人工智能将如何影响就业？

人工智能能力的快速提升和企业日益广泛的应用，让一些顶尖技术专家预测，到本十年末，AI可能会导致大量工作岗位消失。

Rapid improvements in AI capabilities and growing corporate adoption have led some prominent technologists to predict that AI could eliminate a mass number of jobs before the end of the decade.

那么，我们究竟该对AI引发的失业危机有多担心呢？

So just how concerned should we be about an AI job apocalypse?

我是艾莉森·纳森，欢迎收听高盛交流会。

I'm Allison Nathan, and this is Goldman Sachs Exchanges.

每个月，我都会邀请投资者、政策制定者和学者，围绕高盛研究部《头号关注》报告中的关键市场议题展开对话。

Each month, I speak with investors, policymakers, and academics about the most pressing market moving issues for our Top of Mind report from Goldman Sachs Research.

这个月，我采访了麻省理工学院的达龙·阿西莫格鲁和尼尔·汤普森，还有高盛研究部全球经济学团队负责人约瑟夫·布里格斯。

This month, I spoke with MIT's Daron Acemoglu and Neil Thompson, as well as with Joseph Briggs, who leads the global economics team in Goldman Sachs Research.

我首先问约瑟夫，他预计AI将取代多少劳动力。

I started by asking Joseph just how much labor displacement from AI he expects ahead.

约瑟夫，我知道你很清楚，关于AI对美国劳动力市场可能造成的影响，目前争议很大，尤其是最近一些公司把裁员归因于AI的应用。

Joseph, I know you are well aware that there's been a lot of debate about AI's potential impact on The US labor market, especially as we've seen some companies citing AI as a factor in recent layoffs.

这些新闻头条，我们都看到了。

We've all seen the headlines.

你预计短期内和长期内，AI会带来多少劳动力替代？

What are you expecting in terms of AI related labor displacement in the near term and over the longer term?

先说清楚一点，如果看今天的劳动力市场，你已经能在几个行业和领域看到AI的痕迹，这些地方AI确实已经开始产生影响了。

So to level set, if we look at the labor market today, you can see the imprint of AI in a few industries and a few sectors where we know that AI is already having an impact.

把科技、管理咨询、平面设计这些已经广泛应用AI工具的行业加在一起，目前AI每个月大概拖慢了1万到1万5千个新增就业岗位。

And so if we combine across sectors like tech and management consulting and graphic design, areas where tools have already been developed and deployed, you can see that overall there's probably a 10,000 to 15,000 drag on month over month job growth from AI impacts.

不过话说回来，这还只是局部的冲击，对整体经济目前还没造成明显影响。

All that being said, it's still a fairly narrow labor market shock, and we're not seeing a big impact today on the broader economy.

但我相信，这种情况未来肯定会改变。

Now, I do expect that will change going forward.

根据我们的基准预测，AI全面普及后能带来15%的生产率提升。结合历史上技术进步推动生产率提高后对就业的替代规律，我们估算，美国大约有9%的劳动者会在AI转型过程中被重新分配到其他岗位。

Under our baseline forecast for a 15% uplift to productivity following full adoption of AI, if we combine that estimate with the historical elasticity between how much does a technology driven productivity increase tend to displace workers, we come up with an estimate that around 9% of all workers in The US will be reallocated to new positions during the AI transition.

这个数字可不小。

That's a pretty big number.

没错，9%的劳动者被AI取代，意味着现在就有1500万人要离开原有岗位，重新找工作。

It is a big 9% of workers being displaced by AI would correspond to 15,000,000 workers leaving or being displaced from their positions today and having to find new jobs.

让9%的劳动者失业，这种自动化和结构性调整的规模，跟上世纪90年代末到2000年代初以及其他重大技术变革时期的情况类似。

Displacing 9% of workers would be the type of automation and reallocation shock that we saw in the late '90s and early 2000s, and other periods of significant technological change.

我特别想强调的是，这会发生在十年的时间里，只要失业和岗位流失是逐步释放的，那么每年对失业率的影响大概不会太大。

What I'd really emphasize is that it is over a ten year period, and as long as the displacement and the job loss is spread out enough, then the impact on the unemployment rate in any given year will likely not be that large.

举个例子，即使按照我们预测的9%劳动者会被替代，每年失业率的上升幅度也仍会低于一个百分点。

So for example, even under our forecast for 9% of workers being displaced, we'd still expect that the unemployment rate increase in any given year would be less than one percentage point.

而且，你的预测中还有一个关键点，就是除了这些岗位消失，还会出现新的工作岗位。

And then also a key part of your forecast is that in addition to these job losses, you'll have creation of jobs.

能详细说说你在这方面的假设和预期吗？

So talk us through your assumptions and expectations there.

是的。

Yeah.

我们并不认为被替代的劳动者会长期失业。

We're not expecting that displaced workers will be displaced over the long run.

我们预计，最终创造的新岗位会足够多，能够把劳动者重新吸纳回劳动力市场。

We do expect that ultimately there are going to be more than enough jobs created to reabsorb workers back into the labor market.

主要原因在于，历史上技术一直带来大量的新增就业机会。

And the key reason for this is that there's a long historical record of technology delivering significant job gains.

我来提几个相关的数据。

A couple stats that I would flag around that.

