AI 现实核查：AI 是否正在窃取初级岗位？

去年夏天，《华尔街日报》发表了一篇文章，标题令人担忧：人工智能正在破坏本已脆弱的大学毕业生就业市场。

Last summer, The Wall Street Journal published an article with an alarming headline: AI is wrecking an already fragile job market for college graduates.

文章接着指出，企业长期以来一直依赖初级员工来做那些繁琐的工作，同时作为在职培训的一部分。

It then goes on to say, Companies have long leaned on entry level workers to do grunt work that doubles as on the duty training.

现在，ChatGPT和其他机器人可以完成许多这类任务。

Now ChatGPT and other bots can do many of these chores.

如今，年轻人找工作特别困难，而这一部分原因在于人工智能，这种观点迅速流行起来，成为了一种共识。

Now this idea that young people are having a particularly hard time finding jobs and that this is due in part to AI soon took off and became conventional wisdom.

自那以后，这种说法的各种变体一直被引用。

Variations of this claim have been cited ever since.

不过，我要说，这种信念已经开始产生实际影响。

Now, look, this belief is starting to have a real impact.

就在上周，《Axios》发表了一篇题为《人工智能正促使大学生更换专业》的文章，文中引用了一项调查，显示有16%的在读大学生因担心人工智能而改变了专业。

Just last week, Axios wrote an article that was titled AI is Making College Students Change Majors, and it cited a survey that showed sixteen percent of currently enrolled college students have changed their studies due to concerns about AI.

当你只看学习技术相关专业的学生时，这一比例上升到了25%，也就是说，如果你最近关注过人工智能的报道，你很可能已经多次遇到过这类说法。

That number jumps to twenty five percent when you consider students who are studying technology, which is all to say, if you've been reading AI coverage recently, you've probably encountered these type of claims many times.

但这些说法是真的吗？

But are they true?

今天，我们要寻找一些更为严谨的答案。

Today, we're going to look for some measured answers.

我是卡尔·纽波特，欢迎收看《AI现实检验》。

I'm Cal Newport, and this is the AI Reality Check.

好的。

All right.

那么我们今天要探讨的问题是：AI是否在减少初级岗位的市场需求？

So the question we're looking at today is AI reducing the market for entry level jobs?

我不是经济学家，但幸运的是，最近有多位经济学家对‘AI窃取初级岗位’这一说法发表了看法。

Now I'm not an economist, but fortunately for our purposes, multiple economists have weighed in recently on this claim about AI stealing entry level jobs.

关键是，他们对此并不以为然。

And here's the thing, they're not that impressed.

好的。

All right.

我想先谈谈一位名叫托尔斯滕·洛克的人，他是阿波罗全球管理公司的首席经济学家。

I want to start with someone named Torsten Slok, who is the Chief Economist at Apollo Global Management.

上周，他发布了一份名为《破除AI导致青年失业的迷思》的通讯。

Now, last week, he published a newsletter that was titled Busting the AI Youth Unemployment Myth.

好的，这篇文章中，他整理了两张图表，都使用了美国劳工统计局的数据。

All right, now in this article, he has two different charts that he put together, both of them drawing data from the Bureau of Labor Statistics.

我会把第一张图表展示在屏幕上。

I'll put the first chart up here on the screen.

好的，这张图显示了从20世纪90年代中期至今的失业率，包含两条线：一条是16岁及以上所有人的失业率，另一条是20至24岁人群的失业率。

Okay, so this is looking at the unemployment rate from the mid 1990s until today, and it shows two lines: one for all people 16 years and older, and others for just twenty to twenty four year olds.

所以他想探讨的是：年轻人目前是否在失业问题上特别困难？

So he's looking at are young people having a particularly hard time with unemployment right now?

而你看到的是，答案是否定的，整体失业率和年轻人的失业率趋势基本一致。

And what you see is, well, no, the overall unemployment rate and the unemployment rate for young people seems to be moving roughly with the same trends.

好的，这是洛克对这张图表的总结。

All right, here's how Slok summarizes this chart.

数据显示，年轻工人的失业率并未因人工智能而出现结构性上升。

The data does not show any sign that unemployment stronger, that unemployment among younger workers is structurally higher because of AI.

好的，现在你可能会听到一种常见的批评，那就是我们得看具体是哪类年轻人，对吧？

Okay, now a common critique that you might hear here is that it depends what type of young people we're talking about, right?

