为什么人工智能实际上不会夺走你的工作

今天在AI每日简报中，我们将讨论为什么AI实际上并不会夺走你的工作。

Today on the AI Daily Brief, we're discussing why AI actually won't take your job.

AI每日简报是一档每日更新的播客和视频节目，聚焦AI领域最重要新闻与讨论。

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

好了，朋友们，在开始之前先做个简短的公告。

All right, friends, quick announcements before we dive in.

首先，感谢今天的赞助商：毕马威、Robots and Pencils、Blitzy和AIUC。

First of all, thank you to today's sponsors, KPMG, Robots and Pencils, Blitzy, and AIUC.

要获取无广告版本的节目，请前往patreon.com/aideallybrief，或在Apple播客上订阅。

To get an ad free version of the show, go to patreon.com/aideallybrief, or you can subscribe on Apple Podcasts.

如需了解节目赞助、订阅我们的通讯，或了解生态系统中的其他事项，请访问aidailybrief.ai。

To learn about sponsoring the show, sign up for our newsletter, or anything else in the ecosystem, go to aidailybrief.ai.

今天是周末，这意味着当然，这是一期‘深度思考’节目。

Today, it is a weekend day, which means, of course, that this is a Big Think episode.

而今天，我们将探讨一个在人工智能领域中最具争议的话题。

And today, we're taking on a topic that is just about as fraught as anything in artificial intelligence.

这当然就是工作岗位被取代的问题。

That is of course the question of job displacement.

每天都有新的报道说某家公司削减了员工规模，至少部分归咎于人工智能，或者某些研究显示了所有可能被人工智能取代的工作。

Every day there is some new story about a company reducing its workforce, blaming AI at least in part, or some study which shows all the jobs that could be replaced by AI.

而且，美国人对当前经济状况本来就不太安心。

And it's not like Americans are particularly comfortable with the state of the economy already.

现在我要明确的是，本集我的论点并不是说我们完全不必担心就业问题。

Now I want to make clear that my argument this episode is not that we shouldn't be concerned at all about jobs.

我的观点是，我们通常在讨论这个问题时方向错了。

My argument is that in general we're having the wrong conversations about it.

那么，让我们谈谈为什么‘人工智能会取代所有工作’这个问题是错误的几个原因。

So let's talk about a few reasons why will AI replace all the jobs is the wrong question.

第一个问题是，这种说法似乎认为只有白领工作才重要。

The first problem is that it sort of acts as though white collar jobs are the only category that matters.

白领工作确实是美国总劳动力的重要组成部分。

Now white collar jobs are a big part of the total US workforce.

而确实，这一波由技术驱动的岗位替代之所以对人们产生如此不同的影响，其中一个原因就在于此。

And it is absolutely true that one of the reasons that this particular wave of technology driven job displacement is hitting people so much differently.

坦率地说，我们过去经历或历史上曾发生的大多数技术颠覆，首先冲击的都是蓝领和体力劳动岗位。

Frankly, most of the previous tech disruptions that we've experienced or that we've had in our history have hit blue collar and physical jobs first.

而AI却首先针对白领岗位，这实际上颠倒了这一趋势，并带来了一些相当重大的影响。

The fact that AI is on the other hand coming first for white collar jobs is a real reversal of that trend with some fairly big implications.

白领工作者在经济上相对更富裕，也因此在政治上拥有更多话语权，因此我认为，对AI的反弹可能性也随之上升。

White collar workers are proportionally more economically well off and by extension politically enfranchised and so the potential for backlash to AI I think goes up.

然而，很明显，AI正在提醒人们：白领和知识型工作并不是唯一的就业选择。

And yet still it's very clear that one of the things that's happening with AI is that it's reminding people that white collar, knowledge work type jobs aren't all that's out there.

事实上，这至少在一定程度上暴露了一个事实：过去那种‘在高中表现优异、进入好大学、背负巨额债务完成学位、再靠一份体面的白领工作全部赚回来’的路径，早在AI出现之前就已经出了问题。

In fact, it's kind of exposing a little bit at least that the pipeline to white collar jobs do really well in high school, get into a good college, go massively into debt for your college degree, make it all back with your nice white collar job was broken before AI ever came along.

大学学费太昂贵，而其回报也普遍不足以证明其成本是合理的。

College is too expensive and didn't translate well enough into high earning jobs in general to justify its cost.

即使AI最终成为压垮骆驼的最后一根稻草，这个系统本身早已岌岌可危。

That was a system that was already on the fritz even if AI ends up being the straw that breaks the camel's back.

在人工智能发展的早期阶段，我们就已经能看到它对知识型工作的影响，正促使人们重新评估自己到底应该追求什么样的工作，这是非常根本的转变。

You can see already in the early days of AI that its impact in knowledge work is causing people to reevaluate the very fundamentals of which types of jobs they aspire to.

这个转变可能会把我们带向不一样的方向：哪怕部分白领岗位会逐渐消失，带来一定影响，但人们主动做出的职业转向也能缓解这些问题。

That process could lead us into different places where the desiccation of certain categories of white collar jobs, while impactful, is also mitigated by the shifts that people are making.

接下来，我们为什么不该总问「人工智能会不会取代所有工作」这个问题的下一个原因是：

Now the next reason that we shouldn't be asking Will AI replace all the jobs?

这个问题本身就过度放大了近期相关行业动态的影响。

Is that the very question itself is massively over rotating on recent announcements.

当然，近期已经有一连串的裁员公告，其中即便没有把AI列为裁员的唯一原因，也至少将其归为了原因之一。

There, of course, have been a string of layoff announcements where AI was cited as, if not the cause, at least some part of the cause.

支付服务商Block裁员40%，亚马逊等企业也开展了大规模裁员，还有不少企业的CEO纷纷发出警告，称AI可能会逐步改变公司内部的岗位结构。

We had Block laying off 40% of its workforce, big layoffs at companies like Amazon, and more broadly warnings from CEOs around how AI was likely to change the composition of job structures within their companies over time.

现在甚至还出现了统计面板，专门记录有多少工作岗位被AI取代。

We've even got scoreboards now of how many jobs have been displaced from AI.

但与此同时，也有相当充分的证据表明，至少就目前来看，人们将本就会发生的裁员与岗位削减过多地归咎于了AI。

And yet there is also some fairly good evidence that AI is, at this point, being disproportionately blamed, let's say, for job cuts and layoffs that would have happened anyway.

最近一份对1000名招聘经理的Resume.org调查显示，近60%的人表示，他们强调AI在裁员中的作用，是因为相比直接说明裁员或招聘冻结是由财务压力驱动的，这种说法更受利益相关方的欢迎。

A recent resume.org survey of 1,000 hiring managers found nearly 60% said that they emphasized AI's role in layoffs because it is viewed more favorably by stakeholders than saying layoffs or hiring freezes are driven by financial constraints.

与此同时，只有9%的受访者表示AI已完全取代了某些岗位。

Meanwhile, only 9% of those respondents said that AI had fully replaced any roles.

《彭博社》的一篇评论文章写道：其运作原理已被充分理解。

A Bloomberg opinion piece reads: The reason it works is well understood.

数十年来关于市场对裁员公告反应的研究，已经确立了一种一致的模式。

Decades of research on how markets react to layoff announcements have established a consistent pattern.

投资者会惩罚那些将裁员归因于问题的公司。

Investors punish companies that frame cuts as a response to problems.

