Is AI Going to Destroy our Lives or Not?
Good morning from North Carolina!
来自北卡罗来纳州的早上好！
This is a long one, and it’s an attempt to compile much of the research around AI and the labor market that’s come out over the past several months.
这是一篇很长的文章，试图汇集过去几个月发表的有关人工智能和劳动力市场的大部分研究。
It explores what jobs are in the age of AI, how firms are thinking about AI, what wealth looks like, the permanent underclass problem, and a few ways to think about your future in the age of AI.
它探讨了人工智能时代的工作是什么、企业如何看待人工智能、财富是什么样子、永久的下层阶级问题，以及在人工智能时代思考未来的几种方法。
This is a reader-supported publication.
这是一本由读者支持的出版物。
To receive new posts and support my work, consider becoming a paid subscriber.
要接收新帖子并支持我的工作，请考虑成为付费订阅者。
Many college commencement speakers are getting booed off stage for delighting in the productivity of AI in front of the audience it threatens to displace, cheering for all the money it will make them as CEOs, largely at the expense of the students who are supposed to be celebrating their achievement of finishing school.
许多大学毕业典礼演讲者在台下遭到嘘声，因为他们在观众面前对人工智能的生产力感到高兴，人工智能有可能取代他们，并为人工智能将让他们作为首席执行官赚到很多钱而欢呼，这在很大程度上是以牺牲那些本应庆祝他们完成学业的学生为代价的。
Eric Schmidt, former CEO of Google , gave a commencement speech at the University of Arizona, and told students that AI was going to touch every profession and the question to answer wasn’t if AI would shape the world but whether they would shape AI (rhetorically similar but substantially different to JFK’s “ask not what your country can do for you, but what you can do for your country”).
谷歌前首席执行官埃里克·施密特（Eric Schmidt）在亚利桑那大学发表毕业演讲，他告诉学生，人工智能将触及每个职业，要回答的问题不是人工智能是否会塑造世界，而是他们是否会塑造人工智能（与肯尼迪的“不要问你的国家能为你做什么，而是你能为你的国家做什么”在言辞上相似但有本质不同）。
He was booed 1 .
他遭到嘘声 1 。
Real estate owner Gloria Caulfield told graduates that AI was the next industrial revolution, and was booed.
房地产老板格洛丽亚·考尔菲尔德 (Gloria Caulfield) 告诉毕业生，人工智能是下一次工业革命，但遭到了嘘声。
Scott Borchetta, a record-label CEO, during his booing, told graduates that “you can hear me now or you can pay me later.”
唱片公司首席执行官斯科特·博尔切塔 (Scott Borchetta) 在嘘声中对毕业生们说：“你们现在可以听我讲话，或者稍后再付钱给我。”
The asymmetry is really the thing that is being booed here.
不对称确实是这里被嘘的事情。
These students have been training their entire lives for a credentialing system that is now actively being dismantled in the name of efficiency and profit.
这些学生一生都在为一个资格认证系统进行培训，而现在这个认证系统正以效率和利润的名义被积极拆除。
They are booing the people on stage who have benefited from the rules of a previous game, and are now lecturing them about embracing the rules of a new one.
他们对台上那些从上一场比赛规则中受益的人发出嘘声，现在又教导他们如何接受新比赛的规则。
These students aren’t anti-technology, in the same way that the luddites weren’t anti-technology.
这些学生并不反技术，就像勒德分子不反技术一样。
They are anti-technology-without-solution and anti-future-without-hope.
他们反对没有解决方案的技术，反对没有希望的未来。
Of course they are booing.
当然，他们是在发出嘘声。
I did an event at LinkedIn headquarters a few weeks ago on “Careers in the Age of AI.” It was an in-person room of about 25 recent college graduates with a livestream with people of all ages.
几周前，我在 LinkedIn 总部举办了一场名为“人工智能时代的职业”的活动。这是一个面对面的房间，里面有大约 25 名应届大学毕业生，正在与各个年龄段的人进行直播。
The whole point was to talk about jobs and AI and the changing world we are all trying to figure out.
重点是谈论工作、人工智能以及我们都在试图弄清楚的不断变化的世界。
Naturally, the question that kept coming up was: what do I do right now?
当然，不断出现的问题是：我现在该怎么办？
What does it mean to train your whole life for something, only to have a person of power call you a “ lower-value human ?”
为了某件事而训练你的一生，却被一个有权势的人称为“低价值的人”，这意味着什么？
How should someone think about (multiple) AI CEOs saying white-collar work will be “fully automated by an AI within the next 12 to 18 months?” but now walking back those statements ?
人们应该如何看待（多位）人工智能首席执行官所说的白领工作将“在未来 12 到 18 个月内由人工智能完全自动化”？但现在收回那些言论？
What does a career mean anymore, and how should people think about the future they worked so hard to arrive at?
职业生涯意味着什么？人们应该如何思考他们辛勤工作才到达的未来？
The question underneath all of that is timing.
所有这一切背后的问题是时机。
When is the life that people have been told to train for supposed to start?
人们被告知要训练的生活应该从什么时候开始？
According to a recent Economist/YouGov poll , over half of Americans aged 18-29 are pessimistic about AI, and over 60% are very worried or somewhat worried about AI replacing jobs.
根据《经济学人》/YouGov 最近的一项民意调查，超过一半的 18-29 岁美国人对人工智能持悲观态度，超过 60% 的人非常担心或有些担心人工智能取代工作岗位。
The feelings are only deteriorating more over time , with anger and anxiety becoming the dominant emotions over excitement and hope.
随着时间的推移，这种感觉只会更加恶化，愤怒和焦虑成为主导情绪，超过了兴奋和希望。
It’s hard to know what to do because we largely don’t know what’s happening yet.
很难知道该怎么做，因为我们基本上还不知道发生了什么。
The data around AI and the labor market is mixed, and the people telling you that they know are mostly selling you something (usually, an AI product, like the CEOs who gallop to Twitter to delight in how efficient their AI company is in using AI so they can fire everyone).
围绕人工智能和劳动力市场的数据是混杂的，那些告诉你他们知道的人大多是在向你推销一些东西（通常是人工智能产品，就像那些跑到推特上为他们的人工智能公司使用人工智能的效率而高兴的首席执行官，这样他们就可以解雇所有人）。
To level set on my direct experience with jobs and job applications - I grew up in Kentucky and went to Western Kentucky University.
根据我在工作和工作申请方面的直接经验，我在肯塔基州长大，并就读于西肯塔基大学。
I didn’t even know you could major in economics until I got to college, because I had never met anyone who worked in that specific field.
直到我上大学之前，我什至不知道你可以主修经济学，因为我从未见过在该特定领域工作的人。
Once I took my first economics class, I was completely hooked.
当我上了第一堂经济学课时，我就完全被迷住了。
I started dreaming about jobs, new cities, financial stability, and a world where I could do what I loved every day.
我开始梦想工作、新城市、金融稳定，以及一个我每天都可以做自己喜欢做的事情的世界。
I started a blog, Scanlon on Stocks (oh yeah), primarily to meet more people (and met Nick Maggiulli and the Ritholtz team, to whom I owe a lot).