真正应该担心工作被人工智能取代的，其实是那些拥有大学学位的年轻人，因为人工智能自动化更针对白领工作，而非非白领工作。

It's really young people with college degrees that should really be seeing their their jobs being stolen by AI because AI automation is more aimed at white collar jobs than non white collar jobs.

因此，考虑到这一批评，我们可以转向Slok所分析的第二张图表，我会把它显示在屏幕上，这张图展示的是22至27岁美国大学毕业生的失业率。

So with this critique in mind, we can bring ourselves to the second chart that Slok looks at here, which I'll bring up on the screen, which is looking at the unemployment rate among US college graduates who are between the ages of 22 and 27.

图表按性别分列，分为女性和男性。

It's broken out by gender, female and male.

当你放大观察时会发现，当前时期右端的失业率平均来看，并没有比以往其他时期显著更高。

And what you see when you zoom out here is that the unemployment off to the far right for our current period on average really is not that much different than other times we've seen.

因此，最近的情况与过去经济周期中的高峰和低谷相比，并没有明显差异。

So there's not necessarily a major difference here between what we've been seeing recently and what we've seen in other economic upturns and downturns in time past.

但接下来，Slok对这张图的总结是。

But now here's how Slok summarizes this.

男性的失业率有所上升，但最近已趋近于女性的失业率。

The unemployment rate has increased for men, but it has recently converged towards the unemployment rate for women.

自ChatGPT发布以来，女性的失业率一直在下降，但最近又略有上升。

For women, since ChatGPT was released, the unemployment rate has been moving lower, but then more recently it has increased slightly again.

所以这些数据有点奇怪和混乱，但如果AI正在迅速自动化初级大学毕业生的工作（包括男性和女性），你并不会看到这样的情况。

So this is kind of weird and messy data, but it's not not at all what you'd expect to see if AI was beginning to rapidly automate entry level college worker jobs, which would be both men and women.

你可能会看到年轻大学毕业生的失业率急剧上升。

You would probably see a very rapid rise in unemployment among young college graduates.

但你看到的并不是这样。

It's not what you're seeing.

Slok给这张图表起了一个简单的标题：‘没有迹象表明AI对22至27岁美国大学毕业生的失业率产生特定影响’。

Slok gave this chart a simple title, No signs of AI Having a Particular Impact on the Unemployment Rate Among US College Graduates Age 22 to 27.

好吧，我们这里有一些令人信服的数据，或许AI并没有在抢走这些工作。

All right, so that's some compelling data we have here that maybe AI is not not stealing these jobs.

但这本身还不能成为确凿的结论。

But it's not a slam dunk case by itself.

为什么会这样？

And why is this?

这是因为Slok并没有将大学毕业生与非大学毕业生进行比较。

Well, because Slok is not comparing college graduates to non college graduates.

现在，如果你更仔细地观察这些主张，这些AI替代理论的支持者通常谈论的并不是拥有大学学位的年轻人的整体失业率——正如我们刚才所见，这个数据虽然有些波动，但与其他历史时期相比并不异常。

Now, if you look closer at more of these claims, these these proponents of AI displacement theory, what they often talk about is not the overall unemployment rate for young people with college degrees, which as we just saw is like, it's moving noisily, but it's not unusual compared to other past periods.

他说，真正重要的是，相对而言，支持者们更关注的是近期大学毕业生与同龄但没有大学学位的劳动者之间的失业率差异。

He says what matters is, relatively speaking, the proponents rather would say, what matters is relatively speaking, what is going on with the unemployment rate for recent college graduates versus recent workers of the same age that don't have a college degree?

因为他们的观点是，AI的自动化现在会对大学毕业生造成比非大学毕业生更大的冲击。

Because the idea is AI, again, the automation is going to hit college graduates harder than it's going to hit non college graduates right now.

AI替代论的支持者认为，问题不在于大学毕业生的失业率是否异常，而在于它比非大学毕业生的失业率更高、上升得更快，这才是新现象。

And what proponents of AI displacement argue is that, look, the unemployment rate is not that it's unusual for college graduates, but it's higher and rising faster than it is for non college graduates, and that's new.

传统上，如果我们回顾几十年的数据，非大学毕业生的失业率一直更高，但现在我们看到一种逆转：大学毕业生的失业率反而超过了没有大学学位的同龄人。

Traditionally, non college graduates have higher unemployment rates if we look back over many decades, and now we've been we've seen an inversion where actually college graduates have their unemployment outpacing the unemployment rate of their peers who don't have a college degree.

