Three months ago, we began our podcast miniseries by asking how likely artificial intelligence is to take large numbers of jobs. Our first guest, Box CEO Aaron Levie, argued that mass disruption would be highly unlikely. And several subsequent guests, from Amazon Web Services CEO Matt Garman to labor economist Kathryn Ann Edwards, expressed similar skepticism about a labor wipeout.
三个月前，我们开启了这个播客迷你系列，讨论的问题是：人工智能抢走大量工作岗位的可能性有多大。第一位嘉宾、Box 公司 CEO 亚伦·列维（Aaron Levie）认为，大规模冲击不太可能发生。此后的几位嘉宾——从亚马逊云科技（Amazon Web Services）CEO 马特·加曼（Matt Garman）到劳动经济学家凯瑟琳·安·爱德华兹（Kathryn Ann Edwards）——也都对「劳动力被团灭」的论调表达了类似的怀疑。
The argument has significant emotional appeal — who doesn't want to believe that technology will create more jobs than it eliminates? And yet I try to apply extra skepticism whenever anyone tells me what I want to hear. Which is why my ears perked up over the past 14 episodes when founders like Wabi's Eugenia Kuyda told me that she was no longer hiring junior engineers, and Replit CEO Amjad Masad told me the company had begun to replace big enterprise software contracts with home-coded alternatives.
For our final episode, I wanted to speak with someone who has considered the problem from all the major perspectives we've covered here from the start: operator, software builder, and civic leader. And that wish led me to Clara Shih.
Shih spent the past two decades building software for some of the world's biggest tech firms. After early stints at Google and Salesforce, she founded and ran Hearsay Systems for a decade before returning to Salesforce to run Service Cloud. In 2023 she was named CEO of Salesforce AI, where she led the launch of the company's agent platform, Agentforce. The next year she moved to Meta to build and run its business AI group, making agents that now answer customer messages for businesses on WhatsApp, Messenger and Instagram.
过去二十年，史宗玮一直在为全球最大的几家科技公司开发软件。早年在谷歌（Google）和 Salesforce 工作之后，她创办并经营 Hearsay Systems 达十年之久，随后回到 Salesforce 执掌 Service Cloud。2023 年，她被任命为 Salesforce AI 的 CEO，主导发布了该公司的智能体平台 Agentforce。次年她转投 Meta，组建并领导其商业 AI 部门——他们打造的智能体如今正在 WhatsApp、Messenger 和 Instagram 上替企业回复客户消息。
It was while working at Meta last fall that Shih saw something that altered the course of her career. Thanks to the AI agents that the company had recently deployed, a product development process that once required user researchers, designers, product managers, and three kinds of engineers could be reduced into one or two people and a prototype. Shih noticed that the agents were making similar strides in marketing, distribution, and privacy review. And so soon she started taking down entry-level job postings, she told me, because she no longer felt that she needed them.
正是去年秋天在 Meta 工作期间，史宗玮目睹了改变她职业轨迹的一幕。得益于公司新近部署的 AI 智能体，一个过去需要用户研究员、设计师、产品经理和三类工程师才能完成的产品开发流程，如今可以压缩成一两个人加一个原型。她还注意到，智能体在市场营销、渠道分发和隐私审查等环节也取得了类似的进展。她告诉我，不久之后，她开始撤下初级岗位的招聘启事——因为她觉得自己不再需要这些人了。
There remain significant limits to what agents can do — see Katie Paul's account in Reuters this week of how Mark Zuckerberg's plan to cut as much as 60 percent of the company this year due to AI efficiencies was derailed by (among other things) underperforming agents.
