# Earning Judgment
**作者**: Addy Osmani
**日期**: 2026-07-16T03:43:47.000Z
**来源**: [https://x.com/addyosmani/status/2077600055159357548](https://x.com/addyosmani/status/2077600055159357548)
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Taste used to be a byproduct of the reps. Agents took the reps. So if you're junior you now have to go get the taste (and judgment) on purpose.
As I was building my career, I learned to think about code in fundamentally different ways. The first chunk of my learning came from thousands of reps of boilerplate, bug fixes, and solving problems. Almost all of the taste and judgment I have came from those reps, not from talent. Over time, I learned to handle increasingly complex abstractions and tradeoffs. That's how a junior developer matures into a senior one.
But agents will automate all of these reps. This will fundamentally change the path from junior to senior.
Spoiler: The thing that stays durable and ungradeable is not getting better at solving problems that already have a known answer; it's choosing what to build and judging whether it's any good.
The entry-level is weakening. As of March 2026, unemployment among recent college graduates sat at a staggering 5.6%, with underemployment at 41.5% - and recent graduates are now more likely to be unemployed than the workforce as a whole, an inversion of the historical norm (Federal Reserve Bank of New York). Forbes, citing the St. Louis Federal Reserve, notes recent computer-engineering graduates had an unemployment rate of 7.5% and computer-science graduates 6.1%, both elevated above many non-technical majors.
The demand side reshaped underneath them: the Indeed Hiring Lab found junior and standard tech titles down 34% since early 2020 against just 19% for senior and manager roles, while the share of tech postings demanding five or more years of experience climbed from 37% to 42% - even as the number of computer-science graduates kept growing. The most AI-exposed early-career workers (ages 22–25) showed a 13% relative employment decline in the original Stanford Digital Economy Lab study; a February 2026 follow-up revised that to 16% through October 2025 (Brynjolfsson, Chandar & Chen) - and in many of these occupations that age group had accounted for roughly half of all hiring.
Should we panic? Well, entry-level software-developer jobs are more than employment; they're a training system.
Microsoft's Mark Russinovich and Scott Hanselman have warned that, far from taking the bread of all software developers, agents actually help senior developers while robbing juniors of theirs and narrowing the pyramid. Hanselman's telling example: inserting a sleep() to paper over a race condition. The senior developer spots the error and helps you learn from it. The junior developer skips the code review, pushes it to production, and the race later crashes the site - then does it again. Their real point is that the agents aren't doing this on their own; it's less an AI story than a story about how people choose to use the products.
Worse, a 2026 MIT Technology Review piece warned that automating the learning stage buys output now but leaves society gradually less capable later, and the World Economic Forum warned in mid-2026 that cutting entry-level recruitment could weaken the talent pipeline going forward. A word of caution, though: this isn't purely an AI story. We're partly correcting a hiring binge that started in 2022 and ran into 2025. And even the Stanford authors were careful early on - the early signal was muddy, and only after 2024 does the data get clean enough to isolate an AI-attributable decline. Too many vendor "synthetic labor" announcements are marketing prose, not prophecy.
And of course, all of this sits right next to a genuinely optimistic story from the rest of the market. The World Economic Forum's Future of Jobs 2025, published in January 2025, projects that by 2030 some 170 million new roles will emerge against just 92 million displaced - a net gain of 78 million. Apollo Global Management's chief economist Torsten Sløk caused a stir with the claim that there is "zero evidence of job losses because of AI" - arguing that, if anything, the spending is creating work for people with AI skills.
So how can there be contradictory reports in the same year?
Because the net and the entry level are two different questions. The optimists aren't wrong. Senior roles requiring judgment and critical thinking will grow substantially while junior roles will likely shrink. Indeed's numbers say the same. And Goldman Sachs, looking at 2026 payrolls, estimated a net loss of about 16,000 US jobs a month with Gen Z and entry-level roles hit hardest - the empirical answer to "zero evidence."
