
Meta和微软加入了科技裁员的浪潮——但AI真的该不该背锅?
Meta and Microsoft have joined the tech layoff tsunami …
Three ways to think about AI, massive job cuts, and the future of work.
从三个角度思考AI、大规模裁员和工作未来。
Meta and Microsoft are the latest software companies to announce big cuts to their global workforce. Both companies are also making big investments in artificial intelligence (AI).
Meta和微软是最新宣布大幅削减全球员工队伍的软件公司。这两家公司都在人工智能(AI)领域进行大量投资。
The link seems obvious. Meta’s chief people officer, Janelle Gale, said the job cuts – about 10% of staff or almost 8,000 workers – serve to “offset the other investments we’re making”. Meta boss Mark Zuckerberg has previously spoken about a “major AI acceleration” with spending in excess of US$115bn planned this year.
这种关联显而易见。Meta首席人力官Janelle Gale表示,此次裁员——约占员工的10%,或近8000人——是为了“抵消我们正在进行的其他投资”。Meta老板马克·扎克伯格此前曾谈到“重大的AI加速”,并计划今年支出超过1150亿美元。
Microsoft is also betting big on AI. The company also just announced early retirement packages for about 7% of its US workforce.
微软也在大力押注AI。该公司还刚刚宣布了针对约7%美国员工的提前退休方案。
The two tech giants join Atlassian, Block, WiseTech Global and Oracle, who have all made similar announcements this year, each evoking AI without outright blaming it.
这两家科技巨头加入了Atlassian、Block、WiseTech Global和甲骨文(Oracle)的行列。这些公司今年都做出了类似的宣布,都提到了AI,但没有直接指责AI。
What is happening here? How we understand these layoffs depends on what we think AI is, and what implications it will have. Broadly speaking, there are three ways of looking at it: that AI is superintelligence, that it’s mostly hype, and that it’s a useful tool.
这里发生了什么?我们如何理解这些裁员,取决于我们认为AI是什么,以及它将带来哪些影响。广义上说,有三种看待方式:一是AI是超级智能,二是它大部分是炒作,三是它是一个有用的工具。
The end of white-collar work?
白领工作的终结?
In the first view, AI is emerging superintelligence. It is a new kind of mind, that learns, reasons, and will soon outperform humans at most cognitive tasks (hint: it’s not!).
从最初的看法来看,人工智能正在演变成超级智能。它是一种新型的心智,能够学习、推理,并且很快将在大多数认知任务上超越人类(提示:事实并非如此!)。
The job losses are not just a corporate restructuring. They are an early tremor of something seismic.
工作岗位的流失不仅仅是企业重组。它们是某种巨大变动的前兆。
In February 2026, AI entrepreneur Matt Shumer put this view vividly – comparing the current moment to the strange, quiet weeks before COVID-19 broke into global consciousness. Most people, he argued, haven’t yet realised we are facing an “intelligence explosion”.
2026年2月,人工智能企业家马特·舒默(Matt Shumer)生动地提出了这一观点——将当前时刻比作新冠疫情爆发前那段奇怪而平静的时光。他认为,大多数人尚未意识到我们正面临一场“智能爆炸”。
The essay drew significant criticism. Commentators noted it contained little hard data and read at times like a pitch for Shumer’s company’s own AI products.
这篇文章受到了相当大的批评。评论家指出,其中缺乏硬性数据,并且有时读起来像是在为舒默公司自己的AI产品做推销。
But it captured a genuine anxiety. Something real is happening in software engineering, at least, where tasks are well-defined and success is easy to verify.
但它捕捉到了一种真实的焦虑。至少在软件工程领域,一些真实的事情正在发生,因为那里的任务界限清晰,成功很容易验证。
But the leap to “all white-collar work will be automated” is a big one. The view that AI is a kind of universal mind that learns and improves itself is far-fetched.
但将这种焦虑推导出“所有白领工作都将被自动化”的结论,跨度太大了。认为人工智能是一种能够自我学习和改进的通用心智的观点,是过于牵强的。
And most professional work is far messier than coding: ambiguous briefs, competing stakeholder interests, outputs that are hard to verify, and shifting success criteria. Coding may be a canary in the coal mine, but coal mines and boardrooms are very different places.
而大多数专业工作比编写代码要复杂得多:模糊的简报、相互冲突的利益相关者利益、难以验证的产出,以及不断变化的成功标准。编码或许是煤矿里的金丝雀,但煤矿和董事会会议室是两个非常不同的地方。
Are tech companies winding back hiring sprees?
科技公司是否正在收缩招聘热潮?
The second view sees the conversation around AI as mostly hype. AI is being invoked as cover. Companies that hired aggressively during the pandemic boom, and now face financial pressure, are blaming AI as the more palatable explanation.
第二种观点认为,围绕人工智能的讨论大多是炒作。人工智能被用作掩护。那些在疫情繁荣期积极招聘,而现在面临财务压力的公司,正将责任归咎于人工智能,因为它是一个更容易接受的解释。
OpenAI CEO Sam Altman called this dynamic “AI washing” : companies blaming AI for layoffs they would have made regardless.