当然，智能体能做的事情仍有明显局限——可以看看本周路透社（Reuters）凯蒂·保罗（Katie Paul）的报道：马克·扎克伯格（Mark Zuckerberg）原本打算借助 AI 提效在今年裁掉公司多达 60% 的员工，而这个计划之所以受挫，（部分原因）正是智能体的表现不及预期。
Still, Shih told me, the experience radicalized her. This spring, she left Meta (though she remains a senior advisor) and started the New Work Foundation, a nonprofit, along with a consumer brand called Dear CC that delivers tools and advice to entry-level workers. Everything the organization makes is free: a podcast in which hiring managers explain what they're looking for, a data tool called Field Report that shows the AI exposure of different majors and occupations, and a new mentoring app called Game Plan that matches rejected job applicants with peers and a mentor to make the search faster and less lonely.
尽管如此，史宗玮告诉我，那段经历还是「激进化」了她。今年春天，她离开 Meta（仍保留高级顾问身份），创办了非营利机构「新工作基金会」（New Work Foundation），同时推出面向初级职场人的消费品牌 Dear CC，为他们提供工具和建议。这个机构的一切产品都是免费的：一档由招聘经理解释他们到底想要什么样的人的播客；一个叫 Field Report 的数据工具，展示不同专业和职业受 AI 影响的程度；还有一个新出的辅导应用 Game Plan，把求职被拒的申请人与同伴和导师匹配起来，让求职之路更快一些、也没那么孤独。
Most of the builders I spoke to for this series told me that jobs would be fine — they would just be different. Shih is willing to say that the optimistic story she once told herself about her own products — that automating the rote work would free customer support workers for higher-order tasks — has "primarily not been true."
In our conversation, she lays out a three-way taxonomy for how AI reshapes a job, borrowed from MIT economist David Autor; predicts that one in five corporate roles is "especially going to be challenged"; explains why she disagrees with her nonprofit advisor Andrew Yang about universal basic income; and offers a hot take about who will actually do the legal, marketing, and accounting work of the future.
在我们的对话中，她借用了麻省理工学院（MIT）经济学家戴维·奥托（David Autor）的框架，给出 AI 重塑一份工作的三种情形；预言公司里五分之一的岗位「将尤其受到冲击」；解释了她为什么在全民基本收入（UBI）问题上与基金会的顾问杨安泽（Andrew Yang）意见相左；还抛出了一个辛辣判断：未来的法律、营销和会计工作，究竟会由谁来做。
Here's our conversation, lightly edited for clarity and length.
以下是我们的对话实录，为清晰和篇幅做了轻度编辑。
Casey Newton: You've said that last fall, when you were still at Meta, you watched AI agents match and then beat some of your best people on real tasks, and that you felt "radicalized" in that moment when you saw it working. Can you take us into that room with you? What was the task, and what did you see?
凯西·牛顿（Casey Newton）：你说过，去年秋天你还在 Meta 的时候，亲眼看到 AI 智能体在真实任务上追平、然后超越了你手下最优秀的一些员工，而在看到这一幕的那一刻，你感觉自己被「激进化」了。能带我们回到那个现场吗？当时是什么任务？你看到了什么？
Shih: Sure. It feels like just yesterday. It started off in our product design and product development. There was this traditional process of coming up with an idea, doing user research, surveying and interviewing users, then coming up with mockups, then translating that into a project requirements doc, then passing that off to a front-end engineer and a back-end engineer and an ML engineer. We saw all those steps collapse into one or two people being able to ideate in a room, generate the prototype with vibe coding, test it with real users as well as simulated users, and then have a much leaner team of people build that into production. Seeing is believing, and in that moment, I just imagined this amplifying across the economy. We're in for a big ride.
Newton: What you're saying is that seemingly overnight, it was as if that entire stack could be handled by a person or two. Where did your mind go from there? What did you start to think this would mean, both for the company you were at and the broader economy?
Shih: Once you start seeing this pattern — and of course, at a place like Meta, you're under extreme pressure to deliver — you start thinking about how you can apply this to other areas, other bottlenecks, other business processes to help us go faster. So: marketing, distribution, privacy policy. Of course, you've got humans in the loop, experts reviewing the final output. But you're able to collapse what previously took 10 steps and 10 days into a matter of minutes. As we started doing this, I couldn't stop thinking about what this would mean if other companies do this, and you multiply this across an entire economy with many companies, many industries. It's not that this isn't going to be a great place that we land. But what does that transition look like for the people whose jobs are now radically transformed?