因此，对于那些认为AI正在窃取入门级工作的人来说，这是他们最重要的数据之一。

So for the proponents of this idea that AI is stealing entry level jobs, that is one of their big pieces of data.

事实上，这个说法也是可以验证的，经济学家们已经对此进行了研究。

Now it turns out this claim is testable too, and economists have looked at it.

我通过上周发表的一篇文章了解到一些这些研究。

Now I learned about some of these studies through the following article that came out last week.

这篇文章由罗杰·卡马在《大西洋月刊》上撰写，标题为《年轻人正在落后，但并非因为人工智能》。

It's written by Roger Karma in The Atlantic called Young People Are Falling Behind But Not Because of AI.

这是一篇很好的文章，因为罗杰采访了一位名叫内森·戈德斯拉格的经济学家，他最近一直在研究这一趋势，并在一系列论文中得出了相当有启发性的结果。

This is a good article because Roger talks to an economist named Nathan Goldschlag, who has been studying this trend more recently, and in a series of papers has found some pretty informative results.

例如，在最近的一篇论文中，戈德斯拉格与亚当·奥齐梅克合著，找到了一种对拥有大学学位者与没有大学学位者之间失业率差异的替代性解释。

So for example, in a recent paper, Goldschlag, co authoring with Adam Ozemik, found an alternative explanation for differential unemployment rates between those with a college degree and those with not.

我将直接引用《大西洋月刊》这篇文章中对这些数据的总结。

I'm going to actually read from The Atlantic article here summarizing this data.

经济学家亚当·奥齐梅克和内森·戈德斯拉格最近对数据进行了更深入的分析，发现大量没有大学学位的年轻劳动者实际上已经放弃了找工作，这人为地降低了无学位年轻劳动者的失业率，从而制造出一种大学毕业生表现特别糟糕的假象。

The economist Adam Ozimek and Nathan Goldschlag recently took a deeper look at the data and found that a significant number of younger workers without college degrees had simply given up looking for a job, artificially improving unemployment rate for young workers without a degree and thereby giving the appearance that college graduates were doing uniquely poorly.

卡马在《大西洋月刊》的文章中将这种现象称为‘统计幻象’。

Karma in his Atlantic article calls this a quote unquote statistical mirage.

所以，看起来没有大学学历的人情况变好了，而有大学学历的人变糟了，但事实证明这只是一个统计幻觉。

So this, Oh, look, things got better for people without college degrees, but worse for those with, that turned out to be a statistical mirage.

实际上，是那些没有大学学历的人退出了劳动力市场，从而被排除在了这些常用统计数据之外。

It was actually people without college degrees leaving the active job market, which takes them out of standard statistics that are used here.

好吧。

All right.

那么，如果我们使用更全面的失业率指标，不剔除那些已经放弃找工作的人，而是涵盖整个群体，会发生什么？

So what happens then if you use a just overall unemployment metrics, which do not remove people who have stopped looking for a job, but includes an entire population?

如果我们只关注年轻人和整体失业率呢？

So what if we focus on young people only and overall unemployment, right?

也就是这些人中有多大比例有工作或没有工作。

Just what percentage of these people have jobs or not.

你猜猜奥泽梅克和戈德斯拉格在分析这些数据时发现了什么？没有大学学历的人实际上处境更糟。

And guess what Ozempic and Goldschlag found when we looked at that data, those without college degrees are actually doing worse.

所以实际情况恰恰与那些认为技术取代论者所说的相反，他们曾说：看，有大学学历的人处境相对更差，因为人工智能会抢走他们的工作。

So it's actually the opposite of what the displacement proponents were saying, which was like, look, things are getting proportionately worse for people with college degrees because AI could take their jobs.

不，对于没有大学学历的人来说，就业市场正在恶化。

No, it's job markets are getting worse for people without college degrees.

之前只是因为这些人完全退出了劳动力市场，才造成了统计上的错觉，让人以为这种情况并没有发生。

There was just a statistical mirage because they were dropping out of the market altogether that made it seem like that wasn't happening.

戈德斯拉格有一个简洁的总结。

The Goldschlag has a succinct summary.

这是他在《大西洋》杂志文章中的说法。

This is him in The Atlantic article.