但当公司把同样的裁员描述为积极的重组时，这种惩罚就会消失。

But when a company frames the same cuts as proactive restructuring, the penalty disappears.

裁员的公开理由比裁员本身更为重要。

The stated reason for the layoff matters more than the fact of the layoff.

AI已成为最强大的积极表述工具，围绕AI进行重组被视为一种增长信号。

AI has become the most powerful proactive frame available, where restructuring around AI is a growth signal.

我们在疫情期间过度招聘，而收入下滑是一个责任信号。

We overhire during the pandemic and revenue softened is an accountability signal.

因此，在思考我们应当对AI相关工作岗位流失有多担忧时，至少需要考虑我们当前是否正经历一场大规模的‘AI洗牌’。

So one thing we have to at least consider as we think about how much we should be concerned about AI related job displacement is the extent to which we are experiencing a wave of AI washing right now.

第三个理由说明‘AI将取代所有工作’这个问题本身是错误的，尽管它被频繁提出，因为有些人认为，我们对AI从颠覆编程和软件工程到颠覆其他类型知识工作的可迁移性做出了错误的假设。

A third reason why will AI replace all the jobs is the wrong question to be asking even though it's being asked a lot is that there are some who think that we're making some incorrect assumptions about how translatable it is to go from AI's disruption of coding and software engineering to AI's disruption of other types of knowledge work.

最近，卡内基梅隆大学和斯坦福大学联合发布了一项名为《AI代理在多大程度上反映了真实世界的工作？》的研究。

Recently, Carnegie Mellon and Stanford University released a joint study called How Well Does Agent Development Reflect Real World Work?

摘要写道：AI代理正越来越多地在与人类工作相关的基准上进行开发和评估。

The abstract reads: AI agents are increasingly developed and evaluated on benchmarks relevant to human work.

然而，这些基准测试在多大程度上能代表整体劳动力市场仍不明确。

Yet it remains unclear how representative these benchmark efforts are of the labor market as a whole.

研究指出，代理开发往往以编程为中心，与人类劳动和经济价值集中的领域之间存在显著不匹配。

The study, they say, reveals substantial mismatches between agent development that tends to be programming centric and the categories in which human labor and economic value are concentrated.

伊桑·莫洛赫教授这样总结：所有努力都集中在编程相关的基准测试上，但编程只是人们实际工作的一小部分，这使得AI发展的真正趋势变得模糊不清。

Professor Ethan Moloch puts it like this: All of the effort is going into benchmarking for coding, but that is a small part of the actual jobs people do, which leaves the true trajectory of AI progress less clear.

如今，人工智能已经彻底改变了软件工程的整体架构，这早已不是什么秘密。

It is no secret at this point that the entire structure of software engineering has changed because of AI.

这就是过去几个月里我们正亲身经历的重大行业变革。

This is the big disruption that we've been living through for the past few months.

不仅如此，我们还看到编程能力开始影响其他岗位中知识工作的开展模式。

What's more, we're seeing coding start to impact how knowledge work works in other roles.

当所有人都能借助软件解决自身问题时，其他工作的本质也会随之发生改变。

When everyone can use software to solve their problems, it's going to change the nature of other jobs as well.

但现在有部分人形成了一条相当明确的推导逻辑：既然AI能把编码做得极其出色，那它就能把其他所有工作都做得一样好。

And yet there's a pretty clear through line that some are assuming from AI can do coding super well to AI can do everything else super well.

还有一种观点指出，编码工作具备的某些特性——比如它能拥有确定的正确性，存在明确的对错之分——其实并不适用于其他领域的知识型工作；那些工作要复杂混乱得多，无法像编码这样清晰地区分对错、分辨优劣。

And there's an argument that particular attributes of coding for example, its ability to have deterministic correctness and a clear right and wrong don't actually apply to other areas of knowledge work which are much more messy and confused, and don't have quite the same ability to distinguish correct from incorrect and good from bad.

目前这是一场热议的争论，但它进一步印证了一个观点：整件事的发展会比我们坐在原地、亲眼看着AI彻底颠覆传统软件工程流程时所预想的更加复杂微妙。

Now this is a big debate right now, but it's more evidence in the column that all of this is going to be more nuanced than we perhaps think, sitting from our seat watching how AI has just cleaved through the old traditional software engineering process.

第四个认为“AI会不会取代所有工作”是个错误问题的理由是，这个问题低估了人类偏好作为一股市场力量的影响力。

A fourth reason that will AI replace all the jobs is the wrong question is the extent to which it discounts human preference as a market force.

我被困在巴西时写过相关内容，那是我从南美返程的55小时旅途里的一段经历，当时我感慨，虽然这段旅程里人工智能在不少环节都帮了忙，但几乎在每一个关键节点，我都希望能对接上实实在在的人。

I wrote about this when I was stuck in Brazil as part of my fifty five hour trip back from South America, with the reflection that although AI was useful throughout parts of that experience, at basically every critical juncture I was looking for access to an actual human.

我当时想要对接真人，是因为我不想只能被动接受那些白纸黑字写死的规定。

The reason that I was looking for access to a human is that I didn't want to be subject to the policies as they were written.

我想试着争取特殊待遇。

I wanted to try to talk my way into special treatment.

我的核心看法是，人类设计的各类体系本身就内置了一定量可灵活裁量的非硬性执行空间。

The argument that I was making is that human systems are designed with some amount of discretionary non compliance built in.

如果我们把一切都交给AI处理，完全不依靠人类判断力来制定人性化的例外规则，我认为整体的系统会变得更加脆弱不堪。

And if we turn everything over to AI, with no ability to use human judgment to make human exceptions, I think systems in general get more brittle.

除此之外，很多围绕就业展开的讨论都默认市场的唯一功能就是追求极致效率。

Even beyond that, though, a lot of the discourse around jobs assumes that the only function of markets is to be as efficient as possible.

但这只是实现目标的手段，而非最终目的。

But that's means and not end.

市场的真正作用是满足人类的诉求与需求。

What markets are actually trying to do is service human desires and human needs.

而且如果人们想要的是由他人参与主导的人际体验，那就算人工智能能让一切都变得更高效也无济于事。

And to the extent that human desires are for other human mediated experiences, it doesn't matter if everything can be more efficient because of AI.

市场会自发地调整结构，去供应人们真正想要的东西，也就是与人相关的互动与服务。

Markets will organize themselves around provisioning what people want, namely other humans.

我再说明一下，这并不是说人工智能不会带来行业冲击，但冲击的规模和波及的领域会受到诸多因素的制约，绝不会只沿着效率和生产力提升的势头一路狂飙。

Again, this is not to say that there won't be AI disruption, but how much it is and in what areas is subject to a lot of forces that aren't just the onslaught of efficiency and productivity.

第五个证明“人工智能会取代所有工作”是个错误问题的原因是：在人类历史上，这种恐慌从来都没有成真过，至少从未以人们所担忧的那种方式应验过。

A fifth reason why will AI replace all the jobs is the wrong question is that at no point in history has this fear ever been right At least not in the way that people felt it.

这样的例子数不胜数。

There are infinite examples of this.

从卢德运动时期的纺织业自动化，到农业机械化，再到自动取款机与银行柜员、电子表格与会计，乃至互联网与零售业的发展，皆是如此。

From the Luddites and textile automation, to mechanized agriculture, to ATMs and bank tellers, to spreadsheets and accountants, to the internet and retail.