史宗玮：一旦你开始看到这个模式——当然，在 Meta 这样的地方，你背负着巨大的交付压力——你就会开始琢磨怎么把它用到其他领域、其他瓶颈、其他业务流程上，帮我们跑得更快。于是是营销、分发、隐私政策。当然，过程中始终有人在环（human in the loop），由专家审核最终产出。但你可以把过去 10 个步骤、10 天的工作压缩到几分钟。当我们开始这样做的时候，我脑子里挥之不去的问题是：如果其他公司也这么做呢？把这个效应乘以整个经济体里那么多的公司、那么多的行业，会怎样？我倒不是说我们最终到达的地方不会很好。我是想问：对于那些工作被彻底改写的人来说，这个过渡过程会是什么样子？
Newton: Some people I've talked to have said a variation of, "Well, Casey, it's easy to automate a task, but it's hard to automate a job." So maybe you can use AI to generate that slide deck now, or send some emails, but you're still going to need that person to navigate the organization and use their critical thinking skills. What do you make of that argument as a reason that people don't need to take AI job disruption that seriously?
牛顿：我采访过的一些人说过类似的话：「凯西，自动化一项任务很容易，自动化一份工作很难。」也许你现在可以用 AI 生成幻灯片、发邮件，但你仍然需要那个人在组织里纵横捭阖、运用批判性思维。你怎么看这种「所以大家不必把 AI 冲击就业太当回事」的论点？
Shih: The challenge with any type of new disruption, whether it's AI or previous paradigm shifts, is that the impact is never uniform. There's actually been great research done on this specific topic by David Autor and his lab at MIT. They basically proved that the impact on a job depends on what percentage of the tasks in a job get automated, and also which specific tasks. So there are basically three cases of what can happen.
史宗玮：无论是 AI 还是以往的范式变革，任何新型冲击的难点都在于：它的影响从来不是均匀的。戴维·奥托和他在 MIT 的实验室其实就这个课题做过非常出色的研究。他们基本上证明了：一份工作受多大影响，取决于这份工作里有多大比例的任务被自动化，以及被自动化的具体是哪些任务。所以基本上有三种情形。
Case one is if a significant percentage of the tasks get automated, like in translation work, like in customer service. Then we're going to see some direct substitution, just displacement happening — not to 100 percent of the jobs, but to enough where it starts to no longer be a great job to go into or to stay in.
Case two, and this is the happy case, is that what gets automated are the routine tasks. This is what optimists believe — and I want to believe this, and I think that this will happen to certain jobs — where previous bottleneck tasks, like code snippet generation and test case generation, get automated. What ends up happening is that the people who are in the role really do get freed to do higher-order tasks, and then you've got the Jevons paradox as well, and you can see some really great outcomes, such as what happened to software engineers over the last two decades. I think it'll continue, actually, for software engineers as well as radiologists and other types of roles.
Case three is the not-great case, along the lines of case one. Case three is that there are more jobs that are created, but because what the AI automates is the expert tasks, the barrier to entry to getting that job drops very suddenly. This is what happened to taxi drivers with the advent of GPS driving directions, which is the first AI disruption that took place. The combination of GPS driving directions plus rideshare platforms like Uber taking off just flooded the labor supply with lots of drivers. You do have Jevons paradox, so you've got more demand for rides, but at a certain point demand starts to plateau. You still have labor supply coming in, so you do end up creating more jobs, but those jobs are no longer quality jobs. In fact, in metropolitan areas like New York and London, being a driver now, you're making less than a living wage.
