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Wix.com, the Israeli website-building giant, announced it was laying off roughly 20% of its workforce about 1,000 employees. CEO and co-founder Avishai Abrahami didn’t mince words, citing the “fast evolution of AI capabilities” as a primary driver behind what he called the deepest restructuring in the company’s history.

This isn’t an isolated incident. It’s the sound of a tectonic shift in the global economy.

Over the past few years, AI has moved from a promising tool to a boardroom mandate. After a 2024 where many tech layoffs were framed as a response to “economic uncertainty,” 2025 was the year executives began openly tying job cuts to artificial intelligence. By 2026, the floodgates opened. Industry trackers show that more than 95,000 positions have already been eliminated in the tech sector this year alone. Companies like Meta, Cisco, and Block have all restructured around AI, with leaders bluntly stating their intention to move faster with smaller, highly talented teams using AI to automate more work.

As Abrahami framed it, this is not just about new tools, but about “rewiring how companies are built, how they think, how they manage and how they operate.”

Beyond the Press Release: The Human Cost of the AI Shift

While the business logic is compelling, the human reality is harsh. The ripple effects of these layoffs are already being felt far beyond corporate balance sheets.

Goldman Sachs economists have found that AI is already a measurable drag on the U.S. job market, erasing a net 16,000 jobs per month over the past year. The damage isn’t just about the immediate loss of a paycheck. Laid-off tech workers are taking an average of one month longer to find new employment. And when they do, they face a pay cut of over 3% on average a “wage scar” that can compound over time. In fact, one Goldman Sachs study found that workers displaced by tech layoffs see their earnings growth lag by nearly 10 percentage points compared to those who were never laid off.

Furthermore, the brunt of this new AI revolution is falling disproportionately on one specific generation: Gen Z.

Entry-level, white-collar, and administrative roles data entry, customer support, legal billing, and basic coding are precisely the tasks where AI models excel. These are the jobs young people typically fill to gain a foothold in the professional world. As a result, the unemployment gap between entry-level workers under 30 and experienced workers aged 31-50 has widened sharply. As one economist put it, “The destruction is hitting first, faster, and harder in the roles they’re most likely to hold.”

The Productivity Paradox and Economic Uncertainty

This is the paradox at the heart of the AI economy. On one hand, AI is unleashing a potential golden age of productivity. Morgan Stanley Research notes that industries with higher AI exposure have recorded stronger labor productivity gains, driven mainly by faster output growth. McKinsey estimates that enterprise AI use cases could unlock up to $4.4 trillion in annual productivity gains. Higher productivity, in theory, leads to higher incomes, higher spending, and a more robust economy.

On the other hand, that potential is currently being offset by very real, painful displacement.

For laid-off workers, AI tools aren’t a productivity blessing they’re the reason they’re updating their resumes. This creates a drag on consumer spending, which in turn hurts local businesses and regional economies. There’s a deep fear that even as AI drives aggregate economic growth, the rewards will be concentrated in the hands of shareholders and AI infrastructure owners, while a growing class of displaced workers is left behind a phenomenon some economists are calling “digital deindustrialisation.”

The Regulatory and Legal Landscape: Are We Prepared?

The rapid pace of this shift is also forcing legal and regulatory frameworks to catch up. In Europe, AI systems used in employment decisions, including hiring, performance monitoring, and even termination, fall under the “high-risk” category of the EU’s AI Act, mandating strict human oversight. GDPR also offers provisions against fully automated decisions with legal consequences.

In the U.S., there is growing political momentum to address this. The proposed AI Workforce PREPARE Act would require companies conducting mass layoffs to disclose if AI was a substantial factor, name the AI systems used, and estimate the percentage of roles being replaced. Meanwhile, a growing chorus of experts warns that some companies are engaging in “AI washing” blaming the technology for layoffs they would have made anyway, using it as a convenient excuse for cost-cutting.

A Final Thought: The Speed of Change is Everything

History is full of technological revolutions that ultimately created more jobs than they destroyed. The Industrial Revolution, the rise of the personal computer, and the dawn of the internet all reshuffled the labor market, but over time, new industries and new kinds of work emerged. Marc Andreessen and others have pointed to a surge in software engineering job openings as evidence that AI is already triggering a new wave of demand.

The crucial difference today is speed. The core risk is not that AI will permanently eliminate all work, but that the rate of job destruction is happening much faster than the rate of new job creation. The economy has “all sorts of other systems and levers in place that can pull us back to full employment,” Morgan Stanley’s economists remind us. But the great question of our time is whether those levers can be pulled quickly enough to prevent a generation of workers from being left behind in the transition.