每一次，人们都只先看到了“创造性破坏”中“破坏”的一面，却没能预见其中“创造”的部分，但实际上每一次这些技术都极大地推动了市场的扩张。

In each case, people spotted the destruction in creative destruction before they saw the creation, but in each case these were massively market expansionary forces.

不过，过去发生的事情，并不能保证未来会以同样的方式重演。

Now the way things have happened in the past does not guarantee that they will happen the same way in the future.

但毫无疑问，技术引发的就业末日恐惧从未如人们所担心的那样成真，这一模式在我们思考AI背景下就业替代风险时，至少值得铭记。

But certainly the pattern that the fear of technological job apocalypse has never actually played out the way that people feared is worth at least keeping in mind when we're considering how much to fear job displacement in this AI context.

第七个理由说明‘AI会取代所有工作’是个错误的问题，这也是我最终保持乐观的基石。

A seventh reason that will AI replace all the jobs is the wrong question is the one that is the anchor of my ultimate optimism.

简而言之，那就是资本主义具有彻底的扩张性。

Which in short is the fact that capitalism is radically expansionary.

我认为，人类对各类事物、体验和服务的需求几乎是无限的。

I think that the human capacity for stuff of every type experiences, services, things is basically unlimited.

我们是贪婪的、不断扩张的需求机器。

We are voracious, ever expanding demand machines.

我认为甚至可以提出一种观点：技术的市场目的，正是拓展市场满足这种无限需求的能力。

I think there's even an argument to be made that the market purpose of technology is to expand the capability of markets to meet this unlimited demand.

约书亚·巴克以另一种方式表达了这一点。

Joshua Back put it a different way.

他写道：许多人认为世界上工作的总量是固定的，如果我们把这些工作交给机器，人类就会失去工作，甚至挨饿。

He writes: Many people believe that there is a fixed amount of work in the world, and if we give these jobs to machines, humans will not have jobs or starve.

这种直觉层面的经济学模型从根本上就是错误的。

This intuitive model of economics is fundamentally wrong.

我们的财富取决于我们能够生产并在彼此之间分配的商品和服务的数量与质量。

Our wealth depends on the amount and quality of goods and services we can produce and distribute among each other.

自动化能让我们为所有人生产出更多的各类产品。

Automation allows us to make more of everything for everyone.

对我们来说，永远都有更多的事情可以去做。

There is always more to do for us.

在自动化让我们摆脱农业、制造业里那些繁重枯燥的劳作，如今又帮我们从记录、计算、评估、记忆等事务中抽身之前，这些事是我们根本无力去做的。

Things we could not afford to do before automation allowed us to get away from the important drudgery of agriculture, manufacturing, and now documenting, calculating, evaluating, memorizing, and so on.

在我看来，有一个确凿的证据能证明这一点：我使用人工智能的场景里，超过90%都不是用来把我原来做的事变得更高效，而是用来完成我以前根本做不到的新事。

Proof positive of this to me is that 90 plus percent of my AI use cases are not doing stuff I used to do a little bit more efficiently it's doing new things that I never could before.

而我完成这些新事带来的最终结果，不是凭空多出来几个空闲小时、交给各位观众的成果总量却没变。

And the net result of me doing those new things is not extra saved hours and the same amount of things delivered to all of you, my audience.

而是我能交付给观众的内容出现了巨幅增长。

It's a massive expansion in what I am delivering to my audience.

之所以除了播客之外，还有Claw Camp、Enterprise Claw、Superintelligent、AI-DB Intelligence和Agent Madness，是有原因的。

There's a reason that in addition to the podcast, there's a Claw Camp and an Enterprise Claw, and a Superintelligent and an AI-DB Intelligence and an Agent Madness.

如果没有AI，这些事情都是不可能实现的。

These are things that would not be possible if it weren't for AI.

这其中还有一个竞争层面的因素。

There's also a competitive dimension to this.

当吉姆·克莱默最近问英伟达首席执行官黄仁勋，如果AI本应提升每个人的生产力，为什么公司还要裁员时，黄仁勋回答：对于有想象力的公司，你会用更多的资源做更多的事。

When Jim Kramer recently asked NVIDIA CEO Jensen Huang why companies are laying people off if AI is supposed to make everyone more productive, Jensen responded: For companies with imagination, you will do more with more.

对于那些领导层已经毫无创意的公司，他们也就无事可做了。

For companies where the leadership is just out of ideas, they have nothing else to do.

他们没有理由去想象超越现状的可能。

They have no reason to imagine greater than they are.

当他们拥有更强的能力时，却并不去做得更多。

When they have more capability, they don't do more.

我过去曾将这种差异称为效率型AI与机遇型AI的区别。

I've referred to this in the past as the difference between Efficiency AI and Opportunity AI.

我认为，我们不可避免地会经历一个人们专注于用更少资源做相同事情的阶段。

I think it's inevitable that we go through a phase where people are focused on doing the same with less.

这就是效率型AI。

That's Efficiency AI.

我也认为，由于我们扩张性资本主义体系的本性，从长远来看，获胜的公司一定是那些选择机会型AI而非效率型AI的公司——即不是用更少资源做相同的事，而是用同样的资源做更多事，或只多花一点资源就做多得多的事。

I also think that it is completely inevitable that because of the nature of our expansionary capitalist system, the companies that win in the long term will be those who opt not for efficiency AI doing the same with less, but for opportunity AI.

换句话说，就是用同样的资源做更多事，或者只多花一点点资源就做多得多的事。

In other words doing more with the same or doing way way more with just a little more.

我最近在Twitter上更直白地表达过：别说我疯了，但我认为，那些为团队中的每个人配备一支智能代理团队的公司，会彻底碾压那些用一支智能代理团队取代整个团队的公司。

I recently put it a little more crassly on Twitter, writing Call me crazy, but I think the companies that give everyone on their team a team of agents are going to kick the crap out of the companies that replace their teams with a team of agents.

这是我最根本的信念之一，也是我相信从长远来看，AI将带来大量就业机会和市场整体规模扩张的原因。

This is one of my most fundamental beliefs and why I believe in the long run AI will cause a mass expansion of jobs and opportunity and just overall market size.

关于‘AI是否会取代所有工作’这个问题，最后还有一个原因：如果真的发生了，如果我们突然看到15%、20%甚至30%的失业率，我们也将需要一种完全不同的社会结构——这种结构根本不需要工作作为参与社会的必要条件。

One final reason why will AI replace all the jobs is kind of the wrong question: Is if that did come to pass, if we all of a sudden saw 15% or 20% or 30% unemployment, we're going to need some totally different structuring of society that doesn't require jobs to be a full participant anyway.

换句话说，不存在这样一个世界：在AI取代所有工作的前提下，社会却在结构上惩罚那些没有工作的人。

In other words, there's no world in which AI replaces all the jobs where society structurally punishes people without jobs.

既然目前这一切都只是关于未来可能发生的情况的理论探讨，有关这类社会层面的探讨会是什么样形态的证据还都只是零星的碎片。

Now given that this is all a theoretical conversation about what could happen in the future, there are only little drips and drabs of evidence of what that type of societal conversation might look like.

但你已经能开始察觉到这类动向了。

But you're starting to see it.

国会议员罗·卡纳前不久就呼吁制定全新的科技社会契约，还提出了七大核心原则。

Congressman Ro Khanna recently called for a new tech social contract and laid out seven big principles.