情形三就不好了，和情形一类似：岗位确实越创造越多，但因为 AI 自动化的是「专家任务」，入行门槛会突然大幅降低。出租车司机的遭遇就是如此——GPS 导航的出现，是第一次真正意义上的 AI 冲击。GPS 导航加上 Uber 这类网约车平台的崛起，让劳动力供给瞬间涌入大量司机。杰文斯悖论确实存在，打车需求变多了；但需求到一定程度就见顶了，而劳动力供给还在涌入。于是你确实创造了更多岗位，但这些岗位不再是体面的岗位。事实上，在纽约、伦敦这样的大都市，现在当司机挣的钱已经低于维生工资（living wage）。
Newton: You helped to build a couple of products that are relevant here. Agentforce — Salesforce CEO Marc Benioff said it let the company go from around 9,000 customer support staff to 5,000 people. And Meta's Business AI is a customer service agent that is deployed across WhatsApp and other products, and I think the idea there is that it might reduce those businesses' need for staff. Customer service and support are classic entry-level jobs. When those products were getting built, was there an idea of, hey, this will enable businesses to hire fewer people? Was that part of the conversation, or was that not on y'all's minds?
牛顿：你自己就参与打造了几个与此直接相关的产品。Agentforce——Salesforce 的 CEO 马克·贝尼奥夫（Marc Benioff）说过，它让公司的客服人员从约 9000 人减到 5000 人。还有 Meta 的 Business AI，是部署在 WhatsApp 等产品上的客服智能体，其卖点大概就是能帮企业减少对人工的需求。客服与支持是典型的入门级岗位。当年打造这些产品的时候，「这能让企业少雇人」是讨论的一部分吗？还是当时根本没往那想？
Shih: I would say it was in the backs of our minds, but we were so excited about building this. And I think at the time, a lot of people — I'll speak for myself — I believed in this happy case that we'll build these products, and people who work in customer support will use Agentforce, they'll use our AI products to automate the rote tasks, and then that'll free them up for complex problem solving and relationship building. I think that has been a little true, but primarily not been true.
史宗玮：我得说，这个念头在我们脑海深处是有的，但当时我们太兴奋了，一心只想把它做出来。而且当时很多人——我只代表我自己——相信的是那个美好的版本：我们做出这些产品，客服人员会用 Agentforce、用我们的 AI 产品把机械重复的活儿自动化，然后他们就能腾出手来做复杂的问题解决和关系维护。现在看来，这个版本有一点点成真，但基本上没有成真。
Newton: How do you want to help the people being affected? What do you think can be done for them right now?
牛顿：你想怎么帮助那些被波及的人？你觉得眼下能为他们做些什么？
Shih: The short answer is no one knows. No one has a crystal ball on exactly how this plays out. And of course, young people are not the only people who are being affected by AI. You asked me earlier whether I think this is a product of zero interest rates or macro factors. Certainly those are non-zero contributors. But I think there have been enough studies now isolating those variables that show that AI is directly responsible for a lot of this job loss. And I myself took down entry-level job postings because I was able to use AI and because I was under so much pressure to deliver fast. So I can see this playing out.
史宗玮：简短的答案是：没有人知道。谁都没有能精确预言走向的水晶球。当然，被 AI 波及的不只是年轻人。你之前问过我，这是不是零利率或宏观因素的产物。这些肯定是非零的贡献因素。但我认为，现在已经有足够多的研究把这些变量剥离出去，证明 AI 本身就直接造成了其中相当一部分岗位流失。我自己就亲手撤下过初级岗位的招聘启事——因为我能用 AI，也因为我背负着快速交付的巨大压力。所以我看得见这件事正在发生。
What can be done? No one knows for sure, and that's why we have to bring humility and a beginner's mind and a willingness to experiment, and that's really what the New Work Foundation is about.
Newton: The subject that everyone talks about is software engineering and the automation of coding, and I wonder, based on what you've seen in other fields — your Field Report tool flags the legal occupation as having a very high automation risk, despite there being lots of open roles — if we might be too focused on coding. Are we at risk of missing some other signs where we're already starting to see disruption?
