Name: 人工智能導致的失業已經開始了嗎？ (Have the AI Job Losses Started?)
Uploaded: 2025-07-18T18:49:58.000Z
Duration: 41 min 11 s

過去進行式

Microsoft had already cut 6,000 jobs in May, so when the US tech company announced that it was planning to lay off a further 9,000 workers, many claimed that this was the result of AI replacing our jobs.

今年 5 月，微軟已經裁員 6000 人，是以，當這家美國科技公司宣佈計劃再裁員 9000 人時，許多人聲稱這是人工智能取代我們工作的結果。

動名詞

But it was argued that not only did this have nothing to do with AI, but that artificial intelligence is a long way off from replacing us in the workplace.

但有人認為，這不僅與人工智能無關，而且人工智能離取代我們的工作還很遙遠。

Is AI already hitting the job market, and if not, when and whose jobs will be the first to go?

人工智能是否已經衝擊就業市場，如果沒有，何時以及誰的工作會最先消失？

你好，歡迎來到 Beyond the Valley。

I'm Arjun Karpal, and I'm so excited to introduce my new co-host, Steve Kovacs.

我是阿瓊-卡帕爾，很高興向大家介紹我的新搭檔史蒂夫-科瓦奇。

史蒂夫，歡迎來到 "山谷之外"。

It's good to be working with you, and I'm really excited.

很高興能與你合作，我真的很激動。

I'm CNBC's tech correspondent over here in the United States.

I'm based in New York City, so I'm glad we're bringing a global element to Beyond the Valley.

我在紐約工作，所以我很高興我們能為 Beyond the Valley 帶來全球元素。

Steve, you've been covering tech for a long time.

史蒂夫，你長期從事科技報道。

I just hit my seven-year mark, believe it or not.

不管你信不信，我剛剛度過了我的七年大關。

And I've been covering tech pretty much my entire professional career, specifically here at CNBC.

我的整個職業生涯幾乎都在報道科技新聞，尤其是在 CNBC。

I focus on Apple, a little company maybe you've heard of, and Microsoft is my other big beat company.

我主要關注蘋果公司，也許你聽說過這家小公司，微軟是我關注的另一家大公司。

And then I also kind of dabble in some other things.

此外，我還涉足其他一些領域。

When I was kind of putting together my thesis for my coverage here on CNBC, I thought, you know, video games, people don't realize how big video games are and what a big business that is.

當我為我在 CNBC 的報道撰寫論文時，我想，你知道，視頻遊戲，人們沒有意識到視頻遊戲有多大，是多麼大的一個行業。

So I added gaming to my beat element, too.

是以，我也在我的節拍元素中加入了遊戲。

So I cover, you know, companies like Take-Two Interactive and the Xbox division over at Microsoft and all those cool companies doing interesting things in entertainment as well.

是以，我報道的公司包括 Take-Two Interactive 和微軟的 Xbox 部門，以及所有這些在娛樂領域做著有趣事情的很酷的公司。

I feel like, you know, any of our current listeners and any new listeners we pick up along the way, they're going to hear a lot about gaming.

我覺得，你知道，我們現在的聽眾和我們一路走來的新聽眾，都會聽到很多關於遊戲的內容。

I already have a Nintendo Switch 2 at home.

我家裡已經有一臺任天堂 Switch 2。

I just opened a new game yesterday, so we're on the right path.

我昨天剛開了一個新遊戲，所以我們走在正確的道路上。

And look, Steve, we're going to be talking about AI today, and I think, you know, that chat GPT moment we had a couple of years back now was such a big moment.

聽著，史蒂夫，我們今天要討論的是人工智能，我認為，你知道的，幾年前的 GPT 哈拉時刻就是一個重要時刻。

You cover, like you said, Apple, Microsoft, so many of these big companies as well.

就像你說的，蘋果、微軟等這些大公司也都是你的客戶。

What's your sense at the moment of kind of what the big themes are amongst the big tech giants when it comes to AI?

在人工智能方面，你覺得目前各大科技巨頭的主要議題是什麼？

Yeah, I've been kind of toying around with this because I focus so much on Apple and they aren't exactly at the forefront of AI.

是的，我一直在考慮這個問題，因為我太關注蘋果公司了，而他們在人工智能領域並不處於領先地位。

But what I've been kind of thinking a lot about, Arjun, is like this build versus partner or build on top of.

但是，阿瓊，我一直在思考的是，構建與合作伙伴或構建之上的關係。

So, and this is kind of emerging out of Apple, obviously, we saw this year, they were going to put out that new super version of Siri, it was going to be personal, it's going to use the data on your iPhone, it's going to be chat GPT, but with an Apple twist, right?