Aggregate growth and a vanishing first rung are both true at once - and if you're the one trying to climb onto that rung, the net number is cold comfort.
So if junior developers are at risk in this phase, the real question is how we should proceed? 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, all about getting the right answer.
So I propose we look less at the availability of jobs and focus more on developing taste: compressed experience in the form of earned pattern-matching. Kent Beck notes that it's unlikely AI agents will ever possess taste, by which he means judgment, so we'll be left to supply it in these intersections.
We now owe two debts: cognitive surrender and comprehension. When you first learn to code, a wise teacher told you not to copy and paste from Stack Overflow. Unfortunately, I think we've all violated that advice at some point, especially mid-career. The problem gets worse when we offload the copy-paste decision to an AI agent. Shaw and Nave surveyed 1,372 participants over ~10,000 trials and found that they tended to accept incorrect AI output nearly 80% of the time. Their accuracy dropped ~15 points below the no-AI baseline, while confidence rose ~12%.
The agent runs a loop of activities (investigate, implement, test, report).
You, the human, own the outer loop of deciding whether the result is worth your attention; verifying that the result is worthy of approval (diffs, test results, logs, and a short why); approving or blocking; carrying the consequence. The boundary is evidence (my psalm). The gap between the loops is growing rapidly, and you must manage it.
When agents write code, it's easy to think they're doing all the hard work. It's hard to see what a human must do to maintain quality when agents are getting it mostly right. Let me now turn from managing agents to building your taste and judgment, starting with seven concrete things you can do.
- Read far more code than you generate. Hunt for logic errors, security holes, simple or subtle edge cases. On a regular basis, ask yourself about the code you read: Did I consider the right things?
- Keep a wrong log. Every mistake an agent makes gets one sentence. After thirty days you get a sense of patterns.
- Do a few things the hard way on purpose. Build a parser manually, or a CRM, or something meaningful from scratch. Protect your collateral learning. Karpathy emphasizes fundamentals like memory, views (how the world appears to the system), and storage that agents get wrong.
- Go deep on one system end to end. Push it all the way to failure. Then learn what real depth feels like.
- Learn to specify and verify separately. Spec-writing is clear thinking. Verification is evidence. Specification quality is the biggest lever.
- Build an eval, a test framework around a rubric of correctness, maintainability, efficiency, security, style. Run it on fifty real PRs generated by AI agents. Note surprising test failures and fixes. Calibrate to make your internal quality function explicit.
- Calibrate autonomy per task. Turn it way up on cheap, reversible tasks. Down on expensive failures. Learning to calibrate is a senior developer instinct worth exercising daily.
What do people who are building these agents tell juniors?
- "People are still going to need to know the craft underlying software engineering, including languages, compilers, runtimes, and system design" Boris Cherny of Claude Code says. His 'coding is largely solved' statement is about the workflow he personally finds productive, not about forgoing your fundamentals. As an engineer, you really need to know what's going on under the hood.
- Beware the paradox of supervision. A report from Anthropic says they only use AI to help when they already know the answer, and warn that supervising an agent requires exactly the skills that atrophy when you over-rely on one. For a junior developer, growing into that state of mind deliberately, rather than accepting answers blindly, takes effort. Use the tool carefully while deliberately building deeper understanding of the systems you work on.
- Think carefully about which muscles you are willing to let atrophy. Gergely Orosz uses AI for coding but zero AI for writing. Be mindful of trade-offs and decide which ones you are choosing to keep sharp.
Where does durable value concentrate? Start by optimizing for scarce resources.
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 of weeks, but I can't raise a reputation. When vibe-coding makes earning a quick buck trivial, that quick buck is worth very little; the scarce move is choosing something worth shipping. With that lens, four principles.
1. Finish the last mile. Automation covers the easy 80-90% of software engineering. The last mile - edge cases, architecture, taste - is the whole game. As first drafts become free, the finish is the product, and it's where people distinguish themselves.