牛顿：人人都在谈论的话题是软件工程和编程自动化。但我很想知道，根据你在其他领域看到的情况——你们的 Field Report 工具把法律职业标记为自动化风险极高，尽管这个领域还有大量空缺职位——我们是不是太盯着编程了？我们有没有可能正在错过其他已经开始出现冲击的信号？
Shih: Yes. I actually don't think that software engineers are at risk, because I think that the mindset that you get training in computer science — and I'm biased, but I really think that mindset of thinking in algorithms, thinking in repeatable, reusable modularity — is exactly the skill set that is needed to be able to set up systems of agents and to evaluate their outputs. I actually think the opposite, which is that there are going to be many more software engineers than even the rosiest current predictions, and that software engineers will take over these other jobs: legal, marketing, accounting. That's my hot take.
Newton: I'm curious how you think this winds up changing the shape of organizations and what they hire for. I've been asking a version of this to basically everyone I've spoken to. On one end there are founders like Eugenia Kuyda at Wabi, who basically told me she's only hiring "star athletes" now — the people she wants at her relatively small startup are just absolutely elite. On the other hand, Matt Garman, the CEO of AWS, said replacing junior employees with AI is one of the dumbest things he's ever heard; he thinks there's so much advantage in bringing them into the workplace. You've run very large teams inside two of the biggest software companies in the world. What do you think is going to happen?
牛顿：我很好奇，你觉得这最终会如何改变组织的形态、改变企业招人时的标准。这个问题我几乎问过每一位受访者。一端是 Wabi 的尤金妮娅·库伊达这样的创始人，她基本上告诉我她现在只招「明星运动员」——她那家规模不大的初创公司只要绝对的精英。另一端是 AWS 的 CEO 马特·加曼，他说用 AI 取代初级员工是他听过最蠢的事情之一，他认为把年轻人带进职场有巨大的好处。你在全球最大的两家软件公司里都带过非常大的团队。你觉得接下来会发生什么？
Shih: I have a few predictions. One: about one in five roles today within companies are preparing some sort of artifact for someone else in the company to look at. It could be preparing a brief. It could be drafting a slide deck. It could be coming up with an order form that a salesperson then delivers to the customer. I think those roles are especially going to be challenged, because the end person who's ultimately accountable for that outcome and ultimately externally facing — whether it's a salesperson dealing with a customer, or a recruiter working with an external candidate, or a senior legal person who's interfacing with regulators — that person increasingly may find it easier and faster to use AI than to coordinate across multiple of these input-and-output types of roles.
史宗玮：我有几个预测。第一：今天公司里大约五分之一的岗位，工作内容是「为公司里另一个人准备某种交付物」。可能是写简报，可能是画幻灯片，可能是做一份由销售交给客户的订单表。我认为这类岗位将尤其受到冲击——因为那个对结果负最终责任、直接对外的人，无论是面对客户的销售、对接外部候选人的招聘官，还是与监管机构打交道的高级法务，会越来越发现：用 AI 比在好几个这种「输入-输出型」岗位之间来回协调更简单、更快捷。
Two is on the junior side. In order to be effective at using AI, you need to have enough domain experience and domain expertise to provide context to get the best response, but then also to be able to review and critique and refine what the AI comes back with. What you don't want are really junior people who have no idea what looks good and what looks bad, where you're giving something to them and they just delegate it to AI, and then they're just passing the AI response back to you — in which case that's not really adding value.
第二点在初级员工这一侧。要用好 AI，你需要有足够的领域经验与专业知识，才能提供上下文、拿到最好的回答；然后还要有能力审核、批评、打磨 AI 返回的东西。你绝不会想要这样的初级员工：完全分不清好坏，你把活儿交给他，他转手外包给 AI，再把 AI 的回答原样传回给你——那样的话，他并没有真正创造任何价值。
That's something we're really focused on with Dear CC: helping young people start to get that hands-on experience working with AI, even in just one domain, so that they understand the art and science of both the prompting and agent setup, as well as the eval and review side on the other end.