我开设了一个博客，Scanlon on Stocks（哦，是的），主要是为了结识更多的人（并结识了 Nick Maggiulli 和 Ritholtz 团队，我对他们感激不尽）。
I sacrificed much of my college time for the sake of work (something that I would do differently now, it’s okay to have a little fun) I had a 4.0 GPA, three majors, club president and founder, D1 athlete–anything you could do on campus, I was doing it, primarily to try and get a job.
为了工作，我牺牲了大学的大部分时间（现在我会做不同的事情，享受一点乐趣是可以的）我的 GPA 为 4.0，三个专业，俱乐部主席和创始人，D1 运动员 - 任何你在校园里可以做的事情，我都在做，主要是为了尝试找到一份工作。
When it came time to graduate in 2019, I applied to over 150 jobs.
2019 年毕业时，我申请了 150 多个工作。
I got callbacks for about 5 or 6 of them.
我收到了其中大约 5 或 6 个的回电。
I had three good options.
我有三个不错的选择。
One of the options mostly existed because a recruiter from the company gave me a chance.
其中一个选择大多存在，因为公司的招聘人员给了我一个机会。
He had noticed the blog and the initiative and gave me the chance to try, which was all I really needed.
他注意到了这个博客和倡议，并给了我尝试的机会，这正是我真正需要的。
And I got the job.
我得到了这份工作。
In 2026, that doesn’t always happen.
到了 2026 年，这种情况并不总是会发生。
Humans have more or less exited the job process.
人类或多或少已经退出了工作流程。
The hiring funnel has been almost entirely intermediated by AI on both sides.
双方的招聘渠道几乎完全由人工智能中介。
As Derek Thompson wrote about last year, it’s a “plexiglass wall” with both applicants and companies throwing technology at the process, neither side really seeing each other.
正如德里克·汤普森（Derek Thompson）去年所写的那样，这是一堵“有机玻璃墙”，申请人和公司都在这个过程中投入技术，但双方都没有真正看到对方。
Much has changed since I applied, including how we think about jobs.
自从我申请以来，发生了很多变化，包括我们对工作的看法。
But the story that most people tell about how the economy works is not even from 2019, it’s from many, many years ago, the glowing boom of the Post-World War II economy.
但大多数人讲述的关于经济如何运行的故事甚至都不是 2019 年的，而是很多很多年前的二战后经济的繁荣时期。
Work hard, get a degree with enormous government support, buy a house with enormous government support, raise a family, get an awesome pension.
努力工作，在政府的大力支持下获得学位，在政府的大力支持下买房，养家糊口，获得丰厚的养老金。
Sometime in the 1970s and 1980s something went wrong–globalization, automation, high interest rates, the death of pensions, housing as a speculative asset–and we entered a long stagnation.
在 20 世纪 70 年代和 80 年代的某个时候，出现了一些问题——全球化、自动化、高利率、养老金的消亡、住房作为投机资产——我们进入了长期停滞。
In recent years, entry-level hiring has been depressed.
近年来，入门级招聘一直不景气。
Unemployment for college graduates aged 22-27 is 5.6%, up sharply from 3.6% before the pandemic.
22-27岁大学毕业生的失业率为5.6%，较疫情前的3.6%大幅上升。
Guy Berger notes in a WSJ interview that today’s college graduates have usually done better in the face of a weakening job market than they are doing now.
盖伊·伯杰在接受《华尔街日报》采访时指出，当今的大学毕业生在就业市场疲软的情况下通常表现得比现在更好。
Part of it is this AI confusion/problem/opportunity —an April 2026 paper finds that when/if AI absorbs the work juniors used to do, firms cut junior hiring, which weakens the pipeline that creates future seniors.
部分原因在于人工智能的困惑/问题/机遇——2026 年 4 月的一篇论文发现，当/如果人工智能吸收了初级员工过去所做的工作，公司就会削减初级员工的招聘，从而削弱了培养未来高级员工的渠道。
The result is “lost cohorts” of juniors who don’t get the training they need.
结果是“失去了一批”初级员工，他们没有得到所需的培训。
Part of it is other problems, including pandemic overhang.
部分原因是其他问题，包括流行病的蔓延。
A study of 243 million hires across four countries finds that when you separate the effect of AI from the effect of remote work, remote work turns up as the likeliest culprit of a recent collapse in entry-level hiring.
一项针对四个国家 2.43 亿员工的研究发现，当你将人工智能的影响与远程工作的影响分开时，远程工作很可能是近期初级招聘大幅下滑的罪魁祸首。
When teams went remote, the cost of supervising and training a junior went up, so firms stopped hiring them.
当团队进行远程办公时，监督和培训初级员工的成本就会上升，因此公司不再雇用他们。
Both papers point to firms kind of giving up their juniors.
这两篇论文都指出，公司在某种程度上放弃了初级员工。
That’s a core rung on the economic ladder that young people are missing out on.
这是年轻人错过的经济阶梯的核心阶梯。
The booing is rational.
嘘声是有道理的。
So the entire AI conversation has been organized around one question: will AI replace your job, yes or no , and as we can see in the results from these papers, it’s really not clear what is happening.
因此，整个人工智能对话都是围绕一个问题组织的：人工智能会取代你的工作吗？是或否，正如我们从这些论文的结果中看到的那样，目前还不清楚正在发生什么。
That’s why a framework for thinking about jobs is so important.
这就是为什么思考工作的框架如此重要。
Alex Imas has written many excellent pieces on AI and labor market displacement .
Alex Imas 撰写了许多关于人工智能和劳动力市场转移的优秀文章。
He begins a piece from earlier this year discussing a widely cited 2023 paper on AI exposure, which found that around 80% of workers in the US could have at least 10% of their tasks affected by large language models.
他在今年早些时候的一篇文章中讨论了一篇被广泛引用的 2023 年关于人工智能暴露的论文，该论文发现，美国大约 80% 的工人至少有 10% 的任务受到大型语言模型的影响。
Many people interpret this as “80% of jobs are at risk,” and Imas points out that the reading is wrong—that the 80% exposure measures whether AI can do some of the tasks in a job, not whether or not the job entirely disappears.
许多人将此解读为“80% 的工作面临风险”，Imas 指出这种解读是错误的——80% 的暴露衡量的是人工智能是否可以完成工作中的某些任务，而不是工作是否完全消失。
Imas says the dimensionality of a job really matters, the number of distinct tasks it involves.
伊马斯说，工作的维度确实很重要，它涉及不同任务的数量。
A job with two tasks, one of which AI can do no problem, is a high-risk job.
一项有两项任务的工作，其中一项人工智能可以毫无问题地完成，是一项高风险工作。
If a firm can automate half the job, mechanically it’s hard to justify keeping the worker.
如果一家公司可以将一半的工作自动化，那么机械地就很难证明留住工人是合理的。
A job with seven tasks, one of which AI can do well, is a job that probably gets more productive (if the firm is smart) because the worker has more time to do the other six tasks that can’t be automated.
一项包含七项任务的工作（其中一项人工智能可以很好地完成）可能会变得更有生产力（如果公司很聪明），因为工人有更多的时间来完成其他六项无法自动化的任务。
Imas says that the resulting productivity gains, the focus effect, can raise wages instead of eliminating the role.