2. Solve the hard version. Richard Sutton's famed bitter lesson of the last three decades is not just career advice: The easy version is already solved; durable value comes from solving the hard one.
3. Build in public near hard problems. The same constraint that drove junior developers to abandon open source - a well-conceived project takes one person a year, easy to space-out or clone in bits - is less important than the scarcity of an open-source track record of really good work. Think of it like expected goals in soccer: your reputation and public work decide how many chances you get in front of goal, and judgment is whether you convert them. You can't script which chances arrive, only whether you're standing where they land; almost every real opportunity I've had came from work I did in public, never from a job I applied for. So get a foot in the door wherever there is a hard problem you want to solve. That means taking an internship at a small or midsize company, getting on a software engineering track somewhere, or building side projects that matter with production code. Those who try to cut corners will find that it's really hard.
4. Be a T-shaped generalist. Developers with the deepest knowledge in one or two areas while maintaining broad literacy tend to deliver the best results. A good dose of AI assistance means developers will be able to accomplish more as single contributors working on fewer different areas than they could before. Teams that lean into small cross-functional teams and end-to-end engineers will thrive.
To put it plainly: 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.
We talk about the "last mile" as the biggest piece of the puzzle, but in the world of agents the last few feet are effectively infinite - agents scale output infinitely; you don't. Your attention is your most precious asset, and it doesn't refill, so protect it. Anything gradeable by someone else is getting automated.
The career is the ungradeable 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.
Pangram scored this article as 100% human authored
If you enjoyed reading this, you might also appreciate:
- Own the Outer loop
- Cognitive Surrender
- Loop engineering
## 相关链接
- [Addy Osmani](https://x.com/addyosmani)
- [@addyosmani](https://x.com/addyosmani)
- [30K](https://x.com/addyosmani/status/2077600055159357548/analytics)