这正是 Dear CC 专注在做的事：帮助年轻人哪怕只在一个领域里，开始积累与 AI 共事的实操经验，让他们既懂提问和搭建智能体这一头的艺术与科学，也懂另一头的评估与审核。
Newton: I know it's relatively early in this new project, but I would love to hear about some of the early experiences you've seen among the young people you're working with.
牛顿：我知道这个新项目还处于早期，但我很想听听你在接触到的年轻人身上看到的早期观察。
Shih: There are really different stages that people are at. The first stage we want to tackle is that there are a lot of very disillusioned Gen Z grads today. They were promised a bill of goods. These people did everything they thought they were supposed to, and now they're finding themselves — many with college debt to pay off — unable to find the kind of job they went to college for. We see this with all the booing at commencement speeches from Eric Schmidt and others. So the first thing we want to do is address this mindset and this information asymmetry. There are dynamics that those of us who work in AI understand that I believe are important for every young person and every person in the country to understand. Name the problem — and I think that a lot of people, once they have a mental model of what's going on, are going to make smart decisions.
史宗玮：人们所处的阶段确实很不一样。我们首先要面对的一个阶段是：今天的 Z 世代毕业生里有大量幻想破灭的人。他们被开了一张空头支票——这些人做了所有他们以为「应该做」的事，结果发现自己——很多人还背着助学贷款——找不到当初上大学时瞄准的那种工作了。埃里克·施密特（Eric Schmidt）等人在毕业典礼演讲时台下一片嘘声，就是这种情绪的体现。所以我们要做的第一件事，就是改变这种心态、消除这种信息不对称。有些正在发生的动态，我们这些做 AI 的人是清楚的，而我认为每个年轻人、这个国家的每个人都应该了解。先把问题说清楚——我相信，很多人一旦有了「事情到底是怎么回事」的心智模型，就会做出明智的决定。
What we've learned — and this is why the community part is so important in what we do — is that when we lean into AI, the temptation is to say, okay, we don't need any humans, we can just create an AI agent as a mentor. No. That is not what people want. The best motivation, the best mentorship comes from conversations like this — in person, over video, building trust, building relationships. So we're connecting people, because these young people feel really lonely and isolated in their search.
我们学到的一点是——这也是为什么「社区」在我们的工作中如此重要——一旦用上 AI，人很容易产生这样的想法：好，那我们不需要真人了，直接造一个 AI 智能体当导师就行。不对。这不是人们想要的。最好的激励、最好的师徒关系来自像这样的对话——面对面，或者视频连线，建立信任，建立关系。所以我们要把人和人连接起来，因为这些年轻人在求职中感到非常孤独、非常孤立。
What we found in our survey is that by month six of being unemployed, people start to question their own self-worth, their own sense of identity. Many of these young people have been high achievers their whole lives, and all of a sudden there is this rude awakening, and they have parents putting pressure on them, asking why they're doing gig work, not understanding that there's been this broader shift in the job market. So we want to put them with people like them, who can provide support but also be accountability partners as they go through each of the steps in their game plan: updating their LinkedIn profile and resume, knowing which parts to use AI to spruce up versus where not to overuse AI and create spam for these job applications, teaching them how to network, how to have a conversation and reach out to a human hiring manager so they can bypass the AI screening that so many companies have put in place.
我们在调查中发现：失业到第六个月，人们就开始怀疑自己的价值、怀疑自我认同。这些年轻人里很多一辈子都是「别人家的孩子」，突然之间被现实狠狠打醒；父母还在旁边施压，问他们为什么在打零工——父母并不理解就业市场已经发生了结构性的转变。所以我们想把他们和同类人放在一起——既能提供支持，也能在执行「游戏计划」每一步时互为问责伙伴：更新领英（LinkedIn）主页和简历；知道哪些部分可以用 AI 润色、哪些地方不能滥用 AI（否则只会给招聘方制造垃圾申请）；教他们如何经营人脉、如何与真人招聘经理对话并取得联系，从而绕过那么多公司都已部署的 AI 简历筛选。
Newton: You're approaching this problem from the level of the individual person, and I love having those conversations because they're empowering. But I also believe that we're probably going to need solutions at the government level here — it can't just be left up to every individual to find a path through. I'm curious if you've thought about that, either just for your personal views or as a place you might want to take the nonprofit. Are there policies out there that you like — wage insurance, universal basic income — or things you can imagine yourself lobbying for?