伊马斯说，由此产生的生产力提高，即焦点效应，可以提高工资，而不是消除这个角色。
He compares a long-haul truck driver to a consultant.
他将长途卡车司机比作顾问。
A truck driver could be automated, because it’s theoretically one task, of going from A to B.
卡车司机可以实现自动化，因为理论上从 A 点到 B 点只是一项任务。
However, a management consultant, a job that has a bundle of tasks including being a scapegoat for companies, can have some of their work automated, but the political work and client communication and presentation of results cannot be.
然而，管理顾问这个需要承担大量任务的工作，包括充当公司的替罪羊，可以将部分工作自动化，但政治工作、客户沟通和结果展示却不能。
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Thinking of a job as a bundle of tasks, the dimensionality, is a very useful rule of thumb because it gives you a good sense of how at-risk a chosen job might be.
将工作视为一组任务（维度）是一个非常有用的经验法则，因为它可以让您很好地了解所选工作可能面临的风险有多大。
Luis Garicano builds on Imas’s framework , offering the supply side version, and points out that firms buy bundles, not tasks, so they pay for a human so a human can do a bunch of interconnected, context-dependent tasks, because jobs are complex.
Luis Garicano 以 Imas 的框架为基础，提供了供应方版本，并指出公司购买的是捆绑包，而不是任务，因此他们为人类付费，这样人类就可以完成一堆相互关联的、依赖于上下文的任务，因为工作很复杂。
Ernie Tedeschi at Stripe Economics uses travel agents as an example of “the economic value of human expertise” as Imas calls it.
Stripe Economics 的 Ernie Tedeschi 以旅行社作为伊马斯所说的“人类专业知识的经济价值”的例子。
In 2000, the job was exposed to automation–booking, fare aggregation, ticketing—all of it could be automated and no one was safe.
2000 年，这项工作接触到了自动化——预订、票价汇总、售票——所有这些都可以自动化，没有人是安全的。
Travel agent headcount fell by more than 60% from the dotcom peak.
旅行社人数较互联网泡沫时期减少了 60% 以上。
But some survived.
但有些人幸存下来。
They survived by moving up market.
他们通过向高端市场进军而生存下来。
Travel agents now earn close to 99% of the private-sector average wage, up from 87% in 2000.
旅行社现在的收入接近私营部门平均工资的 99%，而 2000 年为 87%。
Those that survived leaned on judgement and accountability and relationships and the ability to swoop in and fix something quickly when things go wrong at 2am.
那些幸存下来的人依靠判断力、责任感和人际关系，以及在凌晨 2 点出现问题时迅速介入并解决问题的能力。
Those seem to be the pillars of strength in the AI age: being able to evaluate, having some element of taste, holding accountability, building out networks, and the capacity to deliver a human touch in an increasingly automated world.
这些似乎是人工智能时代的力量支柱：能够评估、具有一定的品味、承担责任、建立网络以及在日益自动化的世界中提供人性化的能力。
I was in line at the rental car center a few days ago.
前几天我在租车中心排队。
I tried to check-in online but the app kept logging me out.
我尝试在线办理登机手续，但应用程序始终将我注销。
So I had to wait in a 20-person line that took about one hour to get through to have a human fix whatever was going wrong.
因此，我不得不排在 20 人的队伍中，大约花了一个小时才能接通，以便有人修复出现的问题。
The meta-work of that: finding a car, finding my profile, the payment, can all be automated.
其中的元工作：找车、查找我的个人资料、付款，都可以自动化。
But the ability to step in when the technology doesn’t work is still something only a human can do.
但当技术不起作用时介入的能力仍然是只有人类才能做到的事情。
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We don’t quite understand how human most jobs are.
我们不太了解大多数工作的人性化程度。
It drifts into Rory Sutherland’s Doorman Fallacy .
它陷入了罗里·萨瑟兰的门卫谬误。
The job of a doorman is just opening doors, right?
门卫的工作就是开门，对吧？
Well, no - it’s welcoming and helping and security and a place to ask questions.
嗯，不——它是热情、乐于助人、安全的，也是一个提问的地方。
A job is usually a whole lot more than it seems.
一份工作通常比看上去要复杂得多。
Firms are also craving some humanness.
企业也渴望一些人性化。
Gillian Tett at the Financial Times wrote that firms are focusing on “critical thinking” skills and are looking towards hiring students that majored in the humanities rather than STEM.
英国《金融时报》的吉莉安·泰特 (Gillian Tett) 写道，公司正在关注“批判性思维”技能，并希望聘用人文学科而不是 STEM 专业的学生。
Much of this is due to “shallowness” of the ideas of STEM students.
这很大程度上是由于 STEM 学生的想法“肤浅”。
So the big question now — for financiers, regulators and educators alike — is how to create skilled AI natives who can also use the critical thinking needed to spot both the opportunities and (very real) risks in AI.
因此，对于金融家、监管机构和教育工作者来说，现在的大问题是如何培养熟练的人工智能原生人才，他们还可以利用所需的批判性思维来发现人工智能中的机会和（非常真实的）风险。
Those companies which find them will be the real winners.
那些找到他们的公司将是真正的赢家。
All eyes, then, will be on the 2026 intern pool.
那么，所有的目光都将集中在 2026 年的实习生人才库上。
People optimized too much for one side of intelligence, ignoring the curation of their creative skills.
人们对智力的某一方面进行了过多的优化，而忽视了对创造性技能的培养。
Creativity is enormously valuable in the age of AI.
创造力在人工智能时代非常有价值。
My take on it is that AI is sort of like freeze-dried camp food.
我的看法是，人工智能有点像冻干的营地食品。
It does a good enough job, is filling enough, but it isn’t something you want to eat everyday (trust me, I tried).
它做得足够好，足够饱，但它不是你每天都想吃的东西（相信我，我试过了）。
People crave ineffcient, home-cooked meals for a reason, and it’s because it’s a nice thing, not that it provides a certain exact number of calories and fat and protein.
人们渴望低效的家常饭菜是有原因的，这是因为它是一件好事，而不是因为它提供了一定数量的卡路里、脂肪和蛋白质。
It does require some context (some bakers know that breakfast skillets are better with some avocado slices or some butter).
它确实需要一些背景信息（一些面包师知道早餐煎锅加一些鳄梨片或一些黄油会更好）。
It also leans on accountability (a chef is responsible for what they cook!) and taste (in both flavor and meal design).
它还依赖于责任（厨师对他们烹饪的食物负责！）和口味（在风味和膳食设计方面）。
You need camp food some days.
有时你需要露营食物。
It’s good that camp food exists.
营地食物的存在是件好事。
Camp food is efficient and helpful.
营地食物高效且有益。
But, we still need real food too.
但是，我们仍然需要真正的食物。
The technology is very expensive.
该技术非常昂贵。
Much of our assumptions on AI displacement seems to be based on the idea that it is cheaper than labor.
我们对人工智能取代的大部分假设似乎都是基于这样的想法：它比劳动力更便宜。
It might be soon ( Nvidia wrote in February that Nvidia’s new chip allowed the leading inference providers to reduce token (think of these as arcade coins that the AI models eat in order to work) costs by 10x), but right now, that assumption is just not true.