回顾过去八十年，大约85%的就业增长都来自技术催生的新岗位。

If we look back over the last eighty years, around 85% of job growth has been driven by the technological creation of new positions.

同样，美国劳动力市场非常活跃。

Likewise, The US labor market is incredibly dynamic.

每年我们都能看到大约三千万个新岗位被创造出来。

Every year we see around 30,000,000 jobs being created.

当然，与此同时每年也有两千九百万个岗位消失，这说明技术自动化一直在推动劳动力市场的持续更替——新岗位不断产生，旧岗位不断消失。

Now granted, 29,000,000 are being destroyed all the time, implying that technology automation are constantly leading to a significant amount of labor market churn where new positions are created and jobs are destroyed.

我们认为，未来这种情况还会继续，尤其是在人工智能推动创新的背景下。

Now, we think that this will repeat itself going forward, particularly in a world where AI is enabling innovation.

即使新岗位的创造速度只加快5%，也完全足以吸纳我们预计会被人工智能自动化取代的劳动者。

Even a 5% acceleration in the pace at which new jobs are being created would be more than enough to reabsorb the workers that we're expecting will be displaced by AI driven automation.

所以从长远来看，我并不担心我们会看到永久性的失业。

And so over the long run, I'm really not concerned that we're going to see permanent job losses.

更重要的问题是，新岗位的创造速度能否足够快地抵消短期的不利影响？

The bigger question is, does that pace of new job creation pick up fast enough to offset any near term headwinds?

为了说得清楚一点，你不认同那种认为很多人最终会没工作的观点，对吧？

Just to be perfectly clear, you don't subscribe to the view that we are going to see a world in which a lot of people just don't end up with a job.

对，完全不认同。

Yeah, definitely not.

很多科技评论员说工作岗位会被永久取代，这种观点只盯着失业的一面——就像我们讨论过的，那确实会带来不小的影响，但他们却忽略了新岗位的创造。

I think that the view that has been put forth by a lot of tech commentators where jobs are going to be permanently displaced, It really focuses on the job loss aspect, which as we've discussed, will likely be pretty meaningful, but it ignores the job creation aspect.

只要历史能继续重演，只要技术像以往一样不断带来新的工作机会，长远来看我们就不会出现永久性失业。

And as long as we see history repeat itself, and we see that technology does, again, as it always has, lead to new work opportunities, then we won't see permanent job loss over the long run.

麻省理工学院的尼尔·汤普森对AI会大规模取代工人持保留态度。

MIT's Neil Thompson is less convinced that AI will displace a large number of workers.

他认为，单有技术能力还不足以实现这一点。

He argues that capability alone isn't sufficient to do so.

他指出，工作包含许多任务，只有部分可以被自动化。

And he points out that jobs consist of many tasks, only some of which can be automated.

因此，他预计劳动力市场的调整会比当前AI的能力所暗示的更缓慢、更不均衡。

So he seems to expect a slower and more uneven labor market adjustment than AI's current capabilities suggest.

他还认为，我们能提前看到AI的到来，这一点多少让人安心。

And he also takes some comfort in the idea that we can see AI coming.

以下是最近我和他的部分对话。

Here's some of my recent conversation with him.

我们绝对应该把AI看作一种极具变革性的技术，它不仅能力强大，而且正在迅速提升。

We should absolutely think of AI as being this very transformative technology that is not only very capable, but actually becoming capable very quickly.

但当我们把AI和工作联系起来时，必须清楚，AI的能力只是导致工作变化的一系列环节中的一个。

But when we then want to connect that to jobs, it's really important to say that AI capabilities are only one in a series of steps that lead to a change in jobs.

对吧？

Right?

所以第一步是，如果AI获得了所有必要的信息，它能不能完成这项任务？

And so you first say, okay, could AI do this task if it was given all the right information?

关键在于，如果你能提供所有正确的信息，对吧？

Really crucial in there was that if you give them all the right information, right?

当你想象一些可能的应用场景时，比如在医院挂号，这部分工作确实可以由AI完成。

If you actually think of lots of things you might imagine doing, so you say, oh, well, maybe when you check-in for a doctor's office, so some of this can be done.

但一旦你想要获得医疗建议，立刻就得访问隐私记录之类的东西，这时候你会发现，必须处理大量这类数据，整个流程就变得复杂了。

But of course, as soon as you want to get any kind of medical advice, all of a sudden you have to get access to privacy records and things like that, and so you can very quickly get in a situation where oh you need these kind of records' so there's a whole bunch of stuff needs to be done there.

就算你已经知道怎么搭建一个能获取所有必要信息的系统，接下来还得问：这样做合算吗？

Then even if you say 'okay now I know how to build such a system so that it can get all the right information' you can ask the question, is it cost effective to do?

我们实验室之前的研究表明，在很多情况下，其实并不划算，因为要实现这么精确的系统，运行成本会非常高。

And some of the previous work my lab has done has shown that in many cases, might not be because you might need such an exacting system that it would cost a lot to run.

所以，所有这些环节都必须同时满足才行。

And so you really need all of those pieces to come into play.

首先，AI得具备相应的能力。

You need AI to have the capabilities.

其次，得能为它提供做出决策所需的所有信息。

You need to be able to provide it all of the information it needs to make those decisions.

在很多情况下，这并不容易。

That's not an easy thing in many cases.