Shih: Absolutely — organizations like the New Work Foundation are not going to solve this on our own, because we have to get every stakeholder group. Government has to take action, because there are policy actions that are required. There are things that employers have to do — maybe government can incentivize employers to do certain things. There are things that higher ed and K-12 have to do, and then there are things that individuals have to do based on personal agency and personal action.
One of my co-founders is Andrew Yang — he's our founding advisor — and he of course has a lot of strong opinions about policy actions. I actually disagree with him. I don't think that universal basic income on its own will address the gap that's widening. And the reason — of course, it's Maslow's hierarchy of needs. First, you have to make sure that people have a living wage. But as humans, we need autonomy, mastery, and purpose, and for 250 years in our society, and for thousands of years before that in Western society, that has come from work. So if work is going to start to change in such dramatic ways, bringing people along means giving them a living wage, but also giving them a sense of purpose.
I don't know exactly what that looks like, but what gives me hope is a project that's been running in my hometown for the last couple of decades. It was put in place by a nonprofit and by my friend's dad, who is the longtime mayor of Arlington Heights, Illinois. The group is called Connections to Care, and they connect recent retirees and other volunteers in the community with older people in their 80s and 90s, to take them to doctor and dentist appointments and to the grocery store. These are tasks you could hire someone to do — you could Instacart or DoorDash — but it's just so much more meaningful when you connect people to each other and create that sense of shared community and purpose. In this future AGI world, whatever that looks like, that could be an answer. Maybe we can find inspiration not just from national service, which so many people have talked about, but also local service. If you look around us, there are all kinds of unmet needs in local schools, nursing homes, among our neighbors. If we could play a coordination role, that could be very interesting.
我并不知道那具体会是什么样子，但给我希望的是我家乡一个已经运转了二十多年的项目。它由一个非营利组织和我朋友的父亲——伊利诺伊州阿灵顿海茨（Arlington Heights）任职多年的市长——共同设立，叫「关怀连接」（Connections to Care）：把社区里刚退休的人和其他志愿者，与八九十岁的老人连接起来，陪他们去看医生、看牙医、去杂货店。这些活儿你完全可以雇人来做——可以叫 Instacart 或 DoorDash——但当你把人和人连接起来、创造出那种共同的社区感和目标感时，意义完全不同。在未来那个 AGI 世界里——不管它长什么样——这可能就是答案之一。也许我们不仅要从很多人谈过的「国家服务」里找灵感，也要从「本地服务」里找。环顾四周，本地的学校、养老院、邻里之间，到处是没被满足的需求。如果我们能扮演协调者的角色，那会非常有意思。
Newton: This is the last episode of this miniseries, so I want to try to tell you what I think I have learned about jobs and the economy over these past episodes, and you can tell me if I've got it basically right, what I'm missing, or if you disagree with me. The aggregate numbers about AI-related job disruption still look mostly okay. We are not living in a crisis yet. At the same time, you can look at studies like the Stanford "canaries in the coal mine" work, and it does seem like for some entry-level jobs, and for jobs that are considered very exposed to AI, we are starting to see some early pain — and we do seem to have isolated AI as the variable for why we are seeing this. So if that is the case, and you assume that AI capabilities are going to continue to improve, which I do, my assumption is that a year from now we're going to see more people in more pain, and we're going to need to have developed, hopefully by then, an actual coordinated society-level response to a problem that I imagine is going to get worse. Clara, do I sound like a reasonable person, or have I completely lost it?