可能很快就会到来（Nvidia 在 2 月份写道，Nvidia 的新芯片可以让领先的推理提供商将代币（将其视为人工智能模型为了工作而吃的街机硬币）成本降低 10 倍），但现在，这个假设并不正确。
Agentic workflow, which consists of the multi-step work building toward context and revisions and judgement, the human-y AI, is more compute intensive than a chatbot.
代理工作流程由构建上下文、修订和判断的多步骤工作组成，即人性化人工智能，比聊天机器人的计算密集度更高。
Upcoming frontier models could be even more expensive.
即将推出的前沿型号可能会更贵。
Lots of arcade tokens.
很多街机代币。
In April, Uber’s CTO said that he had to go “back to the drawing board because the budget I thought I would need is blown away already.” Azeem Azhar and Hannah Petrovic cover this in a recent piece , writing that over 70% of companies exceeded their AI budgets in 2025.
今年 4 月，Uber 首席技术官表示，他必须“重新制定计划，因为我认为需要的预算已经被取消了。” Azeem Azhar 和 Hannah Petrovic 在最近的一篇文章中对此进行了讨论，并写道，超过 70% 的公司在 2025 年超出了人工智能预算。
This stuff is not cheap!
这东西可不便宜啊！
Some of the enormous spend is good.
巨额支出中的一些是好的。
Brian Albrecht published a long piece a few weeks ago called “You Are Not a Horse” that walks through the AI-displaces-everyone story by tracing the dollars.
几周前，布莱恩·阿尔布雷希特 (Brian Albrecht) 发表了一篇名为《你不是一匹马》的长文，通过追踪美元来讲述人工智能取代所有人的故事。
If AI makes some tasks cheaper, the money saved through efficiency doesn’t vanish, but rather gets spent somewhere else, which creates demand somewhere else, which keeps humans employed somewhere else.
如果人工智能使某些任务变得更便宜，那么通过效率节省下来的钱不会消失，而是花在其他地方，这在其他地方创造了需求，从而使人类在其他地方就业。
For the horse outcome to happen (where horses are no longer our tractors and instead are living a relaxing life in the fields) every dollar of spending would have to land on activities with no human labor inside them, anywhere in the supply chain.
为了实现马的结果（马不再是我们的拖拉机，而是在田野里过着轻松的生活），每一美元的支出都必须落在供应链中任何地方没有人工参与的活动上。
People save money on goods because of the efficiency of AI, so they start spending money on services.
由于人工智能的效率，人们在商品上省钱，因此他们开始在服务上花钱。
The dollar has to go somewhere, and that somewhere can support employment somewhere else in the economy.
美元必须流向某个地方，而那个地方可以支持经济中其他地方的就业。
But some of the spend is bad.
但有些支出是不好的。
Companies have fired people in the name of AI efficiency, but in many cases, it hasn’t really made anything more efficient.
公司以人工智能效率的名义解雇了员工，但在很多情况下，它并没有真正提高任何效率。
Uber’s COO recently came out and said that “ tokenmaxxing ” was making it “harder to justify AI costs within the company” because more tokens spent by engineers wasn’t leading to a measurable increase in “useful consumer features.”
Uber 的首席运营官最近出面表示，“tokenmaxxing”使得“公司内部人工智能成本的合理性变得更加困难”，因为工程师花费的更多代币并没有导致“有用的消费者功能”的显着增加。
Part of this is a measurement problem (we likely don’t totally understand how to implement AI outside of “people-shaped workloads ”) and part of it is AI compute is increasingly constrained and therefore increasingly expensive.
部分原因是测量问题（我们可能不完全了解如何在“人形工作负载”之外实施人工智能），部分原因是人工智能计算越来越受到限制，因此越来越昂贵。
More data centers or whatever might make all of this cheaper and more effective.
更多的数据中心或其他任何东西可能会让这一切变得更便宜、更有效。
But jobs that are expensive to automate at the moment might be safe for a while.
但目前自动化成本高昂的工作可能在一段时间内是安全的。
But for now, when you put these pieces together: the dimensionality + the bundle strength + the compute economics = a framework that is pretty useful.
但现在，当你把这些部分放在一起时：维度 + 捆绑强度 + 计算经济学 = 一个非常有用的框架。
Jobs that are at risk are (1) low dimensional with weak bundle strength with tasks that are already cheap to run on AI, like customer service (although, as someone that has been trying to stop my insurance from double charging me for the past several months, I would very much like a human involved) and back-office document processing and entry-level analyst work.
有风险的工作是（1）低维度、捆绑强度弱，以及在人工智能上运行成本已经很便宜的任务，比如客户服务（尽管，作为过去几个月一直试图阻止我的保险向我收取双重费用的人，我非常希望有人参与）以及后台文档处理和入门级分析师工作。
So if you have a high dimensional job with a lot of tasks that take a lot of compute; a job threaded with judgement and accountability and creativity, that might even benefit from AI spend, you’re probably relatively safe.
因此，如果您有一项高维工作，其中包含许多需要大量计算的任务；一份充满判断力、责任感和创造力的工作，甚至可能从人工智能支出中受益，你可能相对安全。
Imas says that the “relational sector”, which is “human-intensive, provenance-rich, sometimes artisanal part of the economy where the human aspect is part of the value of the good or service” will become increasingly important.
伊马斯表示，“关系部门”将变得越来越重要，“关系部门”是“经济中人力密集、资源丰富、有时是手工的部分，其中人的因素是商品或服务价值的一部分”。
But that requires firms to think about jobs in a way that is more than a cost metric, which they don’t tend to do.
但这要求企业以不仅仅是成本指标的方式来考虑工作，而他们往往不这样做。
Axios recently reported that companies are firing employees to cover their AI bills.
Axios 最近报道称，公司正在解雇员工以支付人工智能费用。
People are getting laid off who might have a high dimensional job because the firm could be unaware of how challenging it will be to AI that specific job.
那些可能从事高维度工作的人正在被解雇，因为公司可能没有意识到人工智能这项特定工作将面临多大的挑战。
Starbucks is retiring AI inventory tooling because it kept hallucinating.
星巴克正在淘汰人工智能库存工具，因为它一直在产生幻觉。
We are likely in a rough transition period of fires and rehires as we adjust to that.
当我们适应这一点时，我们可能正处于解雇和重新雇用的艰难过渡期。
Some firms are using AI .
一些公司正在使用人工智能。
Rob Copeland reported in April that six of the largest US banks posted $47 billion in profits in Q1 2026 while shedding 15,000 jobs, and the CEOs are now openly like “AI let us do that :)” on earnings calls.
Rob Copeland 在 4 月份报道称，美国六家最大的银行在 2026 年第一季度实现利润 470 亿美元，同时裁员 15,000 人，首席执行官们现在在财报电话会议上公开表示“人工智能让我们这样做:)”。
In 2025, Brian Moynihan at Bank of America said that AI was “not a threat” to his 210,000 employees.
2025 年，美国银行的布莱恩·莫伊尼汉 (Brian Moynihan) 表示，人工智能对其 21 万名员工“不是威胁”。
In 2026, he announced that the bank had shed 1,000 jobs through “eliminating work and applying technology”.
2026年，他宣布银行通过“消工加技”方式裁员1000人。
JP Morgan’s Kevon Brunner says that AI has shifted from “hype to real execution.”
