工作, 后工作：就业市场崩溃观察笔记
Ahmed Mohamed
下面是中英对照翻译
I have been putting off writing this for a month, mostly because I did everything I was supposed to do and it still feels like I have no idea what game I am actually playing.
我拖了一个月才写这篇，主要是因为我做了所有应该做的事，但仍然觉得完全不知道自己到底在玩什么游戏。
I went to university. I got good grades. I did three internships. I ran a tiny consultancy for a while, building things for people and sending invoices that were actually paid. I studied computer science. I have done the very normal, very legible technical work you put on a CV: I learned the tools I was told to learn. I watched the right talks. I followed the right people. I can point at a neat little row of experiences and say: I played by the rules you told me about.
我上过大学。成绩不错。做过三次实习。曾短暂经营一家小型咨询公司，为人做东西并开出实际被支付的发票。我学的是计算机科学。我在简历上写的都是非常普通、非常明晰的技术工作：我学了那些被告知要学的工具。看过该看的演讲。关注了该关注的人。我可以指着一排整齐的小经历说：我按你们告诉我的规则玩了。
I am unemployed anyway.
反正我本来也失业。
I have been applying around as a new grad, sending out the same careful batch of CVs and cover letters that older friends used a few years ago to land solid jobs. What I meet instead is a job market that people now casually call broken. The phrase “white collar recession” has escaped into the mainstream. Articles talk about a graduate jobpocalypse and the disappearance of entry level roles just as the largest cohorts of students emerge from university. Computer science, which was marketed for years as the safest degree in the room, now shows up in headlines as one of the majors with the highest unemployment. People in my cohort are not confused about this the group chat tone is not a bit of a rough patch but this market is cooked.
作为一名应届毕业生，我一直在四处投递简历，寄出与几年前那些拿到稳定工作的年长朋友们所用相同的精心准备的简历和求职信。结果遇到的却是人们现在随口称之为“崩坏”的就业市场。“白领衰退”这一说法已经进入主流。文章谈论毕业生就业浩劫，以及在最大规模的学生群体从大学毕业之际，入门级岗位的消失。多年来被宣传为“最安全学位”的计算机科学，如今出现在头条上，成为失业率最高的专业之一。我们这一届的人并不困惑，群聊里的语气也不是把这当成一段小波折——这个市场真的已经完了。
On the official dashboards unemployment is still low, which is what older people tend to quote back at you. From the ground the thing feels different. The postings are there, the interview loops still exist, recruiters still send polite rejections. It is the density of opportunity that has changed. There are more people stacked against fewer real openings, and the default advice of “just apply to more places” lands differently when you know you are running through the same funnel as thousands of other people who also did everything right.
在官方数据显示失业率依然偏低——这也是年长的人常常引用来劝慰你的数据。但从基层感受却不一样。职位发布仍在，面试流程依旧存在，招聘者仍会礼貌地发来拒信。改变的是机会的密度。更多的人挤在更少的真实空缺前，当你知道自己正和成千上万同样做对了所有事的人进入同一个漏斗时，“多投几家”的默认建议听起来就不一样了。
When you talk to older people, including many who are sympathetic, they often reach for familiar explanations first. Rates went up. Funding dried out. The last decade of cheap money was a sugar rush. The current US administration is running a particular mix of trade and industrial policy. These are all real. High interest rates do kill hiring sprees. Entire departments exist today mostly because capital was cheap three years ago. But behind that cycle there is something else, less visible in the graphs and much more visible if you are twenty something and trying to get your first proper job at exactly the moment capital has discovered that software and robots and offshore labour can be stacked together.