就在同一周，皮特·布蒂吉格也谈到了需要出台新社会契约的想法。

That same week, Pete Buttigieg talked about the idea of needing a new social contract.

如果有关人工智能会取代工作岗位的预测真的应验，那这类讨论就是我们未来必须要面对的。

If the prognostications of AI job displacement do come to pass, this is the type of conversation that we're going to have.

换句话说，人工智能取代所有工作岗位这件事，绝不会孤立地发生。

AI replacing all the jobs, in other words, would not happen in a vacuum.

好了各位，我们先稍作停顿。

All right folks, quick pause.

这里有个让人难以接受的真相：如果你的企业人工智能战略仅仅是‘我们采购了一些工具’，那你其实根本就没有成型的战略。

Here's the uncomfortable truth: if your enterprise AI strategy is we bought some tools, you don't actually have a strategy.

毕马威选择了更难的路线，成为了自己的首个试点客户。

KPMG took the harder route and became their own client zero.

他们在整个企业内部落地了人工智能和智能体，这并非单纯的科技项目，而是一次全面的运营模式转型。

They embedded AI and agents across the enterprise not as a tech initiative but as a total operating model shift.

而这个转型带来的真正突破是：它拓宽了人们能力的上限。

And here's the real unlock: that shift raised the ceiling on what people could do.

人类始终牢牢占据核心位置，与此同时，人工智能则消除了流程阻碍、挖掘出深层见解，并推动业务发展提速。

Humans stayed firmly at the center while AI reduced friction, surfaced insight, and accelerated momentum.

最终的成果是一支能力更强、权限更足的劳动力队伍。

The outcome was a more capable, more empowered workforce.

如果你想了解这在现实世界中具体是什么样的，欢迎访问www.kpmg.us/ai。

If you want to understand what that actually looks like in the real world, go to www.kpmg.us/ai.

大多数企业并不会苦于没有思路。

Most companies don't struggle with ideas.

它们的难题是如何把这些思路转化为能够真正创造价值的实用人工智能系统。

They struggle with turning them into real AI systems that deliver value.

Robots and Pencils这家公司的成立初衷就是填补这一缺口。

Robots and Pencils is a company built to close that gap.

他们专注、高效地设计并交付由生成式AI和智能体驱动的智能云原生系统，产出清晰明确的成果。

They design and deliver intelligent, cloud native systems powered by generative and agentic AI with focus, speed, and clear outcomes.

Robots and Pencils采用小型高影响力项目组的模式开展工作。

Robots and Pencils works in small, high impact pods.

工程师、战略师、设计师和应用AI专家通力协作，无需应对不必要的阻碍，就能将创意落地为可投入使用的系统。

Engineers, strategists, designers, and applied AI specialists working together to move from idea to production without unnecessary friction.

借助他们的智能体加速平台Roboworks，团队能够交付切实成果，根据项目范围的不同，甚至可以在短短45天内完成首批项目上线。

Powered by Roboworks, their Agentic acceleration platform, teams deliver meaningful results including initial launches in as little as forty five days depending on scope.

如果你的组织已经准备好加快步伐、降低复杂性，并将人工智能的宏图转化为实际成果，那么Robots and Pencils正是为这一刻而生的。

If your organization is ready to move faster, reduce complexity, and turn AI ambition into real results, Robots and Pencils is built for that moment.

欢迎访问robotsandpencils.com/aideallybrief开启交流。

Start the conversation at robotsandpencils.com/aideallybrief.

再次提醒一下，网址是robotsandpencils.com/aideallybrief。

That's robotsandpencils.com/aideallybrief.

Robots and Pencils在Velocity Weekends推出的影响力活动专为氛围编程打造。

Robots and Pencils Impact at Velocity Weekends are for vibe coding.

如今要把一个创意项目落地比以往任何时候都容易，所以尽管打开你最常用的氛围编程工具开始创作吧。

It has never been easier to bring a passion project to life, so go ahead and fire up your favorite vibe coding tool.

但周一总会到来，用不了多久你就会发现自己要面对一堆乱糟糟的微服务、一套上世纪70年代遗留的COBOL系统，还有一份远在你退休之后都未必能做完的工程路线图。

But Monday is coming, and before you know it, you'll be staring down a maze of microservices, a legacy COBAL system from the 1970s, and an engineering roadmap that exists well past your retirement party.

这就是为什么你需要Blitzy——首个专为企业级代码库打造的自主软件开发平台。

That's why you need Blitzy, the first autonomous software development platform designed for enterprise scale codebases.

在每个迭代周期开始时部署，让你的路线图推进速度提升500%。

Deploy at the beginning of every sprint and tackle your roadmap 500% faster.

Blitzy的智能代理会接入你的整个代码库，规划工作内容，并且能自主完成80%以上的工作。

Blitzy's agents ingest your entire codebase, plan the work, and deliver over 80% autonomously.

以算力的速度，产出经过验证、完成全链路测试的高品质代码。

Validated, end to end tested, premium quality code at the speed of compute.

原本需要数月完成的工程工作，如今压缩到短短数天就能完成。

Months of engineering compressed into days.

周末就用氛围编码打造你的个人 passion 项目吧。

Vibe code your passion projects on the weekend.

周一就把Blitzy带去工作里用。

Bring Blitzy to work on Monday.

想了解为什么财富世界500强企业都信赖Blitzy来处理核心业务代码，欢迎访问blitzy.com。

See why Fortune 500s trust Blitzy for the code that matters at blitzy.com.

没错，就是blitzy.com。

That's blitzy.com.

老听众应该都知道，我最近一直在关注全新的AI代理标准AI UC 1.0。

Regular listeners will know that I've been recently following the new AI agent standard AI UC one.

最初勾起我兴趣的是十一实验室（ElevenLabs）、Intercom和UiPath等一众头部AI企业接连宣布获得了该标准的认证。

What piqued my interest initially was a string of leading AI companies like eleven Labs, Intercom, and UiPath announcing their certifications back to back.

但比起哪些企业参与其中，更值得关注的是，AIUC为我们在节目中反复讨论的几个企业级AI落地核心难题提供了可行的解决方案。

But what's even more interesting than who's participating is the way that AIUC represents an answer to some of the key enterprise AI adoption challenges that we talk about on the show all the time.

首先，这个标准的更新节奏完全跟得上AI技术的迭代速度，每个季度都会同步更新。

First of all, the standard actually keeps up with AI being updated every single quarter.

该标准内容全面，由100多位财富500强的安全负责人参与设计，覆盖了所有企业关注的风险点。

It's comprehensive, designed with over 100 Fortune five hundred secondurity leaders to cover all the risks that enterprises care about.

最后，它还会严格测试AI智能体在复杂场景或对抗性攻击下的表现，这和其他大多只涉及规章制度的标准都不一样。

And finally, it rigorously tests how agents behave in tricky situations or under adversarial attacks unlike other standards that are mostly just about policies.

这些优势结合在一起，让企业能够建立足够的信任，有信心部署AI智能体。

The combination gives enterprises the trust they need to deploy AI agents with confidence.

如果想了解更多，欢迎访问aiuc-one.com。

Head to aiuc-one.com if you want to learn more.

网址是aiuc-one.com。

That's aiuc-one.com.

既然我已经聊过为什么我觉得当前有关就业与岗位被取代的讨论方向可能有问题，我在这集节目的开头就承认过，关于这个话题确实有很多值得探讨的重要内容。

So now that I've discussed why I don't think that the current discourse is maybe the right discourse about jobs and displacement, I acknowledged at the front of the episode that there are important conversations to be had about this.