摩根大通的凯文·布伦纳表示，人工智能已经从“炒作转向真正的执行”。
Some firms are using humans to train the AI to eventually fire themselves.
一些公司正在利用人类来训练人工智能，最终让其自行解雇。
Meta laid off 8,000 employees in an AI-push, assigning 7,000 workers to “AI-initiative focused teams”.
Meta 在人工智能推动中解雇了 8,000 名员工，将 7,000 名员工分配到“以人工智能为重点的团队”。
The SF Standard did an interview with an anonymous Meta employee (who ended up being one of those laid off) about the morale at the company.
《SF Standard》采访了一位匿名 Meta 员工（他最终成为被解雇的员工之一），了解公司的士气状况。
Much of the interview was focused on the sense of detachment from humanity, with the employee stating that Meta is “not at all empathetic enough or human enough in how they are leading humans through [the AI] era.”
采访的大部分内容都集中在与人性的脱离感上，这位员工表示，Meta“在引领人类度过[人工智能]时代方面根本没有足够的同理心，也不够人性化。”
Some firms are designing their own AI.
一些公司正在设计自己的人工智能。
Kirkland & Ellis, the world’s highest-grossing law firm, announced in May that it’s spending $500 million to build its own AI platform designed using information from 250 of its lawyers about how they do their jobs, instead of using a product like Harvey.
全球收入最高的律师事务所 Kirkland & Ellis 在 5 月份宣布，将斥资 5 亿美元建立自己的人工智能平台，该平台的设计使用了 250 名律师的工作信息，而不是使用 Harvey 这样的产品。
The chair, Jon Ballis, told the FT the idea is to “take the collective intelligence of our institution and be able to deploy that throughout our firm.” The senior generation pouring its knowledge into a system that will eventually do the work that juniors used to do to learn the job.
主席乔恩·巴利斯 (Jon Ballis) 告诉英国《金融时报》，这个想法是“利用我们机构的集体智慧，并能够在整个公司中部署它。”老一代人将其知识倾注到一个系统中，最终将完成后辈们过去为学习工作所做的工作。
Some firms are having mixed results.
一些公司的结果好坏参半。
Gillian Tett at the Financial Times wrote about a Judge Business School survey of over 600 finance and AI companies that found that about 80% of private sector finance groups are using AI, but only 40% reported any profit boost from it.
英国《金融时报》的吉莉安·泰特 (Gillian Tett) 撰文介绍了贾吉商学院对 600 多家金融和人工智能公司进行的一项调查，结果发现，约 80% 的私营金融集团正在使用人工智能，但只有 40% 的企业报告称人工智能带来了利润增长。
60% of respondents actually expect AI to increase hiring or reskilling at their firm.
60% 的受访者实际上期望人工智能能够增加公司的招聘或再培训。
Many firms are not using it at all.
许多公司根本不使用它。
Only 1 in 5 firms across the board are actually using AI, as Guy Berger notes in his piece on AI adoption .
正如 Guy Berger 在他关于人工智能采用的文章中指出的那样，只有五分之一的公司实际上在使用人工智能。
That’s with a pretty strong growth rate—Federal Reserve’s Jeffrey Allen does a great breakdown of the three public surveys we use to gauge AI adoption, noting that the adoption rate grew 70% from late 2024 to late 2025—but it’s still pretty low, considering how much money the companies are spending and for all the fuss it makes.
这是一个相当强劲的增长率——美联储的杰弗里·艾伦对我们用来衡量人工智能采用率的三项公共调查进行了详细分析，指出从 2024 年底到 2025 年底，采用率增长了 70%——但考虑到公司花费了多少钱以及它引起的所有大惊小怪，这个增长率仍然相当低。
Ernie Tedeschi ends his piece on travel agent employment by highlighting a framework around job displacement:
厄尼·泰德斯基 (Ernie Tedeschi) 在他关于旅行社就业的文章的结尾强调了一个围绕工作替代的框架：
On the one hand, AI disruptions to the labor market may be delayed and not salient until (perhaps even amplified by) the next recession.
一方面，人工智能对劳动力市场的干扰可能会被推迟，并且直到下一次经济衰退（甚至可能会被放大）之前才变得显着。
On the other hand, the most negative effects may be limited to specific occupations rather than broad-based, and as prices and wages adjust, those occupations may even look more favorable over time
另一方面，最负面的影响可能仅限于特定职业，而不是广泛的职业，而且随着价格和工资的调整，随着时间的推移，这些职业甚至可能看起来更有利
He points out that (1) we might not see any real AI impact until we see a recession and (2) specific jobs might be impacted rather than the labor market and (3) those specific jobs might be fine in the long run.
他指出，（1）在经济衰退之前，我们可能不会看到任何真正的人工智能影响；（2）特定工作可能会受到影响，而不是劳动力市场；（3）从长远来看，这些特定工作可能会很好。
David Solomon, the CEO of Goldman Sachs, wrote a New York Times op-ed last week titled “The A.I.
高盛首席执行官大卫·所罗门 (David Solomon) 上周撰写了《纽约时报》专栏文章，题为“人工智能”。
Job Apocalypse Is Overblown.” His argument is the standard optimistic one: the economy has absorbed technological transitions before (electrification, the digital revolution), job growth has outpaced population growth since the 1960s, and even if AI automates 25% of work hours, people will find more productive things to do.
就业启示录被夸大了。”他的论点是标准的乐观论点：经济之前已经吸收了技术转型（电气化、数字革命），自 20 世纪 60 年代以来，就业增长超过了人口增长，即使人工智能使 25% 的工作时间实现自动化，人们也会找到更有生产力的事情去做。
All will be well, in the long run.
从长远来看，一切都会好起来的。
The aggregate case is fine, sure, but that’s not what's actually breaking people.
当然，总体情况很好，但这并不是真正让人们崩溃的原因。
In April, Goldman Sachs economists published a paper telling a different story.
四月份，高盛经济学家发表了一篇论文，讲述了一个不同的故事。
They examined four decades of individual-level data on workers displaced by technology, and the findings line up with Tedeschi’s concern.
他们研究了四十年来有关因技术而失业的工人的个人数据，结果与特德斯基的担忧相符。
Workers displaced from technology-disrupted occupations take about a month longer to find a new job and suffer real earnings losses of more than 3% on reemployment.
因技术中断而失业的工人需要大约一个月的时间才能找到新工作，并且在再就业时实际收入损失超过 3%。
Ten years out, their earnings are still nearly 10 percentage points behind workers who were never displaced, and 5 points behind workers who were displaced from more stable occupations, known as “scarring” effects.
十年后，他们的收入仍比从未失业的工人低近 10 个百分点，比从更稳定的职业中流离失所的工人低 5 个百分点，即所谓的“疤痕”效应。
Tech-displaced workers experience delayed homeownership and delayed household formation, with the effects amplifying significantly in recessions.
因技术而失业的工人会经历住房所有权延迟和家庭组建延迟，这种影响在经济衰退时会显着放大。
To be clear, this type of displacement has always worked like this.
需要明确的是，这种类型的位移一直都是这样运作的。
The costs land on individuals for a very long time.
这些成本将在很长一段时间内落在个人身上。
The economy will adjust in the long-run, but the adjustment period can still suck.