牛顿：这是这个迷你系列的最后一期，所以我想试着总结一下这几期下来我对就业与经济学到的认识，你来告诉我我是否基本说对了、漏掉了什么，或者你不同意哪里。关于 AI 冲击就业的总量数据，目前看起来大体还行，我们还没有身处危机之中。但与此同时，看看斯坦福那项「煤矿里的金丝雀」研究就会发现：对某些入门级岗位、对那些被认为高度暴露于 AI 的岗位，早期的痛感已经开始出现——而且我们似乎已经把 AI 单独分离出来，确认为导致这一现象的变量。如果情况确实如此，再假设 AI 的能力会继续提升——我相信会——那么我的推测是：一年之后，我们会看到更多人的处境更痛，而到那时我们必须已经拿出一套真正社会层面的协同应对——因为我预想这个问题会恶化。克拉拉，我听起来像个讲道理的人吗，还是我已经完全疯了？
Shih: You sound completely reasonable. We've already seen the trend data from last fall versus the summer — it is trending in a certain way. And in addition to it getting worse for junior-level employees, I think we're going to start to see, as agentic model capabilities continue to push the frontier, it'll go from one year out of school, two years out of school, to people who are three or four years out of school. So it is a moving target, and that's why it's so important to teach and encourage not just a skill set — because a skill set can get you the job today — but the mindset, the first derivative of continuous learning, of hustle, of being more entrepreneurial. Not just thinking that you'll take a job and be there and grow steadily for the next 10 years, but that it's going to be volatile — and preparing yourself for that financially and psychologically. That's very important.
Newton: Maybe let's end by asking you a question that I'm sure you're getting all the time, which is basically a variation of: what do I do? If I'm a sophomore in college right now and I have my eye on law school eventually, but I'm reading on your website that law seems really highly exposed to AI — what are you telling that person? Is this a time to rethink their entire future career path? Is it a time to just get comfortable with AI skills and hope that those are enough to carry you into a good job? Or is it something else?
牛顿：最后用一个问题收尾吧，我猜你经常被人问到这类问题的各种变体：「那我该怎么办？」假如我现在是大学二年级学生，原本打算以后读法学院，却在你们网站上读到法律职业受 AI 影响程度极高——你会对这个人说什么？现在是重新思考整条职业道路的时候吗？还是只要练好 AI 技能、指望它们足以把自己送进一份好工作就够了？又或者是别的什么？
Shih: I say three things. First, really find what you love. Don't choose a major because your mom told you to or because you see that it makes the most money today. Find something that you really love, because then it won't feel like work to go really deep in it — and this economy rewards deep expertise.
Two: you do need to start learning AI skills. Not basic ChatGPT 101, Claude 101, but actual serious agentic systems. Understand how context works. Understand how tool calls work. Start to learn how to build evals, and actually ship something. Ship a legal agent — I get parking tickets a lot — for someone to fight their parking tickets. It could be anything, and you'll know it's successful if you show it to someone who is not in your family and they decide to use it. That's the measure of success.
第二，你确实需要开始学 AI 技能。不是「ChatGPT 入门」「Claude 入门」那种基础课，而是真正严肃的智能体系统：搞懂上下文是怎么运作的，搞懂工具调用（tool call）是怎么运作的，开始学习如何构建评估（eval），并且真的做出一个东西来。比如做一个帮人申诉违章停车罚单的法务智能体——我就经常收到罚单。做什么都行；而判断它是否成功的标准是：你把它拿给一个不是你家人的人看，对方决定真的用它。这就是成功的度量。
And three: make sure that you invest in people skills and have that community around you, because the next few years are going to be bumpy. We saw this with the manufacturing shock of the '80s and '90s — people suddenly seeing jobs shift and change, even though the American economy in aggregate actually improved. We just have to buckle up, and the best way to do that is to have a financial safety net, but also a social safety net of people who love and care and support us.