我认为绝不能盲目乐观地看待这些变化，我们要认清一点：哪怕像我这样的乐观主义者对AI会推动经济体系中长期就业岗位扩张的判断是正确的，这也并不代表不会出现一段极其痛苦、充满颠覆且可能持续很久的过渡时期——在这段过渡期里，天翻地覆的变化本身就会引发各类混乱。

I think it's incredibly important to not be Pollyannish about the changes and to recognize that even if the optimists like me are correct about the long term expansion of jobs in the economic system that AI will represent, that is not mutually exclusive, with there being an extremely painful, disruptive, and somewhat protracted liminal in between in which the incredible amount of change does cause its own type of havoc.

换句话说，AI不需要取代所有岗位，就会在短期内给我们带来不少挑战。

In other words, AI doesn't have to take all the jobs for it to cause some challenges in the short term.

顺便一提，这和萨姆·奥尔特曼等人的说法相差不大。

This, by the way, isn't too far from what people like Sam Altman have said.

在近期的贝莱德美国基础设施峰会上，他就提到：我并不是那种对长期就业形势持悲观末日论的人。

At the recent BlackRock US Infrastructure Summit he said, I am not a long term jobs doomer.

我认为我们最终会找到新的工作方向，但未来几年的调整过程会充满阵痛。

I think we will figure out new things to do, but I think the next few years are going to be a painful adjustment.

而且平心而论，如果我们深挖历史，就会发现：虽然过去所有曾被担心会造成净就业岗位减少的技术变革，最终都极大地扩张了就业市场，但每一次这类变革在短期都仍会造成某些类别的岗位被彻底淘汰。

And to be intellectually honest, if we are going to plumb from the depths of history, while it is the case that all of these previous technology disruptions that people were worried would be net job destructive were actually massively expansionary, they did still have the impact in the short term in each case of completely eliminating certain categories of jobs.

纺织自动化大幅扩张了全球经济，但它也让某一类手工业者彻底失去了生计。

Textile automation massively expanded the global economy, but it also wiped out a certain class of artisan.

机械化农业帮助人们摆脱了饥饿，但在这个过程中，大量社群被迫流离失所。

Mechanized agriculture helped ensure that people stopped going hungry, but entire communities were uprooted as part of the process.

而在自动取款机（ATM）与银行柜员、电子表格与会计、互联网与实体零售的案例中，每一次都有部分岗位消失了，哪怕后续会创造出更多新工作。

And when it came to ATMs and bank tellers, spreadsheets and accountants, the internet and retail, in each cases there were categories of jobs that did go away, even if more jobs were later created.

在我们讨论岗位被人工智能取代的话题时，我们有责任跳出那些博眼球的夸张论调，深入剖析真正有可能发生的具体情况。

We owe it to ourselves as we have the conversation about job displacement to move past big bombastic headline grabbing fears and try to get into the nuance of what actually is likely to happen.

所以，让我们来谈谈我认为关于人工智能对就业影响更值得探讨的一系列话题。

So let's talk about a set of what I think are better conversations to have about the impact of AI on jobs.

首先，我认为我们需要更好地描绘出实际的影响。

First of all, I think we're going to want to try to better map the actual impact.

我认为，最好的方法之一不是把工作作为破坏的基本单位，而是转向关注具体任务。

One of the best ways to do that, I believe, is rather than focusing on jobs as the atomic unit of disruption, to instead look at tasks.

人工智能会消除哪些任务？

What tasks will AI eliminate?

从那里，我们可以自然地回溯，找出哪些工作主要由这些任务组成，从而映射出整体的工作受冲击情况。

From there we can obviously go back and ask which jobs are primarily bundles of those tasks and map overall job exposure that way.

但我认为我们需要在任务层面进行深入探讨。

But I think we need to engage on a task by task level.

幸运的是，有一些证据表明人们已经开始这样做了。

And luckily there's some evidence that people are starting to do this.

例如，高盛最近发布了一项研究，他们以任务为基本单位，发现人工智能可能自动化美国25%的所有工作任务。

Goldman Sachs, for example, recently put out a study where they focused on tasks as a unit, finding that AI could automate 25% of all work tasks in The US.

在此基础上，他们还逐行业进行了调研，统计各行业中有多少比例的任务会受到AI的影响。

With that then, they looked industry by industry to see what percentage of that industry's tasks were exposed.

研究得出的结果，和哪些职业会受到冲击的已有规律大致相符。

Now the results followed a fairly similar pattern to which professions were exposed.

但如果从任务维度切入分析，我们就可以进一步探究，哪些岗位的工作内容会大规模转向其他类型的任务。

But by coming in at it from the task level, you can ask how much are certain jobs likely to shift to other types of tasks.

另一个关键问题是，要更深入细致地探讨AI带来的影响究竟意味着什么。

Another important question is to ask in a more nuanced way what AI exposure actually means.

人们往往会有一个误区：如果一项任务会受AI影响，就意味着这项任务很可能会被AI取代，但事实并非必然如此。

There tends to be an assumption when people say that AI is exposed to a task that it means that AI is likely to displace that task, but that doesn't necessarily follow.

芝加哥大学布斯商学院的亚历克斯·伊马斯教授近期写道：受影响并不等同于面临被取代的威胁。

Chicago Booth professor Alex Imas recently wrote: Exposure does not mean threat of displacement.

它的含义甚至可能完全相反。

It can literally mean the opposite.

受AI影响的岗位可能会扩大招聘规模，还能带来更高的薪资水平。

AI exposed jobs may increase hiring and attract higher wages.

这完全取决于两个因素：一是消费者需求的弹性，二是一份工作中受AI影响的任务数量。

It all depends on: a) elasticity of consumer demand and b) number of AI exposed tasks in a job.

当我们从任务层面切入研究时，还有一个重要问题需要厘清：AI能力的过剩储备中，有多少是受时间因素影响，又有多少是源于其他因素。

Another important question as we come in at the task level is to understand how much of the AI capability overhang is a factor of time versus something else.

你可能见过Anthropic近期发布的经济研究里流传很广的这张图，他们在图里分别标注了AI在不同职业类别中的理论覆盖范围，以及AI实际投入应用的实际覆盖范围。

You might have seen this chart going around from Anthropic's recent economic research where they mapped the theoretical AI coverage of AI on different occupational categories versus the observed AI coverage of what it was actually being used for.

这份研究的核心发现是：尽管从理论上来说，AI能够完成管理、商业金融、计算机与数学这类岗位中的绝大部分工作，但目前AI仅被用来处理这些工作里的一小部分任务。

The big story here was that while AI could theoretically do a huge part of jobs like management, business and finance, computer and math, it was being called on only to do a small portion of those things.

这仅仅是因为它还没有时间普及开来，最终就会取代那些工作任务吗？

Is that simply because it hasn't had time to diffuse yet and it will ultimately do all those things?

还是说存在其他更具结构性、系统性且关乎人文因素的原因，导致实际观察到的人工智能覆盖范围永远无法达到理论层面的水平？

Or are there other more structural, more systematic, more human reasons why the observed AI coverage will never match theoretical AI coverage?

比起直接探讨岗位被全面取代的问题，另一个或许更有价值的问题是：智力资源的普及化可能会给各类岗位带来怎样的薪资压力。

Another maybe better question than just full on role displacement is what sort of wage pressure the democratization of intelligence might put on different roles.