当你和年纪较大的人交谈时，包括许多表示同情的人，他们常常首先抓住一些熟悉的解释。利率上升了。资金枯竭了。过去十年廉价资金是场糖分冲击。现任美国政府在推行一种特定的贸易和产业政策组合。这些都是真的。高利率确实会扼杀大规模招聘。如今有些部门的存在主要还是因为三年前资本便宜。但是在那轮周期的背后还有别的东西，在图表上不那么明显，但如果你正好二十出头、试图在资本刚刚发现可以把软件、机器人和离岸劳动力叠加在一起的时刻去找第一份正式工作，那就会更加明显。
A decade ago people passed around a famous paper that said roughly half of American jobs were at high risk of computerisation over the following couple of decades. It was quoted so often that it turned into a kind of folk memory; a big, scary claim that sat behind every newspaper headline about robots taking work. The follow up work was much less memorable. When the OECD re-did the exercise at the level of tasks rather than whole occupations, the share of jobs flagged as “high risk” shrank to a much smaller slice, and later work showed that employment in those supposedly fragile occupations still grew, just more slowly than elsewhere. Automation as actually measured looked more like a slow pressure on certain kinds of work and wages than a cliff edge.
十年前，人们广为转发一篇著名论文，大意是约半数美国工作在未来几十年内面临被计算机化的高度风险。它被反复引用，以至于变成了一种民间记忆；一个庞大而可怕的断言，潜伏在每一条关于机器人取代工作的新闻标题背后。后续研究却不那么引人注目。经合组织在任务层面而非整个职业层面重复这项工作时，被标记为“高风险”的工作比例缩小为一小片，而后来的研究显示，那些所谓脆弱职业的就业人数仍在增长，只是增速比其他地方慢。实际测量下，自动化更像是对某些类型工作和工资的缓慢压力，而不是悬崖式的骤变。
That slow pressure matters. Industrial robots in the United States have already been associated with sizeable job losses and clear wage falls in the regions where they were deployed. The OECD data shows that occupations with more routine, codifiable tasks have grown less and paid worse than occupations that rely more on social skill or physical presence. This is a story about composition, about how many of the jobs created are in fields people can actually enter and live on. The headline unemployment rate can stay low while a particular cohort feels like they are pushing against a wall that gets a little harder every year.
这种缓慢的压力很重要。美国的工业机器人已经在其部署地区与大量失业和明显的工资下降相关联。经合组织的数据表明，更多从事常规、可编码任务的职业增长较少且报酬较差，而更多依赖社交技能或身体在场的职业则表现较好。这是一个关于构成的故事，关于新创造的许多工作是否属于人们实际上能进入并以此谋生的领域。总体失业率可以保持低位，而特定群体却会感觉自己像在推一堵每年都变得更难推的墙。
If you sit where I sit, as a new grad with a half decent CV and a browser full of ghosted applications, that gradual statistical story does not quite capture the feeling. What it feels like is that the entry corridor has narrowed, and that the bar for being worth a salary at all is moving faster than people want to admit. It feels like you are competing not just with other humans, which would be fine, but with the entire past of the economy: every dataset, every process flow someone has written down, every recording of someone doing the work you are trying to get paid for.
如果你像我一样坐在这个位置，作为一个简历还行、浏览器里满是被放鸽子的申请的新毕业生，那种逐渐累积的统计叙事并不能完全捕捉到那种感觉。真实的感受是入门通道已经变窄，而要被认为值得拿薪水的标准正以比人们愿意承认的更快的速度在提升。感觉上你竞争的不仅仅是其他人（那还好办），而是整个过去的经济：每一个数据集、每一道流程、每一段记录着别人做着你试图靠此获得报酬的工作的录像。
The Amazon story is a useful place to stare at this without drifting into science fiction. Leaked internal planning documents and external analyst notes have sketched out a future in which Amazon replaces a large share of warehouse tasks with robotics over the next decade and saves eye watering sums in the process. The company disputes some of the job loss projections, points to new roles created, and likes to say that robots help humans rather than replace them. It is probably sincere and even correct in some places. It is also true that Amazon has quietly expanded its robot fleet to a huge number of units while overall headcount in the most automated centres has flattened or fallen.
