Blame It on the AI? Why Layoff Waves Have Used the Same Kind of Excuse Since 2008

Marcus Fischer

21 hours ago

7 min read

A business publication recently counted how many of the layoffs announced in 2026 were officially attributed to artificial intelligence. The result: more than half. Sounds like a turning point. Except one number doesn't fit: a survey of roughly 750 CFOs by Duke University and the Federal Reserve puts the AI effect on employment at essentially zero for 2025 — and even for 2026, the expected employment decline is just 0.4 percent. Between what companies say and what's actually happening, there's a gap too large to be a coincidence.

Block cut 4,000 jobs and talked about “smaller, flatter teams.” Snap CEO Evan Spiegel let it be known that 65 percent of new code now comes from AI — shortly before roughly 1,000 people had to leave. Wix justified cutting a good fifth of its workforce by calling it “the most significant shift since the invention of programming languages.” Cisco cut 4,000 jobs with similar rhetoric — and its stock price jumped 13 percent the same day. When a layoff wave lifts the share price, it's worth asking who the story actually benefits.

The Reality Check: How Much AI Is Really Behind It?

Look closer, and there's surprisingly little substance behind the headline. Harvard Business Review asked executives why they made larger workforce cuts: 21 percent cited the expectation of future AI efficiency gains as the reason — but only 2 percent attributed the cuts to AI actually implemented. New York requires companies to disclose and justify mass layoffs under the WARN Act. Of roughly 25,000 reported layoffs, only about 46 — some 0.2 percent — were attributed to AI as the cause. And a survey of US HR leaders found that 59 percent openly admit to deliberately emphasizing AI as the reason, because layoffs sell better that way than “we hired too many people,” “the numbers don't add up,” or “we made management mistakes.”

AI is the least bad excuse companies currently have available. (Peter Cohan, Babson College)

Weak sales unsettle investors, tariffs invite political backlash — but AI sounds like the future rather than a crisis. A company cutting jobs while talking about transformation isn't perceived as struggling, but as visionary. MIT professor Paul Osterman puts it even more bluntly: companies have been using cover stories like this “for 20 years.” It's almost never about an external force forcing the decision — it's about making an already-wanted decision look like an act of nature.

Three Crises, the Same Dramaturgy

Anyone who thinks this is a purely AI-specific phenomenon hasn't been paying attention to the last two decades. The pattern repeats with remarkable reliability — only the name of the excuse changes.

2008/2009 — The Financial Crisis as a Free Pass

The recession was real, no question. But a 2009 Marketplace report vividly documents how companies simultaneously used the crisis as cover for layoffs that had nothing to do with the economy at all.

An HR consultant is quoted in the piece calling “recession” a solid eleven on a 1-to-10 scale of layoff excuses — ideal for getting rid of unwanted employees without having to justify it. Challenger, Gray & Christmas counted over a million announced job cuts by mid-2009, 60 percent more than the year before. Not every one of them was actually necessitated by the crisis — but every one of them could be explained by it.

2022/2023 — The Quiet Correction

After two boom years of historically cheap capital and hiring ahead of demand, the reversal came: Amazon, Meta, Google, and Microsoft together cut more than a hundred thousand jobs. Officially, it was “macroeconomic conditions” or “refocusing.” The more honest version came later from Fortune's and Forbes' own retrospective analyses: companies had simply hired too much, too fast, during the pandemic, often based on growth forecasts that turned out to be wishful thinking.

Admitting “we miscalculated” is unattractive corporate politics. “We're responding to a volatile market environment” sounds like foresight rather than a misjudgment.

2025/2026 — AI as the Universal Explanation

Now it's artificial intelligence's turn to play the excuse — with the added advantage that it's even better suited to the role than its predecessors. A recession eventually ends and can be checked against hard numbers. AI, by contrast, is vague enough that almost no one dares publicly contradict a board claiming the technology has made entire teams redundant.

Investor Marc Andreessen nonetheless names the real cause: nearly every large company is currently overstaffed by 25 to 75 percent — a lingering consequence of the same pandemic-era bloat that was supposed to be corrected back in 2022 and apparently wasn't fully corrected.

The actual pattern, then, isn't the technology — it's the function it performs. Every crisis and every hype cycle supplies an external, impersonal force to which management decisions can be attributed, decisions that in truth were made internally, long before. The more credible the force sounds as necessity rather than leadership failure, the better the story works — with investors, with the media, and, with a shrinking half-life, with the workforce itself.

Why AI Is the Perfect Excuse

Compared to its predecessors, the AI narrative has one decisive advantage: it's nearly impossible to disprove. A recession shows up in economic data; a “volatile market environment” can be checked against revenue numbers — both are claims with an expiration date. Whether a specific AI application actually makes a specific role redundant, on the other hand, is something almost no outsider can verify, and even internally it quickly becomes blurry what automation actually delivers versus what remains pure announcement.

One model currently circulating in the debate about AI in software development describes this rather aptly as a kind of sandwich: AI mainly compresses the middle execution layer — writing code, implementing designs. The two ends — defining the problem and setting priorities on one side, testing, taking responsibility, and maintenance on the other — remain human work. That's exactly why the actually measurable employment effect stays so small while public perception looms so large: the visible, easily illustrated middle of the work changes, the strategic surroundings barely do.

What This Means for Talent Acquisition and Employer Branding

For our field, this is more than a footnote in business journalism. Four things translate directly into daily practice.

Read labor market signals critically.

Anyone drawing conclusions about which roles and skills are actually being displaced from headlines like “Company X cuts 4,000 jobs over AI” is building workforce planning on a narrative rather than on data. The actual displacement caused by AI automation is, as of today, considerably smaller and more localized than the headlines suggest. For sourcing strategy and skills forecasting, primary data such as WARN filings or Fed surveys is worth more than press releases.

Check your own language before you use it.

The instinct to wrap a necessary workforce reduction in “AI transformation” is understandable — and risky. Just as with the executive-rhetoric case I've written about here before, employees notice within days when language and reality diverge, for instance when the same roles get reposted under a new title shortly after. That gap reliably ends up on Kununu, Glassdoor, and LinkedIn — exactly where the candidates you want to attract next are doing their research.

Use the chance to differentiate.

As “the AI did it” becomes a standard phrase, honest communication turns into a competitive advantage. A company that discloses how much of a workforce reduction is actually due to automation versus cost pressure or prior overstaffing loses a bit of shine in the short term — but gains trust that pays off in the next recruiting campaign. Precisely because this pattern has been recognizable since 2008, experienced candidates and journalists increasingly notice when they're looking at it again.

And, especially important for TA leaders with a seat at the restructuring table:

Ask where the phrasing comes from before it goes out.

Who proposes “AI-driven transformation” as the messaging, and what's the motive behind it — PR optics, reassuring investors, or actually documented automation?

TA and employer branding sit at the intersection between internal decisions and external impact, and therefore share responsibility for which version of the story ultimately goes public.

Asking that question early prevents not only reputational damage but also the awkward walk-back when it emerges months later that the supposedly automated-away tasks were simply outsourced or rehired more cheaply elsewhere.

It was never the recession alone, never the market alone, never the technology alone. There were always human decisions hiding behind something bigger.

AI is just the newest, most convenient version of that old trick — and anyone working in TA and employer branding should recognize it as such before their own organization is tempted to use the same one.

Sources

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