很明顯，今年我們看到蘋果公司打算推出新的超級版 Siri，它將是個性化的，它將使用你 iPhone 上的數據，它將是哈拉 GPT，但有蘋果的特色，對嗎？

And that was what everyone got excited about, and they failed to deliver on it.

而這正是大家所期待的，但他們卻沒有做到。

And it was the first time, probably the worst time I've seen Apple go through as far as promising a major product, and then failing to deliver.

這是我第一次，也可能是我見過的蘋果公司最糟糕的一次，因為它承諾推出一款重要產品，但卻未能兌現。

You can argue things like the air power, power charger thing, or, you know, little things like that.

你可以爭論一些事情，比如空氣動力、充電器的事情，或者，你知道的，諸如此類的小事。

But as far as something coming out and saying, we're going to take part in this AI revolution and then failing to do it.

但是，如果有人站出來說，我們要參與這場人工智能革命，卻又做不到。

And then now we hear these reports, Arjun, that they might put that to the side and partner just like they do in search with Google, they might start partnering with OpenAI or Anthropic to kind of power the foundation of this new version of Siri and then move on from there.

阿瓊，現在我們聽到的這些報道稱，他們可能會將其放在一邊，就像在搜索領域與谷歌合作一樣，他們可能會開始與 OpenAI 或 Anthropic 合作，為新版 Siri 提供基礎動力，然後再繼續前進。

我們在三星身上看到了這一點。

I was just at the Samsung event this week, we're recording this the week of July 10th.

我本週剛參加了三星的活動，我們正在錄製 7 月 10 日那一週的節目。

And it was so interesting to see them, they're not building their own AI, they're using Google Gemini and Perplexity, one of the hottest companies right now in the startup world.

他們並沒有構建自己的人工智能，而是使用了谷歌雙子座和 Perplexity，這是目前初創企業界最炙手可熱的公司之一。

They're not building their own LLMs, they're building a layer on top of the existing thing.

他們不是在建立自己的 LLM，而是在現有 LLM 的基礎上建立一層。

So I'm kind of looking at it from that perspective now, and seeing as these companies are spending enormous amounts of money building all those data centers, as they're spending so much money trying to achieve artificial general intelligence and the talent wars that we're going to get into in a little bit.

是以，我現在就從這個角度來看待這個問題，因為這些公司正在花費巨資建設所有這些數據中心，因為他們正在花費巨資試圖實現人工通用智能，以及我們稍後要討論的人才爭奪戰。

Who's building cool products on top of it?

誰在它的基礎上開發了很酷的產品？

It's not necessarily the LLM makers right now.

現在不一定是法律碩士製造商的問題。

It's going to be, you know, there's this idea now, it's going to be someone building on top of that.

這將是，你知道，現在有了這個想法，就會有人在此基礎上進行建設。

Yeah, those are some great themes there, Stephen.

是啊，這些都是很好的主題，斯蒂芬。

And, you know, we're going to dig into those in some future episodes, no doubt, but let's get in to what we're talking about today on the podcast.

毫無疑問，我們會在以後的節目中深入探討這些問題，但還是讓我們進入今天播客的話題吧。

We're talking about AI and whether the threat to the job market is real and happening right now, or maybe it has been overhyped, or maybe it's a bit of both.

我們正在討論人工智能，以及它對就業市場的威脅是真實存在的，還是被過度誇大的，或者兩者兼而有之。

To help us explore all of this and more, I have a very special guest joining us today, Michal Levram, who is a contributing editor at Fortune and also a CNBC contributor as well.

為了幫助我們探討這一切以及更多問題，今天我請來了一位非常特別的嘉賓 Michal Levram，他是《財富》雜誌的特約編輯，同時也是 CNBC 的特約撰稿人。

米哈爾，非常感謝你接受我們的採訪。

And we've got quite the global footprint between the three of us.

We've got the East Coast, we've got the West Coast, and we're here in Europe.

我們有東海岸，有西海岸，還有歐洲。

Michal, look, we introduced this podcast talking about the layoffs we saw at Microsoft.

米哈爾，聽著，我們在播客中談到了微軟的裁員問題。

You know, the companies argued that it was nothing to do with AI.

你知道，這些公司辯稱這與人工智能無關。

Is it to do with what we're seeing in terms of the efficiency gains of AI, the productivity gains of AI, or is there something else going on here?

這是否與我們所看到的人工智能的效率提升、人工智能的生產力提升有關，還是另有隱情？

Yeah, I think my sense is that it absolutely has to do with AI, but not in the way that everybody thinks or is scared of.

