The forever layoff era: why AI budgets are driving recurring, smaller workforce adjustments

If you follow layoff news closely enough, the last few years may feel like Groundhog Day. Microsoft lays off 4,800 people. Cloudflare cuts over 20%. Cisco trims nearly 5%. Then the cycle repeats. This isn't the recession-era layoff pattern you might remember. It is rolling, smaller reductions that happen while companies remain profitable and pour cash into AI infrastructure. Welcome to the era of the forever layoff.

What happened

Major tech employers are no longer treating workforce reductions as one-time restructuring events. Instead, they are adopting what Harvard Business School professor Joseph Fuller calls continuous tuning -- repeated, smaller cuts that happen alongside record revenue and massive AI budgets.

The parade of examples is notable. Microsoft said it would eliminate around 4,800 jobs. Cloudflare CEO Matthew Prince, writing in a Wall Street Journal op-ed, noted that his company cut more than 20% of its staff while growing more than 30%. Cisco CEO Chuck Robbins similarly cut nearly 5% of its workforce, citing the need for discipline in shifting investment toward long-term AI potential. Amazon and Meta have also trimmed teams in ongoing rounds.

The shift shows up in transcripts too. Analysis from AlphaSense found that mentions of layoffs alongside AI on corporate calls rose from fewer than five per quarter in 2022 to more than 100 per quarter today.

Why AI builders should care

For anyone building AI products or running AI teams, this new rhythm matters. The cuts are not typically about replacing whole departments with agents. As Carrol Chang, CEO of Andela, puts it, few firms have reached the point where AI lets them operate with a substantially smaller workforce. But boards are pushing for productivity gains without cost increases.

The resulting dynamic creates a strange market for AI talent. Chang says truly AI-native workers are scarce and expensive. That makes reskilling existing staff a more viable strategy for many companies than competing for top hires.

Practical implications

Recurring layoffs create lasting organizational costs that are easy to underestimate. Stanford professor Jeffrey Pfeffer warns that repeated cuts erode institutional knowledge and encourage top performers to leave. When a company rehires, coordination and communication suffer because teams have not worked together long.

Harvard's Fuller adds a counterintuitive point: as AI takes on more work, companies may actually need more people who deeply understand their processes, markets, customers, and regulations. Losing that context in small, ongoing rounds of layoffs may create bigger problems than the cuts solve.

Some companies are already learning this lesson the hard way, having to rehire for roles they cut after realizing AI could not fully replace the work. The cycle of severance, recruiting, and retraining quickly becomes expensive.

Caveats

Not every cut is directly about AI. Microsoft said its latest reductions are not related to AI, and Amazon has similarly stated that AI was not the driver for most of its cuts. Some reductions simply reflect post-pandemic hiring normalization. The broader pattern of smaller, more frequent layoffs is clear, but attributing every instance to AI is an inference, not a confirmed fact. Company figures and timelines should also be verified as they evolve.

The uncertainty cuts both ways. Fuller observes that CEOs worry about rivals going all-in on AI while they themselves remain cautious. That competitive pressure may lead to more cuts than the technology alone justifies.