而且你还要明白，只有当这些措施在经济上具有吸引力时，才能真正产生效果。

And then you need to know that once you do all those things, it would be economically attractive in order to have the effect.

所以对很多人来说，实际的情况是：我们会关注AI的能力，发现它们进步飞快，然后惊叹——AI居然能做这么多事。

So what that in practice means for lots of people thinking about this process is that we're going to look at capabilities and they're going to improve very fast and we're going to say, my goodness, AI can do a lot.

但接下来，实际推广的过程却要慢得多。

But then there's going to be this adoption process that is going to take a much longer time.

这意味着大企业会比小企业更早实现自动化。

And that means that large businesses are gonna get automated before small businesses.

也就是说，那些更重要、更值得用AI来做的任务会优先被完成，而大量次要的、可能耗时很长或根本不会实现的任务，则会被搁置。

It means that there are things that are more important and more attractive to be using AI for are gonna get done before a very long tail of things that probably will take a long time or not happen a lot.

这其实和我们过去经历的几次技术浪潮没什么不同。

And this actually is not that different than what we've seen in previous waves.

上世纪八十年代的自动化浪潮，关注的是那些重复性高、可以被设计成流水线任务的工作，看看哪些能交给计算机来完成。

The previous waves of automation that we had in say the eighties looked at automation as what can you do that's routine tasks that you could imagine building into a pipeline of say what a computer can do.

当时我们觉得有很多任务都可以想象得出来能做。

And there were quite a number of tasks that we said, oh, we could imagine doing those.

但事实上，只有其中一小部分真正实现了自动化。

But in fact, only a fraction of those have been automated.

所以对我们来说，关键问题是这种采用速度会有多快，而这个问题并没有简单的答案。

And so the question for us, and we don't have an easy answer to this, but it's how fast that adoption pattern will happen.

但毫无疑问，这个过程会比人工智能能力的增长慢得多。

But it certainly is gonna take a lot longer than the growth of AI capabilities.

尼尔，你经常从专家任务和非专家任务的角度来谈这个问题。

Neil, you often talk about this in terms of expert versus inexpert tasks.

为什么这个区别在这里这么重要？

Why is that distinction important here?

确实如此。

Yeah.

这一点非常关键，因为在我和大卫·奥特的一些合作中，我们发现大多数工作并不是所有任务都会被自动化。

So this is very important because in some of my collaborations with David Otter, one of the things we see is that most jobs, it is not that all of the tasks in that job are going to be automated.

大多数情况下，我们会看到工作被部分自动化。

In most cases, we're going to see partial automation of jobs.

那接下来的问题就是，如果你的工作被部分自动化了，那你怎么办？

And then the question is if your job is partially automated, what happens to you?

我们通常从需求端来理解这个问题，意思是，如果我30%的工作被自动化了，可能就会让对我的需求减少30%。

And our intuition on this is often on the demand side by which I mean that if we think of if 30% of my job got automated, that might decrease the demand for my job by 30%.

当我们这么想的时候，就会觉得价格应该下降，工作量也应该减少。

And when we think about that, we think of prices should go down and quantity should go down.

也就是说，我的工资会变少，做这份工作的人也会变少。

So that means I should get paid less and there should be fewer people doing my job.

但我认为更准确的视角是从供给端来看：如果有人自动化了你工作的一部分，你到底会怎样，关键要看被自动化的具体是什么任务。

I think a better intuition for thinking about this is on the supply side of things, which says, if somebody automates part of my job, what happens to me really depends on what the task is that gets automated.

比如说，帮我填报销单，对吧？

So if you say filing my expense reports, right?

这属于我工作中非常非专业的一部分。

That is a very inexpert part of my job.

我很乐意让别人来接手这部分工作，这样我就能把更多时间花在真正体现我价值的事情上。

I'm pretty happy for someone else to take that and focus more of my time on the stuff that really makes me valuable.

所以，直觉上来说，这种安排对我来说更有吸引力。

So, intuitively, we can say that sounds more attractive to me.

但如果有个系统接管了我工作中最核心的部分，而我反而被留下来做更多报销这类琐事，那这种安排就没什么吸引力了。

Whereas if you have a system that comes in and does the most expert part of my job and I'm left doing more of my expense reports and stuff like that, that doesn't sound like it's an attractive deal to me.

我们其实可以拿过去三四十年发生的事来类比这种情况。

And so we can actually analogize this to what has been happening over the last thirty or forty years.

比如出租车司机。

For example, taxi drivers.

当GPS出现后，它自动化了出租车司机工作中最专业的一部分——也就是熟悉城市里所有路线。

So when GPS comes in, it automates the most expert part of what a taxi driver does, which is knowing all of the routes around the city.

结果就是，现在有更多人能胜任开出租车这份工作了。

That means that what actually happens there is now all of a sudden, many more people can do taxi driving.

竞争一多，工资自然就降下来了。

There's a lot more competition that drives wages down.

所以工资确实下降了。

So wages do indeed go down.

但事实上，现在出租车司机的数量比以往多得多。

But in fact, there were many, many more taxi drivers now than there ever were.

他们干脆管这些司机叫搬家司机。

They would just call them mover drivers.

反过来，想想校对工作，以前我们还没用拼写检查功能的时候，校对员得自己检查拼写，对吧？

Conversely, if you think about proofreading, proofreaders used to do spell checking before we had spell check, right?