前Salesforce人工智能部门首席执行官 Clara Shi 近期撰文指出：虽然人工智能确实会全面取代某些特定岗位，但历史表明，新技术对从业者产生影响时，薪资重置是一种更普遍、更隐蔽，且往往同样具有颠覆性的形式。

Ex Salesforce AI CEO Clara Shi recently wrote: While full AI role displacement will happen in certain roles, history shows that wage resets are a more common, insidious, and often equally disruptive way that new technologies affect workers.

她举了一些例子，比如跨行业挤压，即被取代的工人涌入本行业剩余的工作岗位，导致工资被压缩。

A few examples of that she gives include the intersector squeeze, where displaced workers flood the remaining jobs in their own field compressing wages.

这种现象的第二种表现是劳动力供给增长快于劳动力需求。

A second version of this is labor supply growth outpacing labor demand.

基本上，每个人都能做任何事，这可能导致劳动力供给过剩，从而压低工资。

Basically, everyone can do everything, it potentially floods labor supply, which compresses wages.

此外，还有跨行业工资下调和溢出效应，即被取代的高技能工人转行，通常要接受降薪，同时排挤了原本在岗的员工。

And there's also intersector pay cut and spillover effects, where displaced high skill workers switch fields, often taking a pay cut while displacing incumbent workers.

可以将大学毕业生就业不足作为例子来理解。

Think about college graduate underemployment as an example.

我认为，关于工资压力的这个问题，比单纯问‘AI会夺走你的工作吗？’重要得多，尤其是在短期和中期。

This question about wage pressure strikes me as much more significant, especially in the short and medium term, than just Will AI take your job?

现在，另一个更细致、我认为也更有成效的关于AI与工作的讨论领域，是理解幸存下来的工作如何发生变化。

Now another whole area of more nuanced and I think more productive conversations around AI and jobs has to do with understanding how surviving work transforms.

在这个问题中，一个显而易见的问题是：哪些类型的员工最有可能抵御工作岗位的替代？

One obvious question within this is which types of workers are likely to be most resilient to job displacement?

布鲁金斯学会最近发布了关于这一问题的研究。

Brookings recently released research on exactly this.

它试图对U.

It tried to put some scoring around U.

S.

S.

工人的适应能力进行评分，以应对AI导致的失业。

Workers' capacity to adapt to AI job displacement.

尽管他们在方法论设计上有很多值得质疑的地方，但在我看来，这种讨论更有建设性，尤其是在我们思考政策应对措施时，而不仅仅是关注哪些工作可能被取代。

And while there was lots to question in exactly how they designed their methodology, this strikes me as a more productive conversation especially as we think about policy remediations than just solely looking at which jobs are likely to be displaced.

基本上，如果有两种岗位同样受到AI取代，那么对那些被取代工人更易适应其他岗位的类型实施政策干预，是否更有意义？

Basically, if you have two types of roles that are equally displaced by does it make more sense to have policy interventions for the one where the worker being displaced is much more adaptable and resilient to other types of roles?

还是对那些技能组合、地理区域、储蓄状况等使其更难适应的类型？

Or one where their combination of skills, geographic locale, savings profile, etc.

使他们更难适应？

Makes it much harder for them to adapt?

显然，从干预的角度来看，后一类情况可能更有意义。

Obviously that latter category might make more sense from an intervention standpoint.

关于工作如何转变这一主题，另一个问题是，在每个人都能使用代码和软件的今天，剩下的工作是什么样子？

Another question around this theme of how surviving work transforms is what do the remaining jobs look like now that everyone can use code and software?

我最近在推特上问人们，到目前为止，影响更大的是：软件工程师借助智能体改变了开发方式，还是非软件工程师首次能够用代码构建东西？

I recently asked people on Twitter what had had a bigger impact so far: software engineers changing how they build thanks to agents, or non software engineers being able to build things with code for the first time?

结果非常接近（支持非程序员能编程的比例为46%），但当我问及长期来看哪种影响更大时，这一比例上升了，有三分之二的人认为非程序员掌握编程能力的影响将超过软件工程师改变开发方式的影响。

It was pretty close (at 50 four-forty six percent) in favor of non coders being able to code, but it grew when I asked what will have the bigger impact long term, with a full two thirds thinking that non coders' ability to code would have a bigger impact than software engineers changing how they code.

这引出了一个更广泛的问题，即角色的重新设计。

This gets to a broader question of just role redesign.

虽然我认识很多不认为AI会取代所有人的人，但我几乎不认识谁不认为AI将影响并改变几乎所有工作。

While I know plenty of people who don't think that AI is going to replace everyone, I also pretty much don't know anyone who doesn't think that AI is going to impact and change almost every job.

花更多时间探讨这种变化可能如何发生，以及它将把角色转变成什么，比单纯盲目担忧AI会取代工作要更有意义。

Spending more time on how that change is likely to happen and what it transforms roles into feels better than just some blind worry about AI taking the role in the first place.

与此相关的是，我认为一个非常合理的问题是，平均团队或公司规模会缩减多少。

Related to this is, I think, a very reasonable question of how much the average team or company will decline in size.

或许换一种更清晰的表述：过去完成一个目标平均需要投入的工作量是x，那未来完成同一个目标的平均工作量会是x的二分之一、十分之一，还是百分之一？

And maybe a better way to put this is: if the average amount of work it took to accomplish a goal was x in the past, is the average amount of work to accomplish that goal in the future half of x, one tenth of x, or one one hundredth of x?

基于这个问题，我们又会如何调整为了完成目标而协同工作的团队组织模式？

And based on that, how will it change how we organize working units who work together to accomplish goals?

从一种极端情况来看，所有公司的规模都可以完全保持不变，只需要实现产出的大幅提升就好。

On one end of the spectrum, every company could stay exactly the same size and just have a massive increase in output.

而另一种极端情况是，我们短期内会看到企业大规模精简：先是整合成规模更小的团队，却能完成同样的目标——哪怕之后会进入第二阶段，企业借着这些额外节省下来的资源重新扩张，去达成从前无法实现的新目标。

On the other end of the spectrum, we could see a mass compression in the short term of companies first consolidating around smaller teams that accomplish the same set of goals, even if then the second phase is them once again expanding to accomplish new types of goals that weren't possible before with those additional savings.

和团队规模这个问题有些关联的另一个问题是，人工智能将如何改变管理者和独立贡献者（IC）之间的权力平衡。

Somewhat related to the question of teams is the question of how AI will change the balance of power between managers and individual contributors.

借助智能代理工具，独立贡献者自身很可能会成为自己小型团队的管理者或协调者，这会从根本上提升他们的工作产能。

Thanks to agents, ICs themselves will likely become managers or orchestrators of their own small teams, which will fundamentally change how much they can accomplish.

这很可能会给独立贡献者的职权范围和灵活度带来新的限制，而这些限制原本是为了让他们能开展更多工作而存在的。

That will likely create new types of constraints in and around the remit and flexibility that ICs have to actually go do more.

换句话说，他们会不会被那些为「独立贡献者原本只能完成少得多的工作」这个旧世界设计的管理流程卡住，成为工作瓶颈？

In other words, are they going to be bottlenecked by managerial processes that are designed for a world where any individual contributor could do much less?

帕兰蒂尔首席技术官沙亚姆·桑卡尔最近表示，人工智能将成为对二十世纪管理革命的解药。

Palantir CTO Shayam Sankar recently said on AI is going to be the antidote to the managerial revolution of the twentieth century.