亚马逊的案例是观察这一点的一个有益窗口，而不会偏入科幻。泄露的内部规划文件和外部分析师的记录勾勒出这样一个未来：在未来十年里，亚马逊将用机器人取代大量仓库工作，从而节省令人咋舌的费用。公司对部分失业预测持异议，指出创造了新的岗位，并常说机器人是帮助人类而非取代人类。公司在某些方面可能确有其诚意，甚至部分说法是正确的。同样真实的是，亚马逊已悄然将其机器人队伍扩展到大量单元，而在自动化程度最高的中心，整体员工人数已趋于平稳或下降。
What interests me is not just the robots, but the quiet rewrite of the rule that sits under them. For most of the industrial era, you could assume that any large physical operation, like a warehouse, would need a certain number of human bodies to move boxes and drive forklifts. Human labour was a kind of fixed ingredient. You might squeeze wage rates or offshore some tasks, but you did not start your business model by asking whether you could avoid hiring people at all. Amazon and other firms like it are now encouraged by shareholders and banks to ask that question first. Not how do we run this warehouse with people, but how many people can we get away with and where can we put them so that they add marginal value on top of systems that coast on software and steel.
吸引我注意的不仅是机器人本身，还有它们之下那条被悄然改写的规则。在大部分工业时代，你可以认为任何大型的实体作业场所，比如仓库，都需要一定数量的人力来搬运箱子和驾驶叉车。人力劳动是一种固定的要素。你或许会压低工资或将某些任务外包到海外，但你不会在商业模式一开始就问自己是否可以完全避免雇人。Amazon 和类似的公司现在在股东和银行的鼓励下首先要问的就是这个问题。不是我们如何用人来运营这个仓库，而是我们能用最少的人在什么位置安置他们，以便他们在依靠软件和钢铁运行的系统之上增加边际价值。
Teleoperation makes this even stranger. A surprising amount of so called automation today is really labour that has been routed through a screen. There are Filipino workers sitting in offices in Manila wearing VR headsets, remotely steering shelf stocking robots through Japanese convenience stores. There are people in one country sitting at desks, driving forklifts in another country using multi screen setups and a steering wheel, stepping in only when the semi autonomous software gets confused. Security robots patrol office corridors with a remote human ready to take over through a tablet whenever anything looks off.
远程操作让这一切变得更加奇怪。今天所谓的大量自动化实际上是通过屏幕重定向的劳动力。有菲律宾工人坐在马尼拉的办公室里戴着 VR 头盔，远程操控货架补货机器人在日本便利店里活动。也有人在一个国家坐在办公桌前，使用多屏幕设置和方向盘驾驶另一个国家的叉车，仅在半自动软件出现混乱时介入。安保机器人在办公走廊巡逻，随时有一名远程人员准备通过平板接手，只要出现任何异常。
It feels like immigration without immigrants. The rich country gets the labour it wants at a wage that looks more like Manila than Tokyo, but nobody has to build new housing, merge school systems, negotiate over culture or passports. On the rich side, it can be sold as productivity, which plays well politically. On the worker side, it is another rung in the long ladder that runs from call centres to business process outsourcing to micro task platforms. The worker is still human, still fallible, still earning just enough to keep going, but geographically they are treated more like part of the network than part of the town.
感觉像是没有移民的移民。富裕国家以更接近马尼拉而非东京的工资获得所需劳动力，但无需新建住房、整合教育体系、就文化或护照进行谈判。在富裕方，这可以被包装为生产力提升，政治上也好说。在工人一侧，这只是从呼叫中心到业务流程外包再到微任务平台的漫长阶梯上的又一阶。工人仍是人，仍会犯错，仍仅仅挣得够维持生计，但在地理意义上，他们更被视为网络的一部分，而不是城镇的一部分。
There is another twist, which is that these teleoperated jobs rarely stand still. A lot of them exist not just to get the work done, but to collect data so that the work can later be done without the human at all. Humanoid projects make this explicit. Neo, a household robot that went viral this year, spends a lot of its actual working time in an “expert mode” where a remote operator pilots it through chores, opening doors and picking up objects. The company then uses those sessions to train its own control model, using both successes and failures as data. Tesla’s Optimus is being taught in a similar way; workers wear rigs and repeatedly grasp cups or wipe tables so that the resulting recordings can be used as samples for the robot to imitate.