- [Federal Reserve Bank of New York](https://www.newyorkfed.org/research/college-labor-market)
- [Forbes](https://www.forbes.com/sites/michaeltnietzel/2026/02/23/unemployment-and-underemployment-rates-among-recent-college-graduates/)
- [Indeed Hiring Lab](https://www.hiringlab.org/2025/07/30/experience-requirements-have-tightened-amid-the-tech-hiring-freeze/)
- [February 2026 follow-up](https://digitaleconomy.stanford.edu/news/canaries-interest-rates-and-timinga-more-on-recent-drivers-of-employment-changes-for-young-workers/)
- [Brynjolfsson, Chandar & Chen](https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/)
- [Mark Russinovich and Scott Hanselman](https://www.infoq.com/news/2026/04/junior-developer-pipeline-crisis/)
- [MIT Technology Review](https://www.technologyreview.com/2026/05/26/1137865/its-time-to-address-the-looming-crisis-in-entry-level-work/)
- [World Economic Forum](https://www.weforum.org/publications/artificial-intelligence-and-the-future-of-entry-level-work-a-framework-for-safeguarding-and-reinventing-early-career-pathways/)
- [World Economic Forum's Future of Jobs 2025](https://www.weforum.org/press/2025/01/future-of-jobs-report-2025-78-million-new-job-opportunities-by-2030-but-urgent-upskilling-needed-to-prepare-workforces/)
- [caused a stir](https://fortune.com/2026/06/01/apollo-chief-economist-torsten-slok-zero-evidence-ai-killing-jobs-says-its-creating-them/)
- [Goldman Sachs](https://fortune.com/2026/04/06/ai-tech-displacement-effect-gen-z-16000-jobs-per-month/)
- [Kent Beck](https://newsletter.pragmaticengineer.com/p/tdd-ai-agents-and-coding-with-kent)
- [Shaw and Nave](https://knowledge.wharton.upenn.edu/podcast/ripple-effect/how-ai-is-reshaping-human-intuition-and-reasoning-gideon-nave-and-steven-shaw/)
- [Boris Cherny](https://rogerwong.me/2026/02/what-happens-after-coding-is-solved-boris-cherny)
- [A report from Anthropic](https://www.anthropic.com/research/how-ai-is-transforming-work-at-anthropic)
- [Gergely Orosz](https://newsletter.pragmaticengineer.com/p/the-pragmatic-engineer-ama)
- [bitter lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html)
- [scored](https://www.pangram.com/history/97a8a162-1c10-4d30-b185-3a6ab8b68a0e)
- [Own the Outer loop](https://x.com/addyosmani/status/2074927530482835916)