他说，原本被从真正了解自己工作内容的一线员工手中夺走的全部权力，被吸向了那些模糊的中层管理者群体，而现在这种情况正在逆转。

He said all of this power that was sucked away from the frontline workers who actually knew what they were doing to an amorphous blob of middle managers, that's being reversed.

所有的官僚主义都在被削减。

All the bureaucracy is getting cut.

他举了军队的例子。

He gave an example of the military.

在军队中，他说，我看到了一些非凡的人工智能应用开发者，他们并非受过正式训练的计算机科学家。

In the military, he said, I'm seeing incredible AI application developers who are not formally trained computer scientists.

发生了什么？

What happened?

这些人是从哪里来的？

Where did these people come from?

我意识到，他们一直都在那里。

I realize they've always been there.

问题是，这个人十年前会做什么？

The thing is, what would this guy have done ten years ago?

做一份PPT？

Make a PowerPoint?

试图说服某个项目经理，说自己的想法很好，却被告知不行？

Try to convince some program manager that his ideas were good only to be told they weren't?

现在，他只需要躲到角落里两周，自己把它做出来。

Now he just goes away in a corner for two weeks and builds it.

而他争论的是一个实证性的问题。

And he's arguing about something that's empirical.

指挥官会说：这个管用。

And the commander is like, this works.

我们上吧。

Let's go.

关于AI如何改变组织架构，尤其是在咨询圈子里，已经开始出现这样的讨论。

There are the beginnings of the conversation, particularly around, for example, consulting circles around how AI changes the org chart.

但我认为这不仅仅局限于组织架构。

But I think it goes farther than just the org chart.

我认为这更关乎组织领导中不同类别权力平衡的根本性问题。

I think it's more fundamental questions of power balance in different categories of organizational leadership.

关于工作如何转型这一主题，另一个极其重要的讨论是我们如何重新调整对产出的期望。

Another incredibly important conversation around the theme of how surviving work transforms is how we recalibrate output expectations.

我们在这档节目中介绍的最新研究开始表明，虽然我们原本以为AI会节省大量时间，但实际上它却显著加剧了工作强度。

Recent research that we covered on this show has started to suggest that while we thought AI was going to save a bunch of time, it actually in practice is significantly intensifying work.

如果你用过这些工具，可能就会感同身受。

Now if you've used these tools, it'll probably make sense to you.

突然间，所有事情都变得可行了。

All of a sudden, everything is on the table.

没有什么是你做不到的。

There's nothing that you can't do.

至少，感觉就是这样。

At least that's the way that it feels.

所以你就要把所有事情都做完吗？

And so are you just supposed to do everything?

什么才算足够？

What constitutes enough?

我们必须重新讨论人们应该产出多少，因为从今往后，产出能力只会持续扩张。

There's going to have to be a renewed conversation around how much output people are supposed to have, because the ability to output will always be expansionary from here on out.

如果我们不在组织内部管理好这一点，就会引发大规模的倦怠潮。

And if we don't manage that within organizations, it'll just cause massive waves of burnout.

这自然引出了一个问题，或许属于第三类我认为更关键的关于AI与工作的议题，即企业在这一转型过程中的责任。

Which of course gets to the question and maybe a third category of what I think are better questions about AI and Jobs that has to do with corporate responsibility during this transition.

如今，企业责任应该是什么样子？

What does corporate responsibility look like now?

你如何在股东责任与利益相关者责任之间取得平衡？

How do you balance fiduciary responsibility with stakeholder responsibility?

我认为人们正确指出的一点是，公司与员工之间隐含的契约已经破裂了。

One thing that I do think people are right to have identified as having broken is the implicit bargain between companies and their workers.

在二十世纪的大部分时间里，协议是：公司经营得好，员工也过得好。

For much of the twentieth century the deal was: When the company did well, the employees did well.

利润上升，工资也上升。

Profits go up, salaries go up.

人们会加薪，会拿到奖金。

People get raises, people get bonuses.

公司经营不善时，奖金会被冻结，加薪也会被冻结。

Company not doing so well, bonuses get frozen, raises get frozen.

这让人觉得合理、公平，而且直观易懂。

This felt coherent and fair and sort of obvious and intuitive to people.

现在显然过于简化了，认为这种情况一直如此。

Now it is obviously overly reductive to say that that's how it always worked.

当然，在人工智能出现之前很久，对股东的责任与对利益相关者的责任之间的张力就已经带来过重大挑战。

And certainly it was long before AI ever came around that the tension between responsibility to shareholders and responsibility to stakeholders could cause some major challenges.

但我确实认为，尤其是过去几年，人们越来越觉得这两者已经完全脱节了。

And yet I do think that people, especially over the last couple of years, have felt more like these two things have become completely unmoored from one another.

杨安泽近期写道：人类的生活状况与GDP的发展态势正在急剧脱节。

Andrew Yang recently wrote, How humans are doing and how GDP is doing are diverging very sharply.

去年年底，哥伦比亚广播公司新闻频道报道了这样一则新闻：即便裁员潮愈演愈烈，企业利润却在飞速上涨。

At the end of last year, CBS News wrote a story: Corporate profits are soaring even as layoffs mount.

经济学家将这种现象称为‘无就业增长的经济繁荣’。

Economists call it a jobless boom.

就连美联储主席杰罗姆·鲍威尔也在本周的联邦公开市场委员会新闻发布会上表示，美联储对当前新增就业岗位数量极低的状况感到担忧。

Even Fed chair Jerome Powell said in the FOMC Presser this week that the Fed is concerned about the very, very low level of job creation.

事实上，他提到，如果把重复统计的情况修正过来，私营部门的净新增岗位数量实际上基本为零。

In fact, he said if you adjust for overcounting there is effectively zero net job creation in the private sector.

我认为，我们早就该展开一场关于企业责任的更广泛讨论了。

I would argue that we are long overdue for a larger conversation about corporate responsibility.

在我看来，这场讨论的必要性远在生成式人工智能兴起之前就已经存在了。

I think that the need for that greatly precedes the rise of Gen AI.

不过，生成式人工智能的确让这个问题变得愈发尖锐突出。

However, Gen AI is certainly putting quite a fine point on it.

最后一类我所说的关于人工智能与就业的更有价值的问题，聚焦于制度层面和政策层面的应对措施。

Now the last category of what I would call better questions about AI and jobs comes around the institutional and policy response.

有一个问题我格外感兴趣：在当前变化的速度下，真正优质的再培训项目应该是什么样的？

One question that I'm acutely interested in is what do actual good reskilling programs look like given the speed of change?

我认为我们在这个领域严重缺乏高质量的思考。

I believe we are experiencing a huge deficit of good thinking in this area.

我甚至可以说我们现在根本拿不出什么可行的思路，但这并不代表这个问题还没有被人们意识到。

I would almost go so far as to call us bereft of ideas, which is not to say that it's not a problem that's been recognized.

上周五，白宫公布了其国家人工智能立法框架，其中六大核心要点之一是围绕对美国人开展相关教育、打造适配人工智能的劳动力队伍展开的。

On Friday, the White House revealed its national AI legislative framework, and one of the six key points was about educating Americans in developing an AI ready workforce.