那其实并不是他们工作中最核心的部分。

And that was not very expert part of their job.

现在当然全都被微软Word这些工具取代了，拼写检查都自动帮你做了。

That of course has been completely taken away by Microsoft Word and all of those others doing the spell checking for you.

但留下来的校对工作，反而是更复杂的内容，比如怎么组织论点？

But what's left in the proofreading job is the much more complicated, like, how do you think about structuring an argument?

你有没有把证据合理地组织起来？

Have you marshal evidence in the right way?

所以很多人其实都能检查拼写。

So lots of people could check spelling.

但真能把其他部分做好的人没几个。

Not that many people are really good at the other part.

于是随着一部分工作被自动化，能做这行的人变少了，但留下来的人反而挣得更多了。

And so as part of things got automated, there were fewer people who could do it, but the people who did do it actually got paid more.

这就把我们推向了两个完全不同的方向。

And so you can see this pushes us in two different directions.

如果你最不擅长的部分被自动化了，你反而变得更专业，工资也会上涨，但做这行的人会变少。

If your least expert stuff gets automated, you become more expert, your wages go up, but there are fewer of you.

如果你最擅长的部分被自动化了，工资反而会下降，但会有更多人入行，结果是岗位总数通常不会减少，反而可能更多。

If your most expert stuff gets automated, that actually pushes your wages down, but actually more people enter, and that means that there are typically more jobs, not less jobs.

我觉得人工智能也会让我们面临同样的情况。

I think that we will face the same thing with AI.

有时候它会自动化那些高阶的工作，有时候又会取代那些基础的工作。

It will, in some cases, the more expert, in some cases, automate the less expert.

所以，人工智能对劳动力的影响将会是多种多样的。

So that's going to mean that they're going be a diverse set of labor implications for AI.

这一点对政府和企业来说至关重要，因为如果他们只想着一种统一的模式，认为所有人都会受损，那就错了。

And this is actually a very, very important thing that governments and businesses need to think about, because it means that if they're just planning for a uniform, everybody has a bad outcome.

对吧？

Right?

实际情况要复杂得多。

It's actually a much more nuanced thing.

不过，尼尔，我想问个更大的问题：这一切最终会对整体就业数量产生什么影响？

But, Neil, let me just ask the bigger question, which is what does all of this mean for how AI will ultimately impact the aggregate number of jobs?

我觉得这是个关键问题，但如果我们只从这个宏观层面来看，就会忽略一些重要因素。

So I think it's an important question, but it's a question that if we think about it at that level, you have a couple of things.

当一项工作被部分自动化时，就会出现专家技能效应。

So you have this expertise effect as you have a partial automation of a job.

正如我所说，这并不会明显把我们推向某一个方向。

And that, as I say, does not push us particularly in one direction or the other.

但从最宏观的角度来看，真正关键的是，肯定会有一些工作被大规模自动化。

But at the very aggregate level, what really matters is there will be presumably some jobs that will be largely automated.

这些工作在某种程度上就会逐渐消失。

And so those will actually disappear in some sense.

但与此同时，新的工作和新任务也会随之产生。

But of course, new jobs and new tasks are also going to be created.

至于消失的工作和新增的工作之间如何平衡，我们现在还说不准。

And that balance between the jobs going away and new jobs being created, we really don't know.

如果我们回顾以往的自动化浪潮，会发现总体上总会涌现出大量新工作，对吧？

If we look at previous automation waves, we see that in general, there are lots of new work that comes about, right?

所以总的来说，过去的情况还算平稳。

So in general, this has been okay.

现在我们知道，AI处理问题的方式更接近人类，因此我们可能会对此多一分担忧。

Now we know that the way that AI processes things is more similar to humans and therefore we might be a little bit more worried about that.

但我认为现在判断这是否会引发大规模失业还为时过早，毕竟新任务的出现和人类获得的额外助力，很可能依然能让我们保有大量就业机会。

But I think it is really too early to know whether we should be expecting a lot of unemployment from this or whether the new tasks we have created and the extra leverage that humans will get will actually be very important in allowing us to still have lots of work out there.

但人工智能的能力比过去许多技术都要广泛得多。

But AI's capabilities are much broader than many past technologies.

那这是否意味着大规模的岗位替代比过去更有可能发生？

So does that make significant job placement more likely than in the past?

我觉得人工智能作为一种技术，确实比过去很多技术的覆盖范围广得多。

So I do think that AI as a technology is considerably broader than many technologies we've had in the past.

这也带来了一些更难落地的方面。

That also comes with some aspects of it that are more difficult to implement.

我是这么看的：像数据库、Excel这类传统技术，它们的适用范围其实很有限。

The way I think about this is if you think about a traditional technology like databases or Excel or something like that, it has a pretty limited scope.

但在它能覆盖的范围内，效率几乎是百分之百。

But within that scope, it performs basically at a 100% effectiveness.

你从来不用担心Excel算错数字，或者数据库漏掉三分之一的数据之类的问题。

You never worry about Excel multiplying numbers wrong or your database missing a third of the records or something like that.

只要你输入正确的查询，它给出的答案就一定是正确的。

If you put in the right query, it's going to give you the right answer.

所以它范围小，但特别高效。

So it's narrow, but very effective.

AI的范围要广得多。

AI is much, much broader.

