to tell. Judgment will atrophy. Output will look a lot like working code.真正的风险不是智能体写出烂代码——这种事我们以前也经历过。真正的风险是丧失了辨别能力。判断力会退化，而输出结果看起来却很像可运行的代码。
Good practitioners don’t put agents in front of everything. They engage in deliberate practice. Pick a few problems that really matter. Do them the hard way, without the agent, building deep mental models of how systems and languages work. Read a thousand times more code than you ever write. Treat every diff from an agent like a human review you need to carefully justify. Go deep on at least one system end to end, from intake to output. On a daily basis, keep a private log of every time you see an agent suggest something that looks wrong and confidently flag it. That’s where taste accumulates.优秀的从业者不会把智能体放在一切事物的前面。他们会进行刻意练习。挑选几个真正重要的问题，用最困难的方式去解决，不依赖智能体，从而建立起关于系统和语言如何工作的深层思维模型。阅读的代码量要比你写的代码量多一千倍。把智能体的每一次差异（diff）都当作需要你仔细论证的人类评审。至少深入研究一个端到端的系统，从输入到输出。每天记录一份私人日志，记下每一次你发现智能体建议错误并自信地标记出来的时刻。这就是品味积累的地方。
The real thriving engineers won’t be the fastest at getting suggestions. They’ll be the ones who know instantly when to say no.真正脱颖而出的工程师，不会是获取建议最快的人，而是那些瞬间知道何时说“不”的人。
Shift from doing to directing. Just like you’d delegate to a person, you need to learn to delegate to an agent. Scope the task, define done, calibrate trust, and verify the result.从“执行”转向“指导”。就像委派任务给他人一样，你需要学会如何委派给智能体。界定任务范围、定义完成标准、校准信任度并验证结果。
Autonomy is a setting, not a rank; it’s a per-task switch. Turn it up to the maximum on something small and reversible and cheap to check. Turn it down on anything where mistakes will be hard to undo.自主权是一个设置，而不是职级；它是针对每个任务的开关。在小型、可逆且易于检查的任务上，将自主权开到最大；在任何难以撤销错误的任务上，将其调低。
Specification and verification are two distinct, complementary skills. The agent isn’t as good as the intent you hand it. The best engineers are those who know how to write precise specs; clear thinking made legible.规范和验证是两种截然不同但相辅相成的技能。智能体的表现不会超过你交给它的意图。最好的工程师是那些懂得如何编写精确规范的人；这是将清晰的思考转化为可读的指令。
It’s verification, not evidence. Not evidence in the form of an agent grading its own homework. There’s nothing more demoralizing than delegation without verification at scale.这是验证，不是证据。不要把智能体给自己批改作业的结果当作证据。在大规模任务中，没有验证的委派是最令人沮丧的。
Own what you ship. If the agent wrote it and it breaks in production, “the AI did it” is not a defense. Your name is on the change. Adopt the posture of an accountable human who understands what went out the door and how to fix it.为你发布的内容负责。如果代码是智能体写的，且在生产环境中崩溃了，“是 AI 写的”不是借口。你的名字在变更记录上。采取一名负责任的人类应有的姿态：了解发布了什么，并知道如何修复它。
Solve the most ambitious version of the problem. Rich Sutton’s bitter lesson: In almost every field, general methods that scale with additional compute beat out hand-tuned equivalents. As a career lesson, there’s no point in solving an easy version of the problem—it’s worth almost nothing. The value ends up concentrated in the hard version.解决问题的最雄心勃勃的版本。Rich Sutton 的“苦涩的教训”指出：在几乎所有领域，随计算能力扩展的通用方法，最终都会胜过手工调整的等效方案。作为职业教训，解决简单版本的问题毫无意义——那几乎一文不值。价值最终集中在困难的版本上。
Sprint the last mile. No turnkey agent writes a whole system from end to end. As a rule, you’ll get 70% of a feature quickly from an agent, and the last 30%—debugging the gnarly edge cases, figuring out the right architecture, cultivating the right taste—will be the whole game. The median output today is whatever the agent produces from some lazy prompt. The only personal value you can bring to the table is getting as far as you possibly can past that median. When first drafts come free, finish is the product. To sprint the last mile, here’s my tactic: Every few months I completely rebuild from scratch using the latest sharp-end-of-the-sword model. It’s less exhausting than nursing half-hearted old code to health.冲刺最后一英里。没有现成的智能体能从头到尾写出一个完整的系统。通常情况下，你能从智能体那里快速得到 70% 的功能，而剩下的 30%——调试棘手的边缘情况、找出正确的架构、培养正确的品味——才是整个博弈的核心。今天，平庸的产出就是智能体根据一些懒散的提示词生成的内容。你能带来的唯一个人价值，就是尽可能超越那个平庸水平。当草稿变得廉价时，完成度就是产品。为了冲刺最后一英里，我的策略是：每隔几个月，我就会使用最新的尖端模型从零开始彻底重构。这比维护那些半心半意的旧代码要轻松得多。
My job as a software engineer has been to finish strong. The difference between finishing strong and finishing okay is the polish: spending an extra hour, which shows instantly to everyone who matters.作为一名软件工程师，我的工作就是完美收尾。完美收尾与草草了事之间的区别在于打磨：多花一小时，这在所有关键人物眼中都是立竿见影的。
Increase both your xG and your finishing提高你的预期进球数 (xG) 和终结能力