是的，我覺得這絕對與人工智能有關，但並不是每個人都認為或害怕的那樣。

I think that what we're seeing is not engineers or other workers losing their jobs as a direct consequence of AI overtaking jobs.

我認為，我們看到的並不是工程師或其他工人因人工智能取代工作崗位而直接失業。

I think we're pretty far from that at this point.

我認為目前我們離這個目標還很遠。

I personally don't know a single software engineer who has lost their job to AI, been replaced by AI quite yet.

我個人認識的軟件工程師中，還沒有人因為人工智能而丟掉工作，或者被人工智能取代。

Not saying it won't happen in the future, but what I do think is going on, and the reason it is directly related to AI, is that companies like Microsoft, obviously, we will see others as well, are restructuring, reprioritizing, reorganizing, which is a nice way to say laying people off, ultimately, in order to meet the demands of this new era of this technological transformation.

我並不是說未來不會發生這種情況，但我確實認為正在發生的事情，而且其原因與人工智能直接相關，那就是像微軟這樣的公司，很明顯，我們也會看到其他公司，正在進行重組、重新確定優先事項、重新組織，這是說裁員的一種很好的方式，最終是為了滿足這個技術變革新時代的需求。

It is related, it is indirectly a cause of AI, but I don't think we're seeing people losing jobs because AI agents are taking them over.

它與人工智能有關，它是人工智能的間接原因，但我不認為我們會因為人工智能代理取代了人們的工作而看到人們失去工作。

I don't know if you caught yesterday, again, we're recording this on July 10th, so for people who want to go back and look, Bloomberg had an interesting story yesterday talking kind of the fallout from those layoffs that we heard about last week.

我不知道你們是否注意到了昨天的報道，再說一次，我們是在 7 月 10 日錄製的，所以想回去看的人可以看一下，彭博社昨天有一篇有趣的報道，談到了我們上週聽說的那些裁員的餘波。

One of them was the executives telling staff, we've been able to save $500 million through AI productivity benefits, or what have you, and more talk about how much code is being written by AI at Microsoft.

其中之一是高管們告訴員工，我們已經通過人工智能的生產力優勢節省了 5 億美元，或者諸如此類的話，還有更多關於微軟人工智能正在編寫多少代碼的話題。

I look at this with a much more cynical or skeptical view, probably, than a lot of people.

我可能比很多人更憤世嫉俗或持懷疑態度。

My first question is, how do they even quantify that?

我的第一個問題是，他們是如何量化的？

I talked to so many CTOs about adopting these tools, and they have a hard time quantifying this kind of stuff.

我曾與許多首席技術官討論過採用這些工具的問題，他們很難量化這些東西。

What was your take on that when you heard that $500 million number, which, by the way, is nothing to Microsoft?

順便問一下，5 億美元對微軟來說不算什麼，你聽到這個數字時有什麼看法？

Drop in the bucket.  Look, I definitely hear from people out here in the Valley who are absolutely vibe coding.

杯水車薪。  聽著，我肯定聽到過山谷裡的人說，他們絕對喜歡編碼。

I think that movement, that trend is real.

我認為這一運動、這一趨勢是真實存在的。

And also, what's all of the other work that's involved?

此外，還有什麼其他工作要做？

A CEO can get out there, a tech CEO, and say, these are our savings, or 20% to 30% of our lines of code are now being written by AI.

科技公司的首席執行官可以站出來說，這些是我們節省下來的，或者說，我們現在有 20% 到 30% 的代碼是由人工智能編寫的。

But then what's all of the other work that's involved?

但是，還有其他什麼工作要做呢？

There are other aspects of the job of an engineer, and so how good is it at what it's doing?

工程師的工作還有其他方面，那麼它的工作能力如何呢？

I think measuring just in general, the savings part, the dollars part especially, is tricky because maybe it's writing all this code, but then it's requiring more tests after the fact.

我認為，衡量一般情況下的儲蓄部分，尤其是美元部分，是很棘手的，因為可能寫了這麼多代碼，但事後還需要進行更多的測試。

I don't think that we're clear on exactly what the numbers are telling us and what the numbers are quite yet.

我認為我們還不清楚這些數字到底在告訴我們什麼，也不清楚這些數字到底是什麼。

But again, I do think that it's happening.

但我還是認為，這種情況正在發生。

I do hear from people that they're doing it.

我確實聽到有人說他們正在這樣做。

是以，我認為我們還為時尚早。

I've not talked to a single programmer or coder who says they don't use an LLM of some sort to help them out.

我沒有和任何一個程序員或編碼員聊過，他們說自己沒有用過某種法學碩士學位來幫助自己。

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人工智能導致的失業已經開始了嗎？ (Have the AI Job Losses Started?)

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