这让我开始怀疑这是否真的是一个关于人工智能的故事。

This makes me doubt that this is an AI story.

我喜欢这种低调的表达方式。

I love this sort of low key delivery there.

但我们还没说完，因为戈德斯拉格最近还与一位名叫莎拉·艾克哈特的经济学家合著了另一篇论文，分析了不同经济部门的招聘趋势。

But we're not yet done because Goldschlag actually wrote another recent paper, this time co authored with an economist named Sarah Eckhart, where they now analyzed hiring trends in different economic sectors.

他们使用了五种不同的指标来衡量对人工智能自动化的暴露程度。

And they used five different measures of exposure to AI automation.

所以有五种不同的方法来评估某个职业受到人工智能冲击的程度。

So five different ways that people have assessed how exposed a particular job is for AI to come in and take over jobs.

他们研究了某个职业越容易被人工智能自动化，对失业和就业趋势会产生什么影响，对吧？

And they looked at what's the impact on you being more AI automatable to unemployment and employment trends in that particular sector, right?

因此，如果人工智能正在取代入门级工作，我们应该看到的是，最受人工智能影响的职业中，失业率上升、招聘减少。

So again, if AI is stealing entry level jobs, what we should see is a rise in unemployment, a decrease in hiring in the jobs that are most exposed to AI.

那么他们发现了什么？

So what did they find?

我在这里引用《大西洋月刊》文章中他们的原话。

I'm going to quote them here from The Atlantic article.

无论我们如何分析数据，都没有发现人工智能对劳动力市场产生任何显著影响。

No matter how we cut the data, we didn't see any meaningful AI impacts on the labor market.

因此，数据中没有任何迹象表明，人工智能的渗透会导致某种职业的招聘减少。

So there was no signal in there that AI exposure somehow made that particular type of job to be more likely to be hiring less.

事实上，《大西洋月刊》文章中引用的另一位经济学家——韦尔尼·塔德希——展示了相反的结果。

And in fact, another economist quoted in The Atlantic piece, this is Vernier Tadeshi, he showed the opposite.

他说，实际上，自2023年以来，那些最不受AI自动化潜在影响的专业人士的失业率反而上升了。

He said, Actually, in the period since 2023, unemployment has increased for the professionals that are least exposed to potential AI automation.

所以当前的就业市场非常混乱。

So there's a very messy job market out there.

这就是结论。

This is the conclusion.

疫情之后，就业市场变得非常混乱。

There's a very messy job market out there coming out of the pandemic.

当然，情况被搞混了。

Of course, was mixed up.

疫情是一场代际性的冲击和动荡，许多事情都被打乱了。

The pandemic was a generational displacement and disruption and lots of things got shaken up.

我们以前在这档节目中讨论过这一点。

We've talked about this on this show before.

在白领领域，疫情期间出现了大量过度招聘，尤其是在科技公司。

In the white collar world, there's a lot of over hiring during the pandemic, especially in tech related firms.

利率低得惊人，因此借钱非常容易，人们大量招聘，因为对技术解决方案，尤其是基于云的解决方案，兴趣浓厚。

Interest rates were dead low, so you could borrow money very easily, And people were hiring up a storm because there's a lot of interest in technology based solutions, like especially cloud based solutions.

所以当时招聘很多，但现在必须进行大量调整，因为他们不需要那么多人了。

So there's a lot of hiring and now there's a lot of corrections because they don't need that many people.

对这些服务的需求下降了，而利率又回升了。

Interest in those services are down and interest rates are back up.

利率回升本身就足以导致大量裁员，对吧？

Interest rates going back up alone is enough to lead to a lot of cuts, right?

因为这就像有人把你的运营成本提高了那么多。

Because it's as if someone made your operating costs that much more expensive.

所以你必须弥补这一点。

So you have to offset that.

所以这一直是个混乱的市场。

So it's been a messy market.

这影响了白领工作者。

It's affected white collar workers.

它也影响了非白领工人。

It's affected non white collar workers.

所有这些数据都表明情况很混乱。

All of this data show this is messy.

但无论我们如何分析，试图寻找AI正在减缓初级岗位招聘的具体信号，无论从哪个角度看，我们都看不到这种迹象。

But no matter how we slice it looking for a specific signal showing that AI is beginning to slow down entry level hiring, no matter how you come at it, we do not see that signal.

事实上，我们经常看到相反的迹象出现。

In fact, we often see opposite signs showing up.