- [Cognitive Surrender](https://x.com/addyosmani/status/2052124873208799378)
- [Loop engineering](https://x.com/addyosmani/status/2064127981161959567)
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---
*导出时间: 2026/7/16 14:13:47*
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## 中文翻译
# 赢得判断力
**作者**: Addy Osmani
**日期**: 2026-07-16T03:43:47.000Z
**来源**: [https://x.com/addyosmani/status/2077600055159357548](https://x.com/addyosmani/status/2077600055159357548)
---
品味过去是重复练习的副产品。代理人（Agents）接手了这些重复练习。所以，如果你是初级人员，你现在必须刻意去获取品味（以及判断力）。
在我职业生涯的早期，我学会了以根本不同的方式思考代码。我学习的第一部分来自于成千上万次对样板代码、错误修复和解决问题的重复练习。我所拥有的几乎所有的品味和判断力都来自这些练习，而非天赋。随着时间的推移，我学会了处理日益复杂的抽象和权衡。这就是初级开发者成长为高级开发者的过程。
但代理人将自动化所有这些重复练习。这将从根本上改变从初级到高级的成长路径。
剧透：那个经久不衰且无法被替代的，并不是把已知答案的问题解决得更好；而是选择构建什么，并判断它是否足够好。
入门级正在减弱。截至2026年3月，近期大学毕业生的失业率高达5.6%，未充分就业率达41.5%——而且现在的应届毕业生比整个劳动力群体更有可能失业，这扭转了历史常态（纽约联邦储备银行）。《福布斯》引用圣路易斯联邦储备银行的数据指出，近期计算机工程毕业生的失业率为7.5%，计算机科学毕业生为6.1%，均高于许多非技术专业。
需求端在他们的脚下发生了重塑：Indeed Hiring Lab 发现，自2020年初以来，初级和标准技术职位下降了34%，而高级和管理职位仅下降了19%，同时要求五年或以上经验的技术职位比例从37%上升到42%——尽管计算机科学毕业生的数量持续增长。在斯坦福大学数字经济实验室的最初研究中，接触AI最多的早期职业生涯工作者（22-25岁）表现出相对就业下降13%；2026年2月的后续研究将这一数据修正为截至2025年10月的16%（Brynjolfsson, Chandar & Chen）——而且在许多此类职业中，该年龄组曾占所有招聘人数的约一半。
我们应该恐慌吗？嗯，初级软件开发人员的工作不仅仅是就业，它是一个培训系统。
微软的 Mark Russinovich 和 Scott Hanselman 曾警告说，代理人远没有夺走所有软件开发人员的饭碗，它们实际上帮助了高级开发人员，却夺走了初级人员的生计，从而使金字塔变窄。Hanselman 举了一个典型的例子：插入一个 sleep() 来掩盖竞态条件。高级开发人员会发现错误并帮助你从中学习。初级开发人员跳过代码审查，将其推送到生产环境，随后竞态条件导致网站崩溃——然后再次发生。他们的真正观点是，代理人并不是独立做这些事情的；与其说这是一个AI的故事，不如说是一个关于人们如何选择使用产品的故事。
更糟糕的是，2026年《麻省理工科技评论》的一篇文章警告说，自动化学习阶段虽然现在能换取产出，但未来会让社会的能力逐渐减弱；世界经济论坛也在2026年中期警告说，削减入门级招聘可能会削弱未来的人才管道。不过，有一点需要提醒：这不仅仅是一个AI的故事。我们部分是在纠正2022年开始并持续到2025年的招聘狂热。甚至连斯坦福的作者们在早期也很谨慎——早期信号很模糊，只有到了2024年之后，数据才足够清晰，能够分离出可归因于AI的衰退。太多的供应商“合成劳动力”公告只是营销文案，并非预言。
当然，所有这些都与市场其余部分真正乐观的故事并存。世界经济论坛于2025年1月发布的《2025年就业未来》预测，到2030年，将出现约1.7亿个新岗位，而仅有9200万个岗位被取代——净增7800万个。阿波罗全球管理公司的首席经济学家 Torsten Sløk 声称“没有证据表明AI导致失业”，这引起了轰动——他认为，如果说有什么影响的话，这种支出正在为具备AI技能的人创造工作机会。
那么，为什么同一年会有截然不同的报告？
因为总体情况和入门级是两个不同的问题。乐观主义者并没有错。需要判断力和批判性思维的高级职位将大幅增长，而初级职位可能会萎缩。Indeed 的数据也说明了这一点。高盛在查看2026年的薪资表时估计，美国每月净损失约16,000个工作岗位，其中Z世代和入门级职位受打击最严重——这是对“零证据”的经验性回答。
总体增长和消失的第一阶梯同时存在——如果你是那个试图爬上那个阶梯的人，总体数据并不能给你什么安慰。
那么，如果初级开发人员在这个阶段面临风险，真正的问题是我们应该如何继续？如果AI层擅长任何有标准答案的事情，那么学校过去一路都是标准答案，全是关于如何获得正确答案。
因此，我建议我们少关注工作的可获得性，而更多地关注培养品味：以赢得的模式匹配形式出现的压缩经验。Kent Beck 指出，AI代理人不太可能拥有品味，他指的是判断力，所以我们将在这些交叉点上负责提供它。