还有一个变化，即这些远程操控的工作很少是一成不变的。许多此类工作不仅是为了完成任务本身，也是为了收集数据，以便将来完全不需要人类也能完成这些工作。类人项目将这一点明示出来。今年走红的家用机器人 Neo 在大量实际工作时间里处于“专家模式”，由远程操作员通过它来完成家务，开门和拾取物体。公司随后利用那些会话来训练自己的控制模型，既把成功也把失败当作数据。特斯拉的 Optimus 也以类似方式被教导；工人们穿戴设备反复抓杯子或擦桌子，以便将所得录制作为机器人模仿的样本。
This is familiar if you watched what happened with data work for self driving cars and large language models. Scale AI started around 2016 with hordes of people labelling images and LiDAR frames for autonomous vehicle companies. Within a few years those labels fed into perception systems and then into foundation models for driving, and the work that was once scattered across many companies as internal grunt work was mostly concentrated with a handful of providers. Teleoperation feels like the embodied version of that. The immediate job is to keep the warehouse or the road running. The secondary job is to produce training data so that the next generation of robots and models will need fewer of you. Ghost work in the physical world.
如果你关注过自动驾驶汽车和大型语言模型的数据工作发生了什么，这就很熟悉了。Scale AI 大约在 2016 年起步，成群的人为自动驾驶公司标注图像和激光雷达帧。几年之内，这些标注进入了感知系统，随后进入了用于驾驶的基础模型，而曾经分散在许多公司内部的繁重工作大多集中到了少数几个供应商手中。远程操控感觉像是那一过程在实体世界的体现。眼前的工作是维持仓库或道路的运转；次要的工作是产生训练数据，以便下一代机器人和模型需要更少像你这样的工人。实体世界中的幽灵劳动。
I am not the person in the VR rig or in the forklift chair. My world is the white collar side of this, the part where the work happens in code editors, notebooks, documents and meetings. The pattern rhymes. Over the past couple of years you can watch entry level roles thin out in tech, finance, consulting and similar fields that used to soak up computer science graduates. Reports talking about a white collar recession and about entry level jobs disappearing just as the largest cohorts of graduates hit the market. Computer science, once the safest bet in the room, now shows up in stories as one of the degrees with the worst employment outcomes, which would have sounded like a joke not long ago.
我不是戴着虚拟现实头盔或坐在叉车椅子上的那个人。我的世界是白领那一边，工作发生在代码编辑器、笔记本、文档和会议里的那一部分。模式在押韵。过去几年里，你可以看到曾经吸纳计算机科学毕业生的科技、金融、咨询及类似领域的入门岗位在减少。各种报告谈论白领衰退，谈论入门级职位在最大规模的毕业生涌入市场时消失。曾经是房间里最稳妥选择的计算机科学，如今出现在报道中成为就业结果最差的专业之一——不久以前这听起来还像个笑话。
You can see the shape of it in small ways. Entry level job boards that used to be full of junior developer roles now skew toward mid level and senior. Graduate schemes quietly cut their intake and companies push harder on automation and AI tooling instead. Employers tell journalists that they are holding back on junior hiring and prefer to lean on experienced staff plus AI tools, or simply automate parts of the work that would once have gone to juniors so they do not need to open the req at all. The ladder is still there, but it has lost a few rungs, and the remaining rungs sit above a seething pile of people who all did what they were told.
你可以从一些细微的地方看到这一变化。过去充斥着初级开发者岗位的入门级招聘板现在偏向于中级和高级。毕业生项目悄然缩减录取人数，公司更倾向于推动自动化和 AI 工具的应用。雇主告诉记者，他们在初级招聘上采取克制，更愿意依靠有经验的员工加上 AI 工具，或者干脆将曾经由初级员工完成的部分工作自动化，从而根本不需要开出职位空缺。阶梯依然存在，但少了几级，剩下的几个台阶位于一堆愤怒的人群之上——这些人都按指示去做了。
There is a tight relationship here with the way humans and software scale. Humans have always had some narrow horizontal scaling. A good operator could, in most cases, cover more than one vehicle if they were in the right geometry. Supervisors can coordinate teams. A single person with good tools can now keep an eye on several semi autonomous robots, or fleets of trucks, or a room full of humanoids, intervening only when something weird happens. Software takes this logic and stretches it until it breaks the category. Once you have a strong model, you can copy it into as many agents as you can afford to run. Read the current run of agent papers and demos and you see systems built from many copies of the same underlying model, set up to argue with each other, negotiate, plan and execute as little societies. In that world the baseline worker is not a person and not even a single bot but a swarm of cloned minds sharing memory.