好的。

All right.

所以让我在这里退一步说。

So I don't mean let me step back here.

在这种分析中，我并不是想轻视、完全怀疑或反应过度。

In this type of analysis, I don't mean to be dismissive or 100% skeptical or reactionary.

我并没有声称AI将来不会对就业市场造成重大冲击。

I'm not claiming that AI is not one day going to potentially cause big disruptions in the job market.

这完全有可能。

It very well could.

而且这些干扰确实可能从初级岗位开始，尤其是那些容易受到人工智能影响的初级岗位，导致初级招聘放缓。

And it's possible that these disruptions will in fact start with entry level jobs and entry level jobs exposed to AI slowing down entry level hiring.

这很可能就是我们未来会看到的情况。

That may very well be what we see in the future.

但目前并没有发生。

But it is not happening right now.

这之所以重要，是因为已经有很多文章、讨论和采访将这一观点当作事实来引用。

And the reason why this matters is because there's been a lot of articles and discussions and interviews where this idea is being referenced as if it was a fact.

我知道我之前已经提过这一点，但我还是要再说一遍。

Now, I know I've made this point before, but I'm going to make it again.

我认为，在很多关于人工智能的讨论和报道中，评论员们会抓住某个理论、观察或说法，并不是因为他们认为它正确——就像他们对待其他新闻那样经过核实——而是因为他们相信它大致上是对的。

I think what's going on in a lot of this discussion and coverage of AI is that commentators will latch on to a theory or an observation or a claim, not because they think it's correct, like that they've checked it and it's correct like they might with another story, but because they believe it is directionally true.

所以，也许人工智能此刻并没有真正取代初级岗位，但它是方向正确的，因为人们确实需要关注人工智能的影响。

So I don't know, maybe AI is not right this moment actually taking entry level jobs, but it's directionally true because people need to be worried about AI's impact.

所以我会上一篇文章、做一次采访，或者在网上做出一些可能目前并未发生的主张，因为我的最终目标并不是追求真相。

And so I will put an article in or I'll do an interview or I'll make a claim online about something that might not be happening right now because my ultimate goal is not to get to the truth.

我的最终目标是影响人们对这件事的看法。

My ultimate goal is to influence how people are thinking about this.

我其实更清楚。

I know better.

他们需要对这件事更加担忧，而目前的担忧程度还不够。

They need to be more worried about this than there are.

因此，我们应当对人工智能和就业问题保持警惕，这一点在大方向上是正确的。

So it's directionally true that we should worry about AI and jobs.

所以我会提出并推广任何能推动这一方向、帮助强化这种信念的主张。

So I will throw out and promote any claim that in that that moves in that direction and helps to try to give that belief.

我认为这种现象普遍存在，我也见过这种‘大方向正确’与‘事实正确’之间的区别。

I think there's a lot of that going on, and I've seen this sort of directionally true versus factually true.

这种现象在过去十年左右发生的许多重大事件中，我都曾目睹过。

This is something I've seen happen in a lot of different major things that have happened in the last decade or so.

这种转变让我作为评论员的角色变成了塑造人们如何理解和行动，而不一定是试图查明正在发生的真实情况。

This shift towards my job as a commentator is to shape how people understand and act, not necessarily to try to get to the truth of what's actually happening.

但我认为这是一个问题，对吧？

But I think this is a problem, right?

因为当你倾向于那些方向正确、感觉真实、符合你当下感受的东西，而不是努力弄清什么是真正真实的时候，会发生什么？

Because what happens when you lean into what's directionally true, what feels true, what matches the vibe that you're feeling versus actually trying to figure out what is actually true?

会有两件事发生。

Two things happen.

第一，你会削弱公众的信任。

One, you erode public trust.

当越来越多的AI评论员倾向于方向正确的内容时，人们会逐渐察觉到。

The more AI commentators lean into what's directionally true, people pick up over time.

嘿，一年前你说过这件事会发生，但并没有。

Hey, year ago you said this was going to happen, it didn't.

上个月你还那么确信这是事实，结果却发现，其实杰克·多西只是在搞AI炒作。

Last month you were so confident this was the case and it turned out that no, actually Jack Dorsey was just AI washing.

你这样做得多了，人们就不听你了。

You do that enough times, people stop listening.