你可以问它乘法题，也可以问今晚吃什么。

You can ask it a multiplication question or you can ask what to have for dinner tonight.

它对这两个问题都能给出回答。

It will give you answers to both of those questions.

但它能百分百准确回答的能力就差多了。

But its ability to get something a 100% correct is much more limited.

对吧？

Right?

要完全杜绝它出现幻觉或出错的情况，实在太难了。

It's much more harder to stop there from ever being a case of hallucination ever going off the rails.

这意味着它很适合作为辅助工具，但很难把它当成一个可以完全放任不管的模块化组件。

That means that it's very apt to be used as a tool that can help people, but it's harder to use it as a tool where you can have a modular part of your process that you can just forget about.

所以没错，它的范围更广，但也有一些其他挑战。

So yes, it's broader, but there are also some other aspects that are challenging for it.

正是这种广泛性，也让它在帮人做事方面变得更强大。

And that same thing that makes it very broad also makes it much more powerful for being a tool to do stuff.

当然，得记住这些影响发生在好几个不同的层面。

And of course, it's important to remember this happens at several different levels.

比如，作为一个员工，有人给了你这个工具，你干活就更快了。

So it happens at the level of I'm a worker, someone gives me this tool, I now do my job a little bit faster.

再比如，你公司里原本有五个人，其中两个岗位没了，但剩下的人都被增强了。

But even if you say, okay, in my organization, maybe there used to be five people and two of the roles disappear, but that augments the rest of us.

这样一来，我们反而会招更多人，业务也更快地扩张之类。

And that means that we now hire more people and business gets bigger, faster or something like that.

所以这里头的影响多得数不清。

And so there are lots and lots of different effects here.

因此我觉得，说它只会消灭工作、不会创造新工作，这种说法得打个问号。

And so I think we should be very skeptical about saying that it's gonna destroy work and not create work along the way.

所以，关于AI会导致工作大规模消失的担忧，真的有道理，还是被夸大了？

So are the worries that AI will lead to a job apocalypse as we've been hearing, warranted or overblown?

我认为人们关注AI能力的快速发展是合理的，这确实对劳动力构成了潜在挑战。

So I think that people are right to look at the AI capabilities evolving and to say this does present a potential challenge to labor.

但我们最近的一篇论文里，用‘巨浪’和‘涨潮’来比喻AI对劳动者的影响。

But one of our recent papers, we talked about the difference between crashing waves and rising tides as to how it affects human workers.

这个比喻很重要，因为如果你想象巨浪，就像整个职场的人都在海边散步，天气晴朗，大家正享受着温暖的阳光。

I think this is important because if we think about crashing waves, you can think of this as like everybody in the workforce is walking along the seashore and we're all like, oh, it's a beautiful day.

身上一点都没湿。

We're not wet.

一切都安好无恙。

We're just warm and sunny.

突然间，一个巨浪打过来，不少人瞬间就被冲走了。

And then a wave comes out of nowhere and a bunch of people just get totally washed away.

在这种情况下，劳动者当然会感到非常焦虑。

That's a world where it's pretty anxiety producing for workers.

我们的研究发现，这种情况并不是主要的影响方式。

What we see in our research is that does not seem to be the dominant way that comes in.

主要的影响更像是潮水上涨，也就是说，我们还在海边。

The dominant effect seems to be rising tides by which we say, like, okay.

我们中有些人站在沙滩上，

We're still on the seashore, and some of us are on the sand.

有些人水刚到脚踝，有些人水已经到膝盖了。

Some of us are up to the ankles, some of us are up to the knees.

但随着潮水上涨，你会发现，

But as the tide comes in, you say, okay.

水深了一点。

Now it's a little deeper.

水又深了一点。

It's a little deeper.

所以，人工智能确实来了，但并没有那么出人意料。

So, like, AI is coming, but it's not totally unexpected.

所以，只要我们留心，就能对人工智能将带来什么有清晰的预判。

So at least if we're paying attention, we can have a good sense of what AI is going to do.

这并不能让我们避开迅猛上涨的潮水，但至少能让我们提前看到趋势，不至于措手不及，也能更好地应对这个过程。

That doesn't protect us from a fast rising tide, but it does mean that we can see it coming and it won't be as big of a shock and we can manage that process better.

我认为，这其实是个积极的信号——企业和劳动者可以主动观察人工智能的能力，并以更积极的方式去引导这一变化，而不是被动等待它像海啸一样袭来。

And so I think that, to me, is an encouraging sign of businesses and workers can look at what AI can do, can try and manage that process in a way that is much more active than if it was a crashing wave scenario.

麻省理工学院的达隆·阿西莫格鲁看法略有不同。

MIT's Daron Acemoglu sees it a bit differently.

他预计，在未来几年里，人工智能对劳动力的整体影响将是轻微负面的，但他警告说，如果人工智能的投资继续偏向于取代人力而非增强人力，长期来看失业问题可能会更严重。

He expects AI to have a small net negative impact on labor over the next several years, but he warns that job losses could be larger over the longer term if AI investment continues to focus more on replacing workers than on complementing them.

这是我最近和他的一段对话。

Here's some of my recent conversation with him.

达隆，我先问一下，经济学家们的共识是，目前整体劳动力市场的数据还没有显示出人工智能带来明显影响。

Daron, let me first ask, the consensus among economists seems to be that the aggregate labor market data isn't indicating a significant labor market impact from AI yet.