If soccer had a stock ticker, it would be xG. xG measures the number of chances your play should produce. Finishing measures whether you convert them. You can’t plan the number of chances you get, but you can hope your play produces enough, and over your career you can get better at finishing them.如果足球有股票行情显示器，那一定是 xG（预期进球数）。xG 衡量你的战术应该创造多少机会，而终结能力衡量你是否能将这些机会转化为进球。你无法规划自己能获得多少机会，但你可以希望你的战术能产生足够的机会，并且在职业生涯中提高终结能力。
The same is true of careers: Your reputation gets you in front of goal, and you convert them with good judgment. Chances arrive whether you’re ready for them or not; how many you get, and which ones you finish, is up to you. I’ve only ever had big opportunities as a result of work I’ve done in public, never from a job I’ve applied for. You can’t script which chances arrive, only whether you’re standing where they land. You have to create the opening as much as you can, and then be ready to take it.职业生涯也是如此：声誉让你站在门前，而良好的判断力让你完成终结。机会无论你是否准备好都会到来；你能获得多少机会，以及能终结多少机会，取决于你自己。我获得的所有重大机会，都是因为我公开所做的工作，而不是因为我申请的职位。你无法预设机会何时到来，只能决定自己是否站在机会降临的地方。你必须尽可能地创造缺口，然后准备好抓住它。
One easy mistake is anchoring on whatever product your company has right now. It’s true that your work has to exist somewhere, but a good team quickly mutates their current offering into something unrecognizable. So bet on the team and the market opportunity, not the demo. It’s just a snapshot. The team is the trajectory.一个容易犯的错误是锚定在公司当前的产品上。虽然你的工作必须依托于某处，但优秀的团队会迅速将其当前的产品演变成无法辨认的新事物。所以，押注于团队和市场机会，而不是 Demo。Demo 只是一个快照，而团队才代表了轨迹。
On superintelligence: It’s possible (I believe) that future models will eventually come to replace much of what we do as knowledge workers. It won’t erase it overnight, it won’t replace all of it, and it won’t be able to do many of the tasks we do. New kinds of jobs will be created. Verification will always be a bottleneck. Someone has to make the call on which problems are worth solving and allocate the correct amount of judgment to each, and that someone can be you.关于超级智能：我相信未来的模型最终可能会取代我们作为知识工作者所做的大部分工作。它不会在一夜之间抹去一切，不会取代所有工作，也无法完成我们所做的许多任务。新的工作类型将会产生。验证永远是一个瓶颈。总得有人决定哪些问题值得解决，并为每个问题分配正确的判断力，而那个人可以是你们。
But importantly, you can do frontier work right now, from where you are. The gate to AI research is smaller than it looks, and you don’t need a lab to build intuition. Just use models hard, and turn what you notice into evaluations. Evals and benchmarks are where understanding lives.重要的是，你现在就可以从你所在的地方进行前沿工作。通往 AI 研究的大门比看起来要小，你不需要实验室来建立直觉。只需高强度使用模型，并将你的观察转化为评估。评估和基准测试才是理解力存在的地方。
To summarize: The world isn’t short on opportunity; it’s short on people who can find the right problem, tell whether the machine solved it, and finish past where the machine stopped.总结一下：世界并不缺乏机会，缺乏的是那些能找到正确问题、能判断机器是否解决了问题，并能在机器停止的地方继续完成工作的人。
We sometimes talk about the “last mile” as the biggest piece of the puzzle. But in the world of agents, the last few feet are infinite (agents scale output infinitely; you don’t). Your attention is your most precious asset, and it doesn’t refill. You can’t afford not to protect it. Anything which is gradable by someone else is getting automated. The career is the ungradable part: choosing what matters, judging honestly when you’ve got it, and answering for it. Do that. In public. Near the hard problems. The rest tends to follow.我们有时谈论“最后一英里”是拼图中最大的一块。但在智能体的世界里，最后几英尺是无限的（智能体可以无限扩展输出，但你不行）。你的注意力是你最宝贵的资产，而且它不会自动补充。你必须保护好它。任何能被他人量化考核的事情都会被自动化。职业生涯中无法量化的部分才是核心：选择重要的事情，诚实地判断你何时做到了，并为此负责。去做这些事。公开地做。在困难的问题附近做。其余的一切自然会随之而来。
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This piece grew out of Phil Chen’s original, which is well worth reading in full.本文源自 Phil Chen 的原创文章，非常值得完整阅读。
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And be sure to join us at AI Codecon: Building with Open Source AI on August 31, a free half-day virtual conference. You’ll hear from leading developers and technical experts working with open-weight models, self-hosted infrastructure, and real-world AI workflows, and learn how building in the open gives teams more control over costs, data privacy, and what they ship. Register today to save your spot.请务必参加我们于 8 月 31 日举办的 AI Codecon: Building with Open Source AI，这是一场免费的半天虚拟会议。你将听到领先的开发者和技术专家分享如何使用开放权重模型、自托管基础设施和实际的 AI 工作流，并了解在开源环境下构建如何让团队更好地控制成本、数据隐私和发布内容。立即注册以预留席位。