而当真正需要报道的事情发生时——毕竟这些是大型公司，对社会和经济的各个领域都有巨大影响——人们已经不再关注你了。

And then when there's things that really need to be reported, because these are massive companies with huge implications on all sorts of different sectors of our society and economy, people are no longer listening to you.

所以信任是个问题。

So trust is a matter.

这很重要。

It matters.

如果你从追求准确转向追求方向上的真实性，就会开始削弱信任。

And if you go from accuracy to directional trueness, you begin to erode trust.

我认为第二点重要的是，这会让这些前沿AI公司逍遥法外。

The second thing that I think matters is that it lets these frontier AI companies get away with a lot.

我们越是倾向于追求最轰动的报道，因为这符合我们觉得这很重要的情绪，就越会让前沿AI公司更容易获得远超实际需求的资金。

The more we lean into trying to have the most bombastic coverage possible because it matches our vibe that this is a big deal, it allows the frontier AI companies to keep raising money probably way more than they need to.

它们不会受到同样的审查，因为如果你相信这是过去两个世纪最具颠覆性的技术，那我根本不关心你的息税前利润。

They're not being held to the same scrutiny because if you believe this is the most disruptive technology in the last two centuries, I don't care about your EBITDA.

我不关心你的债务。

I don't care about your debt.

我不关心你的收入。

I don't care about your revenue.

我想参与那个将取代所有工作的公司。

I want to be involved in the company that's going to replace all the jobs.

这是个大问题，因为如果这些公司实际上无法成为有史以来增长最快的企业，一旦泡沫破裂，将对美国股市造成巨大负面影响。

That's a big problem because if they're not actually if these companies aren't actually able to become the fastest growing companies in the history of companies, it's going to have a huge impact negatively on the American stock market if and when that bubble burst.

这也让他们得以逃避责任——推出粗糙的产品强加给我们，制造各种行业的问题，带来种种压力。

It also lets them get away with doing stuff, the sloppy products they're jamming down our throats, the problems they're causing in all sorts different sectors, the stresses they're causing.

他们之所以能逃避责任，是因为他们披上了一层巨大颠覆性不可避免的光环，仿佛在说：‘我们能怎么办呢？’

It lets them get away with that because it gives them an aura like they're wearing this cloak of massive disruptive inevitability like, Hey, what can we do?

这项技术，一切都在改变。

This tech, everything is changing.

我们只是尽力跟上步伐。

We're just trying to hold on.

如果不是我们，也会是别人。

If it's not us, it'd be someone else.

相反，如果我们把它们当作一家普通公司对待，你们刚发布的是什么荒唐产品？

As opposed to if we treat them like a normal company, What is this nonsense product you just released?

我为什么非得用这个？

Why do I have to use this?

你们的主张是什么？

What's your claim?

说服我这个有用。

Convince me this is useful.

你们为什么消耗这么多能源和水？

Why are you taking up all this energy and water?

你们为什么要建造这些东西？

Why are you building these things?

只要我们关注颠覆的氛围，就不会对人施加压力。

We don't hold people's feet to the fires as long as we're focused on the vibe of the disruption.

我们沉迷于这种要么反乌托邦、要么乌托邦式的变革理念，而忽视了这些是真正做事、需要问责的真实公司。

We're addicted to the idea of this either dystopian, utopian change as opposed to these are real companies doing real things that need accountability.

好吧，我现在不再说教了。

So I'll get off my soapbox now.

但我只是希望对当前我们对人工智能报道中众多线索中的这一条做出一个小小的修正。

But I just want this this minor correction of a one thread among many that are woven into our coverage of AI right now.

我想让它代表这个更大的问题。

I want it to stand in for this bigger thing.

重点不在于表面上的真相，而在于实际上的真相。

It's not about what's directly true, it's about what is actually true.

作为人工智能评论员，我们的职责不是去影响人们如何看待某件事，而是向他们提供信息，并相信他们在了解真实情况后会自己做出正确的判断。

It is not our jobs as AI commentators to influence how people think about something, it's to inform them and to trust them to think the right way once they know what's really going on.

我们必须让这些人承担责任，必须超越他们的焦虑，触及地面的真实情况。

We have to hold these people's feet to the fire, we have to get past their own anxieties and get to the on the ground truth.

好了，讲道结束。

All right, sermon over.

今天就说到这儿吧。

That's enough preaching for today.

所以记住，下次再见之前，关心AI，但别全信你读到的关于它的所有内容。

So remember, until next time, care about AI, but not everything you read about it.