那么，你认为更大的影响会在短期内被掩盖吗？

So do you expect a larger impact to be invisible in the near future?

要做出任何有把握的预测都非常困难，但我估计到2027年，AI能部分替代任务的岗位可能会出现更多裁员或招聘放缓的情况。

It's very, very difficult to make any kind of predictions with any degree of certainty, but I would imagine that in 2027, we would see a little bit more of layoffs or slowdown in hiring in jobs where AI can at least have a chance of replacing some tasks.

我觉得目前的模型和能力还远没达到能有效辅助员工的水平。

I do not think that we are currently seeing models and capabilities that are that good at complementing workers yet.

很多员工确实在用AI做些小事，比如检查文字、核对参考资料，这没问题，但这些远不是AI真正重要的辅助用途。

I think a lot of workers are using AI for small things like checking text, some reference checks, and that's fine, but that's not like the really big complementary uses of AI.

所以除了像生物研究或化学研究这类少数领域，我不认为AI会在短期内大规模地以辅助方式广泛应用。

So I wouldn't expect that except in a few fields like, say, biological research or chemical research, I don't think we're going to see huge uses of AI in a complementary way that quickly.

因此，我预计未来五年内会有一些净岗位流失，虽然规模有限，但确实是净减少。

So I would expect some net job losses, limited but net job losses within the next five years.

但我想强调的是，以我的看法，这些影响的规模根本无法和一些人所预期的相提并论。

But I want to also underscore that none of this in my estimation will be of a scale anywhere close to the kinds of things that some expect.

我觉得不会超过百分之二到四。

I would say less than two to 4%.

让我对AI的普及持谨慎态度的原因是，目前还没有基于基础模型、易于使用且能被大量企业快速采纳的应用。

What makes me cautious on the spread of AI is that we don't have easy to use applications based on the foundation models that can be adopted by many large scale employers.

要让这些岗位真正用上AI，光靠员工或经理自己动手搭工具是不够的，那样太不稳定、太耗时，而且团队对AI的理解也参差不齐，结果不可靠，这种情形发生的可能性其实很低。

I think you would need more reliable applications built on the foundation layer for these jobs rather than individuals or managers themselves propped engineering, which would be inconsistent, time consuming, often challenged knowledge of teams to use AI, unreliable, all of these things would make that not as likely a scenario.

所以我们真正需要的是现成的AI应用，但目前这类应用还根本看不到。

So then we would really need to rely on these applications, and we're not seeing those applications yet.

编程和软件工程是个例外，一方面，现在的模型在编程方面已经相当厉害了，因为很多编程任务已经高度标准化；另一方面，搞软件工程的人本身对AI就很熟悉，知道怎么写好提示词，也能检查和调试结果。

Coding, software engineering is an exception because first, the models are already quite capable in coding because parts of coding have now been quite routinized, and second, the people in the software engineering space are all quite experts in AI, so they can give the right prompts and then troubleshoot and check the work.

所以我觉得编程领域可能会率先看到一些影响，即使没有那种稳定易用的AI工具，但话说回来，这里面的不确定性依然很大。

So that's why I think coding may be an exception in that some impact can be seen without these kinds of reliable applications, easy to use applications, but again, there's a lot of uncertainty.

智能代理型AI确实为开发这类应用打开了大门，但问题是，如果每家公司都得自己从头开发，那这种进展很可能既不普遍，也不高效。

Agentic AI opens the way to develop more of these applications, but once again, I don't know that it's that likely or that productive if every company has to develop those applications themselves.

真正有潜力的突破，是AI模型或应用的开发者能利用这些技术，推出可靠、灵活、用户友好的工具，让其他企业直接拿来用。

So the agentic advances would be most useful if AI model developers or AI application developers could use those to offer to the market reliable, flexible, easy to use tools that other companies can then adopt.

就像AI界的微软办公套件那样。

So like the Microsoft Office version of AI, so to speak.

哪类工作者最容易受影响？

What type of worker is most vulnerable?

目前来看，最容易受到冲击的工作还是那些认知性高但流程化、重复性强的任务，也就是做类似的事情，没什么新意，不需要太多创新或创造，也不需要频繁的社交互动或复杂判断。

At the moment, I see still the most vulnerable tasks to be those that are cognitive and routine, meaning that involve similar things and not too many new things, not too much innovation, not too much creation, and not too much intense social interaction or judgment.

比如客服人员或者后台文职工作，就属于这一类。

So those would be tasks like customer service reps or back office work.

做这类工作的人非常多。

There are a lot of workers who do that.

我觉得加起来的话，美国大概有八百万到九百万人从事这些岗位。

I think in total, if you add those two, it will be 8,000,000 in The United States, 9,000,000.

这个数字不算小，但也不算特别庞大。

So that's not a small amount, but also not huge.

不过，从中期到长期来看，AI对劳动力市场的影响会不会发生变化？

But does that estimate of AI's labor market impact shift over the medium to longer term?

未来十年到十五年的情况就非常不确定了。

It's much, much more uncertain over, let's say, ten to fifteen years.

这最终要看投资方向往哪儿走。

It will depend on where the investments go.

我过去十多年一直主张，互补路径其实非常有成效。

I have argued for more than ten years now that the complementary path is actually quite productive.