从长远来看，经济会调整，但调整期仍然很糟糕。
Jasmine Sun summarizes it well with a quote from Carl Benedikt Frey:
Jasmine Sun 引用 Carl Benedikt Frey 的一句话很好地总结了这一点：
Most economists will acknowledge that technological progress can cause some adjustment problems in the short run.
大多数经济学家都会承认，技术进步可能会在短期内引起一些调整问题。
What is rarely noted is that the short run can be a lifetime.
很少有人注意到的是，短期可能是一生。
The economy can absorb AI the way it absorbed previous technologies, slowly, unevenly and with new sectors appearing at the same time that the rungs of the economic ladder are getting squeezed.
经济可以像吸收以前的技术一样缓慢地、不均匀地吸收人工智能，并且在经济阶梯的梯级受到挤压的同时，新的行业也会出现。
But jobs are only one part of the AI conversation.
但工作只是人工智能对话的一部分。
Wealth creation is another important part.
创造财富是另一个重要部分。
Wealth is mainly a combination of labor income and investment income and real estate.
财富主要是劳动收入和投资收入以及房地产的组合。
True wealth comes owning shares of companies, and the US stock market is meant to be that wealth-creation opportunity.
真正的财富来自于拥有公司股票，而美国股市就是创造财富的机会。
Companies go public because that’s the promise we made people—don’t worry, tying your retirement to the stock market is going to go really great because all of the biggest companies go public and you, the general public, can participate in the upside.
公司上市是因为这是我们向人们做出的承诺——别担心，将你的退休生活与股票市场挂钩将会非常好，因为所有最大的公司都会上市，而你，作为普通大众，可以分享到上涨的机会。
And retire.
然后退休。
But companies don’t go public anymore.
但公司不再上市了。
Deedy Das wrote a post about Silicon Valley where he estimates that about 10,000 people, those working at Anthropic and OpenAI and Nvidia and Meta have all hit retirement-level wealth, more than $20 million dollars within the past five years.
Deedy Das 写了一篇关于硅谷的文章，他估计在 Anthropic、OpenAI、Nvidia 和 Meta 工作的大约 10,000 人在过去五年内都达到了退休水平，财富超过 2000 万美元。
Everyone outside of that circle is frothing at the mouth to try and get in, but the door has closed—the companies are pushing back against people selling secondaries , meaning that the most enormous wealth engine in history is increasingly confined to just a few people.
这个圈子之外的每个人都在口吐白沫，试图进入，但大门已经关闭——这些公司正在抵制人们出售二级资产，这意味着历史上最巨大的财富引擎越来越局限于少数人。
OpenAI and Anthropic 4 are both sniffing around IPOs, but both are near $1T private market valuations, which could leave little room for more upside.
OpenAI 和 Anthropic 4 都在考虑 IPO，但私募市场估值都接近 1T 美元，这可能几乎没有进一步上涨的空间。
As Joachim Klement wrote “the IPO of these AI companies is probably nothing more than a major transfer of investment risk from the current owners to retail investors, pension funds and others who are willing to buy the hype.”
正如约阿希姆·克莱门特（Joachim Klement）所写，“这些人工智能公司的首次公开募股可能无非是将投资风险从当前所有者转移到散户投资者、养老基金和其他愿意购买炒作的人身上。”
SpaceX just filed to go public after 24 years—they are unprofitable on $18.7 billion of revenue, losing almost $5 billion last year, and are about to IPO at a $1.5 trillion valuation, with Musk holding 85% of the voting power.
SpaceX 在 24 年后刚刚申请上市——他们的收入为 187 亿美元，但尚未盈利，去年亏损近 50 亿美元，即将以 1.5 万亿美元的估值进行 IPO，马斯克持有 85% 的投票权。
This is a company that we are asking people to build their retirement on!
这是一家我们要求人们建立退休生活的公司！
If you don’t own equity here (or more importantly, work at one of those companies and have access to shares regular people can only dream about) your participation in the AI boom is entirely through the lens of: is this thing going to destroy my life?
如果你在这里没有股权（或者更重要的是，在其中一家公司工作并有权获得普通人只能梦想的股票），那么你参与人工智能热潮完全是通过这样的视角：这件事会毁掉我的生活吗？
Much of the age of AI is like this, the privatized gains and socialized losses.
人工智能时代的大部分时间都是这样，私有化的收益和社会化的损失。
It’s a contributor to the discourse around the “permanent underclass.” Jasmine Sun wrote about this concept in the New York Times a few weeks ago, the idea that if you don’t get wealthy now, you will never, ever be successful.
它是围绕“永久下层阶级”的讨论的贡献者。几周前，Jasmine Sun 在《纽约时报》上写到了这个概念，即如果你现在不致富，你将永远不会成功。
I spoke to a room of Stanford undergraduates a few weeks ago and that term was floated a few times.
几周前，我与斯坦福大学的本科生进行了交谈，这个词被提及了几次。
The vibecession, the marked disconnect between economic data and consumer sentiment, is back in the discourse again, with terrific new research from Jared Bernstein and Daniel Posthumus pointing to enormous price level variability as a driver of negative sentiment and Annie Lowrey at the Atlantic advocating for erasing the term entirely and replacing it with the term “ permacession .”
经济数据与消费者情绪之间明显脱节的“振动放弃”再次回到人们的讨论中，贾里德·伯恩斯坦（Jared Bernstein）和丹尼尔·波塞莫斯（Daniel Posthumus）的出色新研究指出，巨大的价格水平波动是负面情绪的驱动因素，而《大西洋月刊》的安妮·劳瑞（Annie Lowrey）则主张完全删除这个术语，并用“永久放弃”一词取而代之。
I read the 600+ comments on her piece — all of them, plus many on r/Economics.
我阅读了关于她的文章的 600 多条评论——所有这些评论，还有许多关于 r/Economics 的评论。
Most relitigated what she’d already granted and what many people grant when the vibecession comes around: that housing and healthcare and groceries are brutal.
大多数人都对她已经给予的以及许多人在震动发生时给予的东西重新提出了抗议：住房、医疗保健和食品杂货是残酷的。
But the ones who actually engaged her question — why the vibecession exists — kept talking about the concept of economic security, which appears to be defined as a:
但那些真正提出她的问题——为什么存在振动转移的人——一直在谈论经济安全的概念，它似乎被定义为：
Bounded downside: an illness or layoff can’t erase everything you’ve built
有限的缺点：疾病或裁员无法抹去你所建立的一切
Predictable floor: you can count on and plan around where you are
可预测的楼层：您可以根据自己所在的位置进行规划
Reward for work : A perceived link between effort and outcome still holding
工作奖励：努力和结果之间的联系仍然存在
Anticipated progress: And a believable path to the next thing: the job, the house, the kid.
预期的进展：以及通往下一件事的可信路径：工作、房子、孩子。
The vibecession is complicated, but there seems to have been a structural break in sentiment in 2022, I think largely driven by structural gaps in how people feel connected to their economic reality.
经济衰退很复杂，但 2022 年的情绪似乎出现了结构性断裂，我认为这主要是由于人们感觉与经济现实的联系存在结构性差距。
Economic data captures a moment, but I think sentiment is capturing people’s concern about their economic future.