这里有人类与软件扩展方式之间的紧密关联。人类一直存在某种有限的横向扩展。在合适的环境下，一个优秀的操作员在大多数情况下可以同时监控不止一辆车。主管可以协调团队。现在，一个配备良好工具的个体可以同时监视几个半自主机器人、车队，或一间满是仿人机器人的房间，只在出现异常时干预。软件将这种逻辑拉伸到极致，直到打破类别界限。一旦你拥有一个强大的模型，就可以将它复制到尽可能多的代理中，只要你能负担得起运行成本。阅读当前一系列关于代理的论文和演示，你会看到由许多相同底层模型的副本构建的系统，它们相互争论、协商、规划并执行，形成小型社会。在那样的世界里，基线工人既不是一个人，也不是单个机器人，而是一群共享记忆的克隆心智的蜂群。
Managers are starting to adjust their habits around that. There are already public memos from large companies where leaders tell their staff that any request for headcount has to come with a justification for why an AI system cannot do the job. Shopify’s chief executive talked this way when he told teams to try AI first before asking for more people. Some firms that used to keep small armies of contractors now advertise themselves as “AI first” and quietly shrink the human pool while their products shift more tasks onto models. It is not that nobody gets hired at all. Certain roles are still snapped up. It is that the default answer has flipped. The question is no longer whether a model can cover the job that was going to exist anyway. The question is whether a human can justify their presence next to a stack of models.
管理者开始相应调整他们的习惯。已有大型公司的公开备忘录，领导者告诉下属，任何关于增加编制的请求都必须附带说明，解释为什么不能由某个 AI 系统来完成这项工作。Shopify 的首席执行官也以类似说法要求团队在提出增员前先尝试使用 AI。有些曾经雇佣大批合同工的公司如今将自己宣传为“AI 优先”，在人手悄然减少的同时，把更多任务转移给模型来处理。并不是说完全没有人被雇用，某些职位仍然很快被填满。只是默认答案已经翻转。问题不再是某个模型能否胜任本来就会存在的工作，而是人在一堆模型旁边能否证明自己的存在合理性。
This is where I keep coming back to a phrase that has been rattling around my brain for the past month: out of distribution humans.
这是我不断回想起的一句话，过去一个月它一直在我脑海中回荡：分布外的人类。
Most work lives in the fat middle of a bell curve. Tasks repeat with small variations. Most graduate schemes are built around that fact. You take reasonably bright people, give them a handbook and a mentor, and let them climb a well mapped gradient. Shared service centres, call centres, warehouses, junior consulting rotations, entry level software roles, even a lot of legal and accounting work, all sit in that comfortable hunk of the curve where yesterday’s data is a very good guide to tomorrow’s tasks.
大部分工作都处在钟形曲线的肥厚中部。任务重复，只是有细微变化。大多数毕业生培养计划就是围绕这一事实建立的。你招收相当聪明的人，给他们一本手册和一位导师，让他们沿着一条绘制良好的梯度攀爬。共享服务中心、呼叫中心、仓库、初级咨询轮岗、入门级软件岗位，甚至很多法律和会计工作，都位于曲线中那块舒适的区域，在那里昨天的数据可以很好地指导明天的任务。
Models feast on that part of the curve. That is what they are trained on: logs, emails, historical cases, recordings of someone else doing the job, code repositories, scanned documents. If your work looks a lot like a large pile of past episodes, it is a short hop from playing them back to imitating them. The central question for future labour markets is not whether you are clever or diligent in some absolute sense. It is whether what you do is ordinary enough for a model to learn or strange enough to fall through the gaps.
模型以曲线的那一部分为食。这正是它们被训练的材料：日志、电子邮件、历史案例、有人做这份工作的录音、代码库、扫描文件。如果你的工作看起来很像一大堆过去的事例，从回放它们到模仿它们只是一小步。对未来劳动力市场而言，核心问题不是你在某种绝对意义上有多聪明或勤奋，而是你所做的工作是否足够普通让模型学会，还是足够奇特以至于落入缝隙。
An out of distribution human, in my head, is someone whose job sits far enough in the tail of that curve that it does not currently compress into training data. Maybe they work with genuinely novel problems. Maybe they operate at small scales or in messy physical situations where we do not yet have enough sensors. Maybe they have taste that is not easily reduced to click logs. They are not safe; nothing is. They are simply late on the automation curve. The system needs them until it can watch them for long enough and in enough detail that it can flatten what they do into data.