只是我们还没在这方面投入资源。

It's just that we haven't invested in it.

这条路需要很多前提条件，涉及多种不同的投资，无论是预训练阶段还是应用层面。

It has a lot of preconditions, it has a lot of different investments that it requires, either at the pre training level or the application level.

它需要完全不同的数据——质量要求极高，而且必须高度聚焦于特定领域。

It requires very different kind of data, very high quality data, but much more domain specific.

我认为我们目前的投入远远不够，如果继续这样下去，未来十年到十五年，净失业人数会大幅增加。

I believe we are not making those investments sufficiently, so I would expect bigger net job losses within the next ten to fifteen years if things continue like this.

还有很多巨大的不确定性因素。

There are also so many big wild cards.

最大的不确定性，就是人工智能和机器人技术的融合。

The big, big, big wild card is the integration of AI and robotics.

这方面的工作，国外比美国更积极，但美国也在推进，如果真能取得重大突破，人工智能将对大量工作岗位产生深远影响。

There are efforts on this much more outside of The US than The US, but also in The United States, and if there were amazing breakthroughs there, that would open up the number of jobs that AI would impact hugely.

在美国经济中，体力劳动约占工作总量的一半。

Physical tasks is about 50% of the work in The US economy.

接下来是需要判断力的工作，比如中层管理者。

The next layer is jobs that involve judgment, middle managers.

在这方面，自主型AI的进步和实际应用将变得至关重要。

That's where the agentic advances are going to be very important and the applications are going to be very important.

这一点还很不确定，但未来一两年内我们可能会获得更多线索。

That is very uncertain, but we may get more information on that in the next year or so.

再接下来是涉及人际互动的工作，要让这类岗位受到AI影响，需要两个关键变化。

And then the next big chunk is jobs that involve social interactions, where you need two kinds of changes for those to be in the crosshairs.

第一，AI模型在社交能力上必须大幅提升。

One is the models need to improve in the social dimension.

目前它们在某些方面已经不算太差，但仍然需要显著进步。

They're already not that bad in some of those, but they would still need quite significant improvements.

同时，用户也得改变偏好，接受AI机器人参与社交互动，把它当成正常现象。

But also the human consumers need to change their preferences and what they see as normal, that they accept social interaction from AI bots more than they do.

新一代对这些问题的态度更开放，我觉得，但这种变化会有多快，我就说不准了。

And the new generation is more open, I think, on some of these issues, but how quickly that will go, I don't know.

不过让我稍微跳出来看一眼。

But let me zoom out for a second.

自1940年以来，绝大多数就业增长其实都来自技术进步和创造性破坏的过程。

The vast majority of job growth since 1940 has been driven by technology as well as the creative destruction process.

所以你觉得这次会不一样吗？

So do you think this time will be different?

经济学里并没有一条普遍规律说，就业创造必须跟就业破坏完全同步。

There's no general law of economics that says that job creation always has to match job destruction.

回顾过去八十年，很多新增岗位其实来自任务的转变、新任务的出现，以及职业结构的调整，但这种变化从来不是匀速的。

If you look at the last eighty years, a lot of job growth has come from changing tasks, new tasks, changing structure of occupations, but it has not been at an even pace.

自1980年以来，我们为没有大学学历的劳动者创造的岗位，远不如1980年之前那么多。

We have not generated as many jobs for workers without a college degree since 1980 as we used to before 1980.

这就是为什么，尤其是那些没有大学学历的男性，他们的就业人口比率大幅下滑，工资也长期停滞不前。

And that's why if you look at the employment to population ratios of especially men without a college degree, they have fallen quite a lot, and wages have fallen and stagnated for workers of that sort.

在某些时期，比如五十年代、六十年代和七十年代初，创造的岗位多于消失的岗位，带动了强劲的需求，所以那段时间工资增长甚至超过了生产率。但从七十年代末开始，岗位创造就一直跟不上岗位消失的速度，中间虽然有几次短暂的例外。

And during certain periods, like the 50s, 60s, early 70s, creation exceeded destruction and created a lot of demand, so that's why wages increased even faster than productivity during that period, but it has fallen short of destruction since the late 1970s, with some periods of exception in between.

如果我们只是重复从七十年代以来的模式，只不过这次技术冲击主要来自认知类办公室工作，那已经足以造成我所说的那种有限的岗位流失。

If we just were to repeat the period since the 1970s, but now with the destruction coming from cognitive office jobs, that would already be, I think, the kinds of limited job losses that I'm talking about.

我并不是说这次有什么根本性的不同，但每一次周期都各有特点，因为岗位创造与消失、自动化与新任务、职业结构变化之间的平衡，在每个阶段都不一样。

So I'm not saying that there is anything completely different this time, but every episode is different because those balance between creation and destruction, automation versus new tasks and changing occupations, those are different in every sub period.

那么，达隆，你认为人工智能带来的这些变化，会对工资和收入差距产生什么影响？现在这可是大家最关注的问题。

And so, Daron, what do you think the impact of these shifts from AI will be on wages and income inequality, which is very much in focus right now?

首先，我觉得收入差距和就业状况之间的关联，其实比人们通常认为的要紧密得多。

First of all, I think that inequality and employment are actually more tightly linked than sometimes is implied.

