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).
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.
Microsoft is also betting big on AI. The company also just announced early retirement packages for about 7% of its U.S. workforce.
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.
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.
The end of white-collar work?
In the first view, AI is an 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!).
The job losses are not just a corporate restructuring. They are an early tremor of something seismic.
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 realized we are facing an “intelligence explosion.”
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.
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.
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.
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.
Are tech companies winding back hiring sprees?
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.
OpenAI CEO Sam Altman called this dynamic “AI washing”: companies blaming AI for layoffs they would have made regardless.
For example, Meta announced in March that 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.
We don’t know in detail the make-up of the present job cuts, so Meta may just be repackaging earlier failures as AI-driven productivity gains.
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.
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 a convenient cover for financial restructuring.
Are layoffs a way to make staff use AI?
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.
This has implications for what jobs are needed and in what quantities. We think this view has the most merit.
On this reading, the tech leaders believe AI will change how software gets built. But they don’t know exactly how.
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.
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.
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 the total workforce for most of the mentioned companies.
What this means for knowledge workers
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.”
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.
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.
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.
We will have answers to the question of whether AI is mostly hype or a useful tool in the next few years.
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.
If you want to know where tech companies are going, don’t look at what they cut — watch what they hire.
Kai Riemer, Professor of Information Technology and Organization, University of Sydney, and Sandra Peter, Director of Sydney Executive Plus, Business School, University of Sydney
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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