经济数据捕捉到了一个时刻，但我认为情绪捕捉到了人们对经济未来的担忧。
People can’t save, so of course, they aren’t going to think to positively about the future they can’t save for.
人们无法储蓄，所以当然，他们不会积极思考他们无法储蓄的未来。
Below are the charts for consumer confidence and consumer sentiment, both nosing downwards, with the University of Michigan Consumer Sentiment Survey 5 showing a marked decline among all generations (most notably, for Gen X, the increasingly squeezed ‘sandwich generation’).
下面是消费者信心和消费者信心的图表，两者均呈下降趋势，密歇根大学消费者信心调查 5 显示所有世代的消费者信心均显着下降（最明显的是 X 世代，即日益受到挤压的“三明治一代”）。
When none of those four conditions of economic security hold, the casino looks rational.
当这四个经济安全条件都不成立时，赌场就显得理性了。
Of course it does.
当然可以。
Prediction markets did roughly $25 billion in volume in April alone.
仅 4 月份预测市场的交易量就达到约 250 亿美元。
A recent Wall Street Journal article found that 70% of Polymarket bettors have lost more money than they made.
《华尔街日报》最近的一篇文章发现，70% 的 Polymarket 投注者损失的钱比赚的多。
The system is designed to extract from people who need a fast win and to compound for people who can afford technology to get ahead.
该系统旨在从那些需要快速获胜的人中提取利益，并为那些能够负担得起技术以取得成功的人提供复合。
There are also people who are opting out of the system because they have economic security.
也有人因为经济安全而选择退出该系统。
While middle-class kids are being told to embrace AI to keep up, wealthy families (including many who make the AI products) are pulling their kids out of algorithmic credentialing entirely.
虽然中产阶级的孩子被告知要拥抱人工智能才能跟上时代的发展，但富裕家庭（包括许多制造人工智能产品的家庭）正在让他们的孩子完全脱离算法认证。
No-screens private schools are growing, with handwritten essays, oral examinations, in-person seminars, human access: you can pay your way out of technology.
无屏幕私立学校正在发展，提供手写论文、口试、面对面研讨会、人工访问：你可以花钱摆脱技术。
The elite version of education is becoming analog.
精英版的教育正在变得模拟。
Everyone else is stuck talking to ChatGPT.
其他人都被困在与 ChatGPT 交谈中。
The thing that is good for you is something that you have to be wealthy enough to access.
对你有好处的东西是你必须足够富有才能获得的东西。
AI (probably?) isn’t going to destroy your life, but it’s accelerating the closure of doors that were already closing, and the panic about that is rational.
人工智能（可能？）不会摧毁你的生活，但它会加速已经关闭的大门的关闭，对此的恐慌是合理的。
I do think we are facing a duration problem, which ties into the economic security/vibecession issue.
我确实认为我们面临着持续时间问题，这与经济安全/振动问题有关。
Things are just taking longer than they used to.
事情只是比以前花费了更长的时间。
Career formation takes longer because retirement is pushed farther out, which is great because people are living longer but creates career ladder issues.
职业形成需要更长的时间，因为退休时间被推迟，这很好，因为人们的寿命更长，但会产生职业阶梯问题。
Skill compounding takes longer.
技能复合需要更长的时间。
Family formation, home ownership, financial stability, career stability–it’s all getting pushed out by about 5ish years.
组建家庭、拥有住房、财务稳定、职业稳定——这一切都被推迟了大约五年。
The thing that used to happen between 18 and 25 is now happening between 25 and 30.
过去发生在18岁到25岁之间的事情现在发生在25岁到30岁之间。
The news cycle and the social media cycle and the AI hype cycle and the casino cycle have all gotten faster, and the timescale of building a durable adult life has gotten slower, and the gap between those two things is enough to drown in.
新闻周期、社交媒体周期、人工智能炒作周期和赌场周期都变得更快，而建立持久的成人生活的时间尺度却变得更慢，这两件事之间的差距足以被淹没。
25 is the new 18, you might say.
你可能会说，25 是新的 18。
Figuring out who you are, what you’re good at, how the world works is now happening later.
弄清楚你是谁、你擅长什么、世界是如何运作的，这些都是稍后才会发生的事情。
Partly because of the pandemic knocking a few developmental years off (I lost a good chunk of my early twenties to COVID and now am 28 years old and just now figuring out how to be a person in the modern universe).
部分原因是新冠疫情让我的发育时间缩短了几年（我二十岁出头的大部分时间都因新冠病毒而消失，现在我已经 28 岁了，刚刚弄清楚如何在现代宇宙中做人）。
It’s a different clock than what the world expects from young people.
这与世界对年轻人的期望不同。
So the failures that we often state as problems–the delayed home buying (which is also part of high mortgage rates and high home prices) and the marriage data and the wage data and all of the other data–those are functions of people just taking a long time.
因此，我们经常称之为问题的失败——延迟购房（这也是高抵押贷款利率和高房价的一部分）、婚姻数据、工资数据和所有其他数据——这些都是人们花了很长时间的函数。
It’s a longer development timescale colliding with a faster extraction timescale.
这是更长的开发时间尺度与更快的提取时间尺度的冲突。
That’s why the cheating and casino economy is so enticing.
这就是为什么作弊和赌场经济如此诱人。
Everything takes so long.
一切都需要那么长的时间。
The despair and the doom and the booing and the financial nihilism are all rational responses to being told you are behind on a schedule that quite literally cannot apply to you.
绝望、厄运、嘘声和金融虚无主义都是当你被告知你落后于时间表时的理性反应，而时间表实际上并不适用于你。
Tyler Cowen published some notes on jobs advice in the age of AI .
泰勒·考恩 (Tyler Cowen) 发表了一些关于人工智能时代就业建议的笔记。
His first principle is to look for “messy jobs,” the ones hard to describe, that change by the day, with many discrete tasks, as well as work in biomedical, work in energy, run experiments, gather data.
他的首要原则是寻找“混乱的工作”，这些工作很难描述，每天都在变化，有许多离散的任务，以及生物医学工作、能源工作、进行实验、收集数据。
Go where the capex is going.
去资本支出去的地方。
I think it’s terrific advice, and wanted to compile other things I’ve heard over the past several months.
我认为这是很棒的建议，并且想整理一下我在过去几个月中听到的其他内容。
Pick the manager, not the company: The training pipelines that used to build up junior employees have been more or less gutted across most white-collar industries.
选择经理，而不是公司：在大多数白领行业中，用于培养初级员工的培训渠道或多或少都被破坏了。
The single biggest variable in whether your first job teaches you anything is the person who manages you, so as you go through interviews, try to get a sense of what that relationship might be like.
你的第一份工作是否能教会你任何东西，最大的一个变量是管理你的人，所以当你通过面试时，试着了解这种关系可能是什么样的。
The search is the job: One warm introduction is worth fifty cold applications.
搜索就是工作：一份热情的介绍胜过五十份冷淡的申请。
If you don’t have a network, the work of building one is the work.
如果您没有网络，那么建立网络的工作就是工作。
Lean into creativity: Gillian Tett’s piece makes this really clear.
发挥创造力：吉莉安·泰特 (Gillian Tett) 的作品清楚地表明了这一点。
Firms want good thinkers, and they are prioritizing creativity.