我们现在欠两笔债：认知投降和理解力。当你第一次学习编程时，一位明智的老师会告诉你不要从Stack Overflow复制粘贴。不幸的是，我想我们都在某种程度上违反了这个建议，尤其是在职业生涯中期。当我们把复制的决定权交给AI代理人时，问题变得更糟。Shaw和Nave调查了1,372名参与者，进行了约10,000次试验，发现他们倾向于在近80%的情况下接受错误的AI输出。他们的准确率比没有AI的基线下降了约15个百分点，而信心却上升了约12%。
代理人运行一个活动循环（调查、实施、测试、报告）。
你，作为人类，拥有决定结果是否值得你关注的外部循环；验证结果是否值得批准（差异、测试结果、日志和一个简短的原因）；批准或阻止；并承担后果。边界就是证据（我的诗篇）。循环之间的差距正在迅速扩大，你必须管理它。
当代理人编写代码时，很容易认为它们正在完成所有艰苦的工作。当代理人基本正确时，很难看出人类必须做什么来维持质量。现在让我从管理代理人转向构建你的品味和判断力，从你可以做的七件具体事情开始。
- 阅读比你生成的代码多得多的代码。寻找逻辑错误、安全漏洞、简单或微妙的边缘情况。定期问自己关于你阅读的代码：我考虑的事情对吗？
- 坚持写错误日志。代理人的每一个错误记录成一句话。三十天后，你就会对模式有所感觉。
- 刻意用困难的方式做一些事情。手动构建一个解析器，或一个CRM，或者从头开始构建一些有意义的东西。保护你的附带学习。Karpathy强调了代理人容易出错的基础知识，如内存、视图（系统如何看待世界）和存储。
- 深入研究一个系统的端到端。将其推向失败。然后学习真正的深度感觉是什么样的。
- 学会将规格说明和验证分开。编写规格说明就是清晰的思考。验证就是证据。规格说明的质量是最大的杠杆。
- 构建一个评估框架，一个围绕正确性、可维护性、效率、安全性和风格标准的测试框架。在AI代理生成的50个真实PR（拉取请求）上运行它。记录令人惊讶的测试失败和修复。进行校准，使你的内部质量功能显性化。
- 根据任务校准自主性。在廉价、可逆的任务上将其调高。在昂贵的失败上将其调低。学会校准是值得每天练习的高级开发者本能。
那些构建这些代理人对初级人员有什么建议？
- Claude Code 的 Boris Cherny 说：“人们仍然需要了解软件工程背后的技艺，包括语言、编译器、运行时和系统设计。”他关于“编码基本解决”的陈述是关于他个人发现高效的工作流程，而不是放弃你的基础。作为一名工程师，你确实需要知道幕后发生了什么。
- 警惕监督悖论。Anthropic的一份报告说，他们只有在已经知道答案的情况下才使用AI来帮忙，并警告说，监督代理人所需要的技能，正是当你过度依赖代理人时会萎缩的技能。对于初级开发人员来说，刻意培养这种心态，而不是盲目接受答案，需要付出努力。在使用工具的同时，刻意加深对你所工作的系统的理解。
- 仔细考虑你愿意让哪些肌肉萎缩。Gergely Orosz 使用AI进行编码，但写作用零AI。注意权衡，并决定你要保持哪些技能敏锐。
持久的价值集中在哪里？从优化稀缺资源开始。
资本是充足的。时间是充足的。真正的联系，尤其是做好工作的记录，仍然是稀缺的：我可以在几周内筹集资金，但我无法筹集声誉。当氛围编码使赚钱变得微不足道时，那点快钱就变得一文不值；稀缺的一步是选择值得发布的东西。以此视角来看，有四个原则。
1. 完成最后一公里。自动化涵盖了软件工程中容易的80-90%。最后一公里——边缘情况、架构、品味——才是整个游戏。当成稿变得免费时，完成就是产品，这也是人们区分自己的地方。
2. 解决困难版本。Richard Sutton 过去三十年著名的“苦涩教训”不仅仅是职业建议：容易的版本已经解决；持久的价值来自于解决困难的那一个。
3. 在困难问题附近公开构建。驱使初级开发者放弃开源的同样的限制——一个构思良好的项目需要一个人一年时间，很容易分散或被克隆——不如真正好的工作的开源记录稀缺重要。把它想象成足球中的期望进球：你的声誉和公开作品决定了你在球门前获得多少机会，判断力是你是否能将它们转化为进球。你无法剧本化哪些机会会到来，只能决定你是否站在它们落下的地方；我拥有的几乎每一个真正机会都来自我在公开场合做的工作，从未来自我申请的工作。所以，只要有一个你想解决的困难问题，就争取先迈出一步。这意味着在小型或中型公司实习，在某处进入软件工程轨道，或者用生产代码构建有意义的副业项目。那些试图走捷径的人会发现这真的很难。
4. 成为T型通才。在一两个领域拥有最深知识同时保持广泛识字能力的开发者往往能提供最好的结果。大量的AI辅助意味着开发者作为单一贡献者，在比以前更少的领域工作时，能够完成更多工作。那些倾向于小型跨职能团队和端到端工程师的团队将茁壮成长。
坦率地说：世界并不缺乏机会；缺乏的是能够找到正确问题、判断机器是否解决了它，并在机器停止的地方继续完成的人。
我们谈论“最后一公里”是谜题中最大的一块，但在代理人的世界里，最后几英尺实际上是无限的——代理人无限扩展输出；而你不会。你的注意力是你最宝贵的资产，它不会自动补充，所以保护它。任何可以被别人评分的事情都在被自动化。
职业生涯中不可评分的部分是：选择重要的事情，诚实地判断你何时得到了它，并对此负责。去做吧。在公开场合。在困难问题附近。其余的往往会随之而来。
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*导出时间: 2026/7/16 14:13:47*