OpenAI首席执行官山姆·奥特曼将这种现象称为“AI漂白”:指公司将裁员归咎于人工智能,而实际上这些裁员无论如何都会发生。
For example, Meta announced in March it would shut down its Metaverse platform Horizon World by June. Reality Labs, the division developing the technology, employed 15,000 people as of January 2026.
例如,Meta宣布,它将在六月之前关闭其元宇宙平台Horizon World。开发该技术的部门Reality Labs,截至2026年1月,雇佣了15,000名员工。
We don’t know in detail the make-up of the present job cuts, so Meta may just be repackaging earlier failiures as AI-driven productivity gains.
我们不清楚当前裁员的具体构成,因此Meta可能只是将早期的失败重新包装成人工智能驱动的生产力提升。
Another cynical reading suggests that laying off workers in the name of AI is a way to drive up stock prices. When Block invoked AI and cut nearly 4,000 roles, its stock jumped the following day.
另一种愤世嫉俗的解读是,以人工智能的名义裁员,实际上是一种推高股价的方式。当Block援引人工智能并裁减了近4000个职位时,其股价在第二天暴涨。
Announce AI-driven layoffs and you may find investors reward you for being future-focused. It is a historically familiar trick: technology has repeatedly served as convenient cover for financial restructuring.
宣布人工智能驱动的裁员,你可能会发现投资者会奖励你具有前瞻性。这是一个历史上常见的伎俩:技术曾多次作为财务重组的便利掩护。
Are layoffs a way to make staff use AI?
裁员是否是让员工使用AI的一种方式?
The third view is more nuanced. It sees AI as a powerful tool, but one that companies will need to transform themselves to take advantage of.
第三种观点更为微妙。它认为AI是一个强大的工具,但公司需要自我转型才能利用它。
This has implications for what jobs are needed and in what quantities. We think this view has the most merit.
这对所需的工作岗位及其数量具有影响。我们认为这个观点最有价值。
On this reading, the tech leaders believe AI will change how software gets built. But they don’t know exactly how.
根据这一解读,科技领袖相信AI将改变软件的构建方式。但他们不清楚确切的方式。
So they do what tech companies often do when faced with uncertainty: they create pressure. They cut headcount staff, expect those remaining to produce just as much as before, and force teams to find ways to meet those expectations using AI.
因此,当面临不确定性时,科技公司经常采取的做法就是:制造压力。他们削减员工人数,期望留下的员工能像以前一样多产,并迫使团队利用AI找到实现这些期望的方法。
It’s not a bet that AI will do everything, but that the pressure will force humans to work out how to use AI to increase productivity.
这并非赌AI能做所有事情,而是赌压力会迫使人类想出如何利用AI来提高生产力。
This also lines up with industry experience. For example, Google chief executive Sundar Pichai claims a 10% increase in engineering speed from AI adoption across the company. This could tally with cuts of around 7-10% of total workforce for most of the mentioned companies.
这也符合行业经验。例如,谷歌首席执行官桑达尔·皮查伊声称,公司范围内采用AI后,工程速度提高了10%。这可能与大多数提及公司的总员工减少7-10%的幅度相吻合。
What this means for knowledge workers
这对知识工作者意味着什么
These three views are often presented as mutually exclusive. In practice, all three expectations exist simultaneously. The honest answer to “what is really happening here” is probably “a bit of everything”.
这三种观点通常被认为是相互排斥的。但在实践中,这三种期望同时存在。对于“这里到底发生了什么”这个问题的诚实答案,可能是“什么都有点”。
What is true is that software development tends to be an early indicator of broader shifts in knowledge work. Productivity benefits from AI are real for those who adopt it. Yet adoption is unevenly distributed, and lags in less technical industries.
事实是,软件开发往往是知识工作领域更广泛转变的早期指标。对于采用AI的人来说,AI带来的生产力提升是真实的。然而,这种采用分布不均,在技术含量较低的行业尤为明显。
In this context, the ability to understand AI and make good decisions about how and where to use it is becoming a baseline professional skill.
在这种背景下,理解AI并就如何以及何处使用它做出良好决策的能力,正成为一项基础的专业技能。
The workers most at risk are not necessarily those whose tasks can be replicated by AI. They are those who wait for pressure to arrive from outside rather than getting ahead of it now.
面临最大风险的工人,不一定是那些任务可以被AI复制的人。而是那些没有主动预见风险,而等待外部压力到来的人。
We will have answers to the question of whether AI is mostly hype or a useful tool in the next few years.
在未来几年,关于AI究竟是大部分炒作还是有用工具的问题,我们将会有答案。
If Meta, Microsoft, and their peers rehire staff with different skills, redesign workflows, and emerge genuinely more capable, the case for useful AI looks good. If they simply pocket the payroll savings, the cynics were right.
如果Meta、微软及其同行重新雇佣拥有不同技能的员工、重新设计工作流程,并真正变得更有能力,那么AI作为有用工具的论点就很有说服力。如果他们只是将工资节省的钱揣进自己口袋,那么那些愤世嫉俗的人就是对的。
If you want to know where tech companies are going, don’t look at what they cut – watch what they hire.
如果你想知道科技公司的发展方向,不要看他们裁减了什么——要看他们雇佣了什么。
The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.
作者不为任何受益于本文的公司或组织工作、提供咨询、拥有股份或接受资金,并且除了其学术任职之外,未披露任何相关隶属关系。