如果某个群体的工资出现下降或停滞，他们通常也会降低就业率或劳动参与率。

If you experience declining or stagnant wages for some groups, they typically also reduce their employment population ratio or the participation rate.

所以我相信，有些群体不仅会面临工资下降，就业增长也会放缓，甚至出现岗位减少。

That's why I believe that some groups are going to suffer in terms of their wages, as well as seeing somewhat slower employment growth or some employment declines.

正因如此，我才认为，劳动收入的不平等很可能会进一步加剧。

That, therefore, is the basis of my belief that we should expect increase in labor income inequality.

不过这里有个重要的例外情况：如果被取代的岗位原本是由管理者或高薪员工担任的，那情况就会不一样。现在的情况不同于上世纪八九十年代到两千年初的技术变革，那时候取代的是蓝领工人做的工作，导致中等收入岗位被低薪职位取代，从而加剧了不平等。

Now, is one caveat to that, an important caveat, which is that if the jobs that are replaced were done by managers or already highly paid workers, that would work out differently, that now you're not replacing the jobs that blue collar workers used to perform like the technologies of the 1980s, 1990s, 2000s, which then increased inequality because those middle class type of wages were replaced by lower wages that many of these workers could get only in lower ranked occupations.

如果你开始用AI取代大公司里那些高薪的管理者，贫富差距反而可能缩小。

If somehow you started replacing managers in large companies who are well paid, inequality could decline.

但我认为这不太可能发生。根据我前面说的，最容易被替代的是那些结构简单、可预测性高的工作，比如客服、后台办公，这些岗位的员工通常并不高薪。而且，真正高薪的人就算丢了工作，也能轻松转到其他领域，真正承受冲击的，是下一层次被挤出去的人。

I don't think that's the most likely scenario because my earlier account, it's the simpler, cognitively more predictable jobs, customer service, back office, those are not the highly paid workers, and moreover, very well paid employees would go and find jobs in other occupations, so they wouldn't be the ones that bear the burden as much as then the next layer who are then displaced.

所以，这就是我为什么相信劳动收入的不平等会加剧的原因。

So that's the basis of my belief that labor income inequality would increase.

说实话，听完这些对话，我也不确定自己是更担心了，还是更安心了。

Honestly, I'm not sure if I came away from these conversations more concerned or more comforted.

我们还有很多未知的东西，不过今天就先谈到这儿吧。

There's a lot that we still don't know, but I'll leave it there for now.

感谢达隆·阿西莫格鲁、尼尔·汤普森和约瑟夫·布里格斯的参与。

My thanks to Daron Acemoglu, Neil Thompson, and Joseph Briggs.

也感谢您收听高盛交流会这一期节目，本节目录制于2026年6月。

And thank you for listening to this episode of Goldman Sachs exchanges, which was recorded in June 2026.

我是艾莉森·纳森。

I'm Allison Nathan.

本文所表达的观点和看法截至发布日期，可能随时变更，且不一定代表高盛集团或其关联机构的立场。

The opinions and views expressed herein are as of the date of publication subject to change without notice and may not necessarily reflect the institutional views of Goldman Sachs or its affiliates.

本材料仅供信息参考，不构成任何投资建议，也不代表高盛任何实体推荐采取任何特定行动，或构成购买或出售任何证券或金融产品的要约或邀请。

The material provided is intended for informational purposes only and does not constitute investment advice, a recommendation from any Goldman Sachs entity to take any particular action, or an offer or solicitation to purchase or sell any securities or financial products.

本材料可能包含前瞻性陈述。

This material may contain forward looking statements.

过往表现不代表未来结果。

Past performance is not indicative of future results.

高盛及其关联机构不对本材料中陈述或信息的准确性或完整性作任何明示或暗示的保证，并对任何基于此类信息所作的决策不承担任何责任。

Neither Goldman Sachs nor any of its affiliates make any representations or warranties expressed or implied as to the accuracy or completeness of the statements or information contained herein and disclaim any liability whatsoever for reliance on such information for any purpose.

文中提及的第三方机构名称均为其所属公司的财产，此处仅用于信息说明和识别目的，不代表这些公司与高盛之间存在任何所有权或授权关系。

Each name of a third party organization mentioned is the property of the company to which it relates, is used here strictly for informational and identification purposes only and is not used to imply any ownership or license rights between any such company and Goldman Sachs.

为方便起见，此处提供文字稿，内容可能与原始视频或音频有所出入。

A transcript is provided for convenience and may differ from the original video or audio content.

高盛不对本 transcript 中的任何错误承担责任。

Goldman Sachs is not responsible for any errors in the transcript.

未经高盛明确书面同意，任何接收方不得复制、分发、发布、全文或部分重现，或向他人披露本材料。

This material should not be copied, distributed, published, reproduced in whole or in part or disclosed by any recipient to any other person without the express written consent of Goldman Sachs.

如本材料提及了相关发行方，其研究适用的披露信息可通过您的高盛代表获取，或访问 www.gs.com/research/hedge 查看。

Disclosures applicable to research with respect to issuers, if any, mentioned herein are available through your Goldman Sachs representative or at w w w dot gs dot com slash research slash hedge.

HTML。

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高盛不支持任何候选人或任何政党。

Goldman Sachs does not endorse any candidate or any political party.

版权所有 2026 年。

Copyright 2026.

高盛。

Goldman Sachs.

保留所有权利。

All rights reserved.