在我脑中，分布外的人类是指那些其工作位于那条曲线尾部足够远、目前无法被压缩进训练数据的人。也许他们处理着真正新颖的问题。也许他们在小规模或混乱的物理环境中操作，而我们还没有足够的传感器。也许他们有一种无法被点击日志轻易简化的品味。他们并非安全；没有任何东西是安全的。他们只是自动化曲线上较晚被替代的群体。系统需要他们，直到它能足够长时间且以足够细致的方式观察他们，从而将他们所做的事情压平为数据。
The obvious problem is that most people, including most conscientious, capable new grads, are not doing anything like that. Most of us are trying to get into the middle of the curve, into the part of the labour market that has historically been considered sensible and respectable. My three internships were not wild experiments. They were exactly the kinds of things you do when you are aiming for a normal job: some engineering, some product, some research. The consultancy I ran was tiny and real, but it lived on predictable work. That was the point I was building a CV that sat neatly in the centre of the distribution, because that was where the jobs were.
明显的问题是，大多数人——包括大多数认真的、有能力的应届毕业生——并没有做那种事。我们中的大多数人都在努力进入曲线的中间位置，进入那部分历来被认为明智且体面的劳动力市场。我的三段实习并不是疯狂的试验。它们正是你在追求一份正常工作的情况下会做的那类事情：一些工程工作，一些产品工作，一些研究。我运营的咨询公司很小且真实，但它依赖可预见的工作。这就是重点：我在构建一份简历，使其恰好位于分布的中心，因为工作就在那儿。
Now it feels like the centre is being hollowed out. Employers are still very happy to talk about skills and effort, but the quiet question under everything is: is your contribution weird enough that we cannot paste it together from a few agents and a cheaper worker in another country. If the answer is no, then even when you do get hired you have to live with the knowledge that your day job is essentially a labelling job. You are adding examples to the pile that will train your future replacement. Out of distribution humans are the ones who manage to stay just far enough ahead of the pile for long enough to have a career.
现在感觉中心正在被掏空。雇主们仍然很乐意谈论技能和努力，但在一切之下潜藏的那个无声问题是：你的贡献是否足够奇特，以至于我们无法用几名代理和另一个国家的更廉价工人拼凑出来。如果答案是否定的，那么即便你真的被雇佣，你也得带着这样的认知生活：你的日常工作本质上就是一个贴标签的工作。你是在往那堆样本里添加更多示例，以训练未来替代你的人。分布之外的人类是那些设法比那堆样本领先得足够久从而得以拥有职业生涯的人。
The political reaction to this has not caught up. The industrial nations of the twentieth century were built around the idea that work was the organising principle of life. Catholic social teaching talked about the dignity of labour. Socialist movements sang about the worker as a hero. Protestant infused capitalism turned productivity into a route to salvation. Even the centrist stripe of postwar politics treated a job as the main vehicle through which adults were meant to find status, income and a place in the world. This hung around through the neoliberal years, even as manufacturing shrank and services expanded. You can hear it every time someone from any mainstream party talks about “hard working families”.
政治反应尚未跟上。二十世纪的工业国家建立在“工作是生活组织原则”这一理念之上。天主教社会教义谈论劳动的尊严。社会主义运动歌颂工人是英雄。受新教影响的资本主义把生产力变成通往救赎的途径。即便在战后中间路线的政治中，也把工作视为成年人获得地位、收入与社会归属的主要途径。尽管在新自由主义时代制造业萎缩、服务业扩张，这一观念依然存在。每当任何主流政党的人谈论“勤劳的家庭”时，你都能听到这种声音。
The result is that a lot of our institutions still act as if giving everyone a job is the primary goal, long after the underlying economic logic has started to drift. You can see it in the way some regions subsidise employment programmes that barely produce anything useful, or insist that people physically come in to do tasks that could be handled in far leaner ways. You can see it in the zombie jobs that exist mostly so that a local unemployment statistic looks less embarrassing. You can see it in small things like states that keep petrol pump attendants around in an age where self service is trivial, just to keep people on the payroll and maintain a social script about service. These are not vast conspiracies but instead residual behaviour of a world that treated labour as sacred.