公司需要优秀的思考者，并且他们优先考虑创造力。
They want people who are unique and who can look at hard problems and come up with novel solutions.
他们想要的是独特的、能够解决难题并提出新颖解决方案的人。
Original thinking is an asset in the age of AI.
原创思维是人工智能时代的财富。
The goal isn’t to compete with the AI, but to be the person who knows what to do when it all stops working.
我们的目标不是与人工智能竞争，而是成为知道在一切停止工作时该怎么做的人。
Build something: Networks come from building a living portfolio online.
建立一些东西：网络来自于在线建立一个活生生的作品集。
It’s very unfortunate that Twitter has devolved into what it is now, but the smartest people in the world still flock to the site.
非常不幸的是，Twitter 已经沦落为现在的样子，但世界上最聪明的人仍然涌向该网站。
Building a voice, interacting with people on social media, and developing evidence that you can think in public is enormously valuable.
发出自己的声音，在社交媒体上与人们互动，并找到可以在公共场合思考的证据，这些都是非常有价值的。
Ernie Tedeschi wrote a terrific piece about the surge of new US business applications, specifically the rise of AI solopreneurs who are using the technology to run companies or side businesses.
Ernie Tedeschi 写了一篇精彩的文章，介绍了美国新商业应用的激增，特别是使用该技术来运营公司或副业的人工智能个体企业家的崛起。
Be AI native, not AI proof: What jobs get more leverage from AI?
是人工智能原生的，而不是人工智能证明：哪些工作可以从人工智能中获得更多影响力？
Sarah Eckhardt and Nathan Goldschlag at the Economic Innovation Group have been tracking this and found that students are flocking toward AI-exposed degrees, not away.
经济创新集团的萨拉·埃克哈特 (Sarah Eckhardt) 和内森·戈德施拉格 (Nathan Goldschlag) 一直在追踪这一情况，发现学生们纷纷涌向人工智能相关的学位，而不是放弃。
Part of this is momentum–the jobs that pay the most tend to the the most AI-exposed, like finance and engineering.
其中一部分是动力——薪酬最高的工作往往是最受人工智能影响的工作，比如金融和工程。
This is the importance of learning about the tooling behind AI.
这就是了解人工智能背后的工具的重要性。
We can boo AI, and we should for the way people talk about it, but there is still opportunity.
我们可以嘘人工智能，我们应该按照人们谈论它的方式来嘘人工智能，但仍然有机会。
Being able to help a non-tech company automate some tasks is likely one of the fastest growing jobs out there .
能够帮助非科技公司自动化某些任务可能是目前增长最快的工作之一。
The Pope was the first world leader to really go toe-to-toe with AI .
教皇是第一位真正与人工智能正面交锋的世界领导人。
It was a relief (especially as a Catholic, it was cool) to see someone say something substantial and thoughtful.
看到有人说出一些实质性的、深思熟虑的话，真是一种解脱（尤其是作为一名天主教徒，这很酷）。
The Pope acknowledged that AI is indeed helpful, but it’s not morally neutral.
教皇承认人工智能确实有帮助，但它在道德上并不中立。
It needs guidance.
它需要指导。
We otherwise don't know what AI is to us, evidenced by Paul Graham, one of the people who pushes the private market around AI forward, working it out in real time on Twitter:
否则我们不知道人工智能对我们来说意味着什么，保罗·格雷厄姆（Paul Graham）就是证明，他是推动人工智能私人市场向前发展的人之一，他在 Twitter 上实时解决了这个问题：
There is a “right” way to use the product, but no one has made that clear, at any level, other than Pope Leo.
该产品有一种“正确”的使用方式，但除了教皇利奥之外，没有人在任何级别上明确表示这一点。
The discourse around AI is growing increasingly negative, and a technology that could have been used as a force for good, was marketed as a job killer.
关于人工智能的讨论变得越来越负面，一项本可以被用作正义力量的技术，却被宣传为就业杀手。
The despair and the doom and the booing and the financial nihilism are all rational responses to being told that you are behind on a schedule that doesn’t really apply anymore.
绝望、厄运、嘘声和金融虚无主义都是当你被告知你落后于一个不再适用的时间表时的理性反应。
The rules are changing.
规则正在改变。
Bounded downside, a predictable floor, reward for work, and a believeable path to the next thing are the conditions under which a future feels possible.
有限的下行空间、可预测的下限、工作奖励以及通往下一件事的可信路径是未来感觉可能的条件。
For a lot of people, none of that holds.
对于很多人来说，这些都站不住脚。
This is a reader-supported publication.
这是一本由读者支持的出版物。
To receive new posts and support my work, consider becoming a paid subscriber.
要接收新帖子并支持我的工作，请考虑成为付费订阅者。
Students are pushing back against AI integration on campus .
学生们正在反对人工智能在校园内的整合。
The universities are spending millions of dollars on partnerships that the students, who are their customers, do not want.
大学花费数百万美元建立合作伙伴关系，而作为其客户的学生并不想要这些伙伴关系。
The backlash is only growing, as communities fight data center buildouts and vote out politicians that align with artificial intelligence
随着社区反对数据中心扩建并投票罢免与人工智能结盟的政客，这种强烈的反对意见只会越来越大
Later on in that trip, I had landed in a city and waited 15 minutes for an Uber at the airport, which I felt like was a long time.
后来在那次旅行中，我降落在一个城市，在机场等了 15 分钟的 Uber，我感觉这段时间很长。
But as we started driving, a pipe burst on a main road, leading to a 20 minute car ride taking about 90 minutes.
但当我们开始开车时，主干道上的一根管道爆裂，导致原本 20 分钟的车程需要大约 90 分钟。
Humans had to come in and fix the problem.
人类必须介入并解决问题。
Google and Amazon own a lot of Anthropic, so you can get exposure, but it’s not as direct as it could be
谷歌和亚马逊拥有很多 Anthropic，所以你可以获得曝光，但它并不像想象的那么直接
Great essay!
很棒的文章！
One point predates AI, but will become more important: "But that requires firms to think about jobs in a way that is more than a cost metric, which they don’t tend to do." Amen.
有一点早于人工智能，但将变得更加重要：“但这要求企业以一种不仅仅是成本指标的方式来考虑工作，而他们往往不这样做。”阿门。
I saw this in the extreme in my 40 years as an engineer and manager in high-tech R&D.
在我担任高科技研发工程师和经理 40 年的经历中，我极端地看到了这一点。
The easy-to-measure gets funding and the hard-to-measure gets drip-fed or cut.
易于衡量的项目获得资金，而难以衡量的项目则得到滴灌或削减。
Well staffed R&D, where I worked, get's throttled by underfunded HR, IT, admin support, tools, and office space which need to be thought of as force multipliers, not overhead.
我工作的地方，人员配备充足的研发部门，却因人力资源、IT、管理支持、工具和办公空间资金不足而受到限制，这些都需要被视为力量倍增器，而不是开销。
Last week a friend was lamenting that their management told everyone to use AI, but wouldn't fund the AI licenses.
上周，一位朋友感叹他们的管理层告诉每个人都使用人工智能，但却不为人工智能许可证提供资金。
Some things never change.
有些事情永远不会改变。