这份概述中提到：本届政府希望美国工人能够参与到人工智能驱动的经济增长中，并从中获益，因此呼吁国会进一步推动劳动力发展与技能培训项目，扩大各行各业的发展机会，在人工智能赋能的经济体系中创造新的就业岗位。

The overview says: administration wants American workers to participate in and reap the rewards of AI driven growth, encouraging Congress to further workforce development and skills training programs, expanding opportunities across sectors, and creating new jobs in an AI powered economy.

但只要你读一读篇幅更长的政策全文，就会发现他们完全不清楚这套说法到底该如何落地。

And yet when you read the more extended policy framework, it's clear that they have absolutely no idea what that is supposed to mean.

他们在文中写道，国会应当采用非监管手段，确保包括学徒制在内的现有教育项目、劳动力培训及扶持项目主动将人工智能相关培训纳入其中。

Congress, they write, should use non regulatory methods to ensure that existing education programs and workforce training and support programs, including apprenticeships, affirmatively incorporate AI training.

好的。

Okay.

国会应当扩大联邦层面的研究力度，研究任务层面的劳动力调整趋势，从而为支撑美国劳动力的相关政策提供参考。

Congress should expand federal efforts to study trends in task level workforce realignment in order to inform policies supporting the American workforce.

确实。

Sure.

国会应当强化赠地大学的各项能力，以提供技术支持、启动示范项目，并制定人工智能青少年发展计划。

Congress should bolster capabilities at land grant institutions to provide technical assistance, launch demonstration projects, and develop AI youth development programs.

行吧，这些说法都没什么问题。

Fine, all of these things are fine.

但这些完全无法解决全国性的人工智能再培训问题啊。

But boy are those not an answer to national AI reskilling.

要是还假装这些能解决问题，那简直是疯了。

And to pretend they are is just absolute madness.

我们早已脱离了那个时代：仅仅靠大学开设的课程，或是一套带证书的线上视频，你拿到证书后贴到领英上，这种程度的举措根本不足以应对让劳动者完成彻底转型的实际再培训挑战。

We are firmly out of the world, where a course delivered by a college or an online set of videos with a badge you slap on your LinkedIn after are anywhere close to dealing with the challenge of actual reskilling people for a totally different type of working.

当然，这还更广泛地涉及我们的主要教育体系需要如何变革。

And of course this extends more broadly into how our main education systems need to change.

人们越来越质疑大学是否还值得上。

There is a growing question of whether college is still worth it.

我认为，甚至这个问题本身也应重新定义为：大学如何才能再次变得值得？

And I think probably even that should be redefined to a question of how college could still be worth it.

它应该转变为怎样的形式，才能再次以经济上具有增值意义的方式发挥作用？

What should it turn into to be once again valuable in an economically accretive way?

当然，在教育之外，还有更重大的问题，即什么样的过渡干预措施才是正确的。

And then of course outside of education there's even bigger questions about what the right transition intervention programs look like.

我们应该像安德鲁·杨提出的那样，对机器人征税吗？

Should we, as Andrew Yang has proposed, tax the robots?

或者，像安德鲁·杨提出的那样，我们是否应该实行全民基本收入或全民基本服务？

Or also as Andrew Yang has proposed, should we have universal basic income or universal basic services?

如果我们真的相信人工智能将取代所有工作，那么我们就必须展开这类讨论。

If we really are convinced that AI is going to take all the jobs, those are the types of conversations we need to be having.

但最后还有非常重要的一点，我认为我们还需要具备及时发现新路径的能力，并且支持人们完成转型，适应新出现的经济形态。

But finally, and very importantly, I think we also need to be in a position to spot the new paths as they arise, and to support people's transition to the new type of economy that emerges.

哪怕是在当下，人工智能也并非只会摧毁岗位。

Even right now, AI is not only destroying jobs.

欧洲央行近期发布的一篇博客指出，那些对人工智能接受度最高的企业，实际创造出的岗位比它们缩减的岗位还要多。

A recent European Central Bank blog argued that companies who are the most AI inclined had actually created more jobs than they had lost.

Gusto公司的一项最新研究发现，使用人工智能的小型企业生产效率有所提升，并且雇佣了更多员工。

A recent study from Gusto found that small businesses using AI got more productive and hired more people.

Anthropic近期的一项研究采访了8.1万名AI用户，结果发现已经从AI中获得经济收益的人群绝大多数是企业家、小企业主以及有副业的从业者。

Anthropics' recent research where they interviewed 81,000 AI users found that the people who had already experienced economic benefit from AI skewed heavily toward entrepreneurs, small business owners, and workers with side projects.

对部分人而言，这一模式清晰明了。

For some, the pattern is clear.

克莱顿·克里斯坦森研究所的托马斯·阿内特近期在《eSchool News》发表的一篇专栏文章中提出，人工智能或许会催生出有史以来最具创业精神的一代人。

An op ed in eSchool News from Thomas Arnett of the Clayton Christensen Institute recently argued that AI may unleash the most entrepreneurial generation we've ever seen.

当然，目前的情况也确实如此：创业的边际成本，也就是创立一家企业的成本，不仅已经跌到了历史最低点，还在朝着真正的零成本靠拢。

And of course, it is absolutely the case that the marginal cost of entrepreneurship the cost of starting a business not only has never been lower, but is trending towards actual zero.

我们已经看到新网站、新iOS应用和推送到GitHub的代码出现了惊人的增长。

We've seen an incredible increase already in new websites, new iOS apps, new code pushed to GitHub.

所有这些事物都在急剧上升。

All of these things are exploding upwards.

但任何尝试过创业的人都知道，启动一件事和让这件事成功是两回事。

But as anyone who has ever tried to start anything knows, starting the thing and making the thing successful are not the same.

因此，如果我们正在转变为一个整体上更具创业精神的经济，我们需要什么样的支持？

And so if we are turning into an overall more entrepreneurial economy, what types of support do we need there?

无论是培训、政策，还是其他方面。

Whether that's training, policy, or something else.

显而易见的是，人工智能正在并且将改变许多岗位的形态。

What is abundantly clear is that AI is and will change the shape of so many roles.

它将影响员工对产出的期望。

It will impact expectations of output for workers.

它将影响员工与管理者之间的关系。

It will impact their relationship with managers.

它将影响团队和公司的规模。

It will impact the size of teams and companies.

它将影响市场对该公司应做之事的期望。

It will impact market expectations of what companies should be doing.

换句话说，人工智能将极大地改变工作方式和经济运行方式。

AI will change a huge amount, in other words, about how work happens and how our economy functions.

对大多数人来说，人工智能并不会直接夺走他们的工作。

What it won't do for most people will be to straight up take their jobs.

我的最终观点是，我们正进入一个时期，在这个时代，无论多么吸引点击，我们再也无法承受肤浅的对话。

My argument, ultimately, is that we're moving into a period where we no longer have the luxury of dumb conversations no matter how good they are for clicks.

我乐观地认为，我们正开始迈向一些更好、更细致、更实际、更有成效且方向明确的对话。

I am optimistic that we are starting to move into some of these better, more nuanced and more actually useful and productive and directional conversations.

如果你一路听到了这个播客的这里，我可以肯定地告诉你，你是解决方案的一部分，而不是问题所在。

And if you have made it all the way to this point in this podcast, I can tell you for sure that you are part of the solution and not the problem.

目前，今天的AI每日简报就到这里。

For now, that is going to do it for today's AI Daily Brief.

I appreciate you listening or watching, as always, and until next time, peace.

I appreciate you listening or watching, as always, and until next time, peace.