结果是，我们的许多机构仍然表现得好像为每个人提供工作是首要目标，尽管支撑这一目标的经济逻辑早已开始偏离。你可以从一些地区补贴几乎没产出什么有用东西的就业项目中看到这一点，或坚持要求人们亲自到场去做本可以用更精简方式处理的任务中看到这一点。你可以从那些主要存在为了让本地失业数据看起来不那么尴尬的“僵尸工作”中看到这一点。你也可以从一些小事看到，比如在自助加油变得微不足道的时代，某些州仍保留加油员，只是为了让人们继续领薪并维护一种关于服务的社会模式。这些并非庞大的阴谋，而是一个曾将劳动视为神圣的世界遗留下来的行为残存。
Unions sit in the middle of this. They have in some cases slowed automation in ways that probably preserved wages and bargaining power for longer than markets would have allowed. Metro lines in Europe run with drivers even though driverless lines exist in the same city and have been proven technically viable. Port workers have fought hard to restrict automated cranes and remote controls, sometimes winning explicit clauses that bar certain kinds of automation for the life of a contract. Strikes that shut down whole systems have been used as leverage to negotiate over the future of jobs that could in principle be done with much less human involvement.
工会处于这一问题的核心。有些情况下，它们减缓了自动化进程，这种减缓很可能比市场自身会允许的更长时间地维护了工资和谈判权。尽管同一城市已有无人驾驶线路并在技术上被证明可行，欧洲的地铁线路仍然由司机驾驶。码头工人曾努力限制自动化起重机和远程控制技术，有时在合同中成功争取到明确条款，禁止在合同期内采用某类自动化。曾有罢工导致整个系统停摆，被用作就那些原则上可以用更少人力完成的工作的未来进行谈判的筹码。
There is a strange symmetry here. On one side you have firms quietly routing labour through screens and robots, and repeating that jobs will be fine on aggregate. On the other you have unions and politicians insisting that jobs must be preserved, even when that means attaching people to tasks that are technically obsolete. Neither camp really articulates what it would mean for work itself to shrink as a central organising story. They just fight over where the remaining jobs will be and who will do them.
这里有一种奇怪的对称。一方面，公司悄悄地通过屏幕和机器人重新分配劳动，并不断重复说总体上就业会没问题。另一方面，工会和政治家坚持必须保住工作，即便那意味着把人们强行绑定到技术上已经过时的任务上。双方都没有真正阐明“工作本身作为一个核心组织叙事缩减”会意味着什么。他们只是争论剩下的工作会在哪里以及由谁来做。
If you want to see how far this can go without the sky falling, you look at places that have already pushed automation hard. The International Federation of Robotics statistic tables are slightly dry, but they tell a simple story. South Korea, Singapore, Japan and Germany have been packing industrial robots into their factories at a remarkable rate for years. China started later, then began installing new units at such a pace that it now accounts for more than half of global industrial robot installations and has overtaken Germany in robot density in manufacturing. At the same time, China’s GDP per capita by purchasing power parity is still maybe a third of that of the United States, and its youth unemployment has lurched upwards, with official figures in recent years hovering in the mid to high teens and unofficial estimates higher.
如果你想看看在不引发灾难的情况下自动化能推进到何种程度，就去观察那些已经大力推进自动化的地方。国际机器人联合会的统计表格略显枯燥，但它们讲述了一个简单的故事。多年来，韩国、新加坡、日本和德国一直以惊人的速度在其工厂中部署工业机器人。中国起步较晚，但随后以如此之快的速度安装新设备，现在其工业机器人安装量占全球的一半以上，并且在制造业机器人密度上已经超过德国。与此同时，按购买力平价计算，中国的人均 GDP 仍可能只有美国的大约三分之一，青年失业率也在急剧上升，近年来官方数据徘徊在十几甚至接近二十的中高位，非官方估计则更高。
So you have a giant country that has thrown money and policy at automation, packed factories full of robots, tied itself into global supply chains, and still produced a generation of graduates who complain on social media about “rotting” in low paid service jobs or online hustle work. You have very visible memes about lying flat and giving up on the competition game. You have an official narrative about hi tech growth that is not exactly false, but feels distant from the daily experience of a twenty three year old with a degree and no good offers.
于是你有一个投入大量资金和政策推动自动化、把工厂塞满了机器人的大国，把自己绑进全球供应链，却仍然培养出一代在社交媒体上抱怨在低薪服务业或网络副业中“腐烂”的毕业生。你会看到关于躺平和放弃竞争游戏的非常流行的梗。你也会有一个关于高科技增长的官方叙事，虽不完全虚假，却让一个二十三岁、拿着学位却没有好工作机会的年轻人的日常感受显得遥远。
The gig economy in the United States and Europe is another small window into this. Robotaxis are still barely a rounding error in total miles driven. Waymo carries a trivial share of all rides in the cities where it operates. Yet if you talk to ride hail drivers in San Francisco or Phoenix, you hear anxiety that grows faster than the fleet. Data from driver apps already shows earnings slipping in markets where robotaxis operate even though their absolute presence is small, and banks have issued notes warning that urban ride hail platforms face “AV risk” as robotaxi coverage expands. The job loss story gets there before the job loss itself.
美国和欧洲的零工经济是另一个小窗口。自动驾驶出租车在总行驶里程中仍然几乎可以忽略不计。Waymo 在其运营的城市中所承担的乘车份额微不足道。然而，如果你与旧金山或菲尼克斯的网约车司机交谈，就会听到一种焦虑，其增长速度比车队还快。司机应用的数据已经显示，在自动驾驶出租车运营的市场中，尽管它们的绝对存在很小，收入却在下降，而银行也已发布报告警告，随着自动驾驶出租车覆盖范围扩大，城市网约车平台面临“自动驾驶车辆风险”。失业的故事在实际失业到来之前就已经传播开来。
This is the pattern that worries me more than the big forecast numbers. The technical line is that automation is a slow, uneven pressure; that jobs appear as well as disappear; that productivity can even raise wages in the long run. The lived line for my cohort is that the good jobs at the centre of the curve are thinning out, that junior entry points are being quietly closed, and that we are being told to somehow mutate into outliers while competing with systems that learn from everything we do.
这种模式比那些大型预测数字更让我担忧。技术上的论调是，自动化是一种缓慢、不均匀的压力；工作会有新增也会有消失；从长远看，生产力甚至可以提高工资。对我这代人的切身感受则是，曲线中心的好工作在变少，初级入门岗位正在被悄然关闭，我们被告知要 somehow 变成异常值，同时还要与从我们一切行为中学习的系统竞争。
I do not know how many jobs will exist in twenty years, or whether my own work will sit far enough into the tail of the distribution to matter. I will certainly try to become an out of distribution human by doing a lot of different things, and by refusing to live entirely in the centre of the curve but if your entire life plan rests on being a respectable, central case worker, doing a standard job in a standard company, I think you should at least stare straight at how much effort is going into eroding that category. If your politics rest on the idea that everyone will work full time and find dignity there, you should stare at it too. The twentieth century spent a lot of intellectual and moral effort glorifying labour because economies needed people to show up every day. The twenty-first century is starting to build machines and systems that do not need quite as many of us.
我不知道二十年后会有多少工作存在，也不知道我的工作是否会落在分布尾端到足以重要。我肯定会尽力通过做许多不同的事，拒绝完全生活在曲线的中心，来成为一个分布外的人，但如果你的人生计划完全依赖于做一个体面的、处于中心的个案工作者，在一家标准公司做一份标准工作，我认为你至少应该直视有多少努力正被投入到侵蚀那个类别上。如果你的政治观点建立在人人都会全职工作并从中找到尊严的想法上，你也应该直视这一点。二十世纪投入了大量的思想与道德努力去颂扬劳动，因为经济需要人们每天到岗。二十一世纪开始构建不再那么需要我们许多人的机器和系统。