The AI Layoff Wave 2.0: Why Goldman Sachs Warns Automation Could Trigger Another Round of Job Cuts in 2026

The tech industry's layoff cycle may be far from over. A sobering new report from Goldman Sachs warns that 2026 could bring another significant wave of job cuts as companies shift spending from human workers to artificial intelligence systems. The analysis, which circulated among institutional clients this week, challenges the narrative that AI will create more jobs than it destroys—at least in the near term.

The warning comes as companies across industries report plans to dramatically increase AI investments. According to a CompTIA survey cited in the report, 84% of organizations plan to at least moderately increase their AI resources in 2026. That spending has to come from somewhere, and increasingly, it's coming from headcount budgets.

The Numbers Are Stark

Goldman's analysis synthesizes multiple data sources to paint a concerning picture of employment trends:

• 2025 layoffs: Tech companies announced 783 separate layoffs affecting 245,953 workers—up dramatically from 2024
• Total U.S. job cuts: The Challenger, Gray & Christmas outplacement firm counted nearly 1.2 million job cuts announced by U.S. employers in 2025, up 54% year-over-year
• AI attribution: An increasing share of layoff announcements explicitly cite AI and automation as factors
• Budget reallocation: Survey data shows companies are moving budget dollars from labor to AI tools

"I think on the flip side of seeing an incremental increase in AI budgets, we'll see more human labor get cut and layoffs will continue to aggressively impact the U.S. employment rate."

— Investor quoted in the Goldman Sachs report

Which Jobs Are Most at Risk

Not all workers face equal exposure to AI-driven displacement. The Goldman analysis identifies "repetitive cognitive work" as the category most vulnerable to automation:

• Accounting and bookkeeping: Rule-based financial tasks are highly automatable
• Basic legal work: Contract review, document analysis, and compliance monitoring
• Junior software development: Routine coding tasks increasingly handled by AI assistants
• Financial modeling: Standardized analysis that AI can perform faster and cheaper
• Customer service: Chatbots have reached the point where they handle most routine inquiries
• Paralegal and research: Document summarization and research are AI strong suits

The common thread is predictability. Jobs built on applying known rules to new situations are precisely what large language models excel at. The workers most at risk are those whose tasks can be clearly described and whose outputs can be objectively evaluated.

The Offshore Twist

Perhaps the most counterintuitive finding from the analysis comes from a Forrester Research prediction: half of workers laid off for AI-related reasons will be quietly rehired—but offshore or at significantly lower salaries.

This suggests that many companies announcing "AI-driven efficiency gains" are actually pursuing a more complex strategy. They eliminate expensive domestic workers, deploy AI tools where possible, and hire lower-cost offshore workers to handle tasks that still require human judgment. The result looks like AI transformation but is partly old-fashioned labor arbitrage.

This dynamic has significant implications:

• For workers: Displacement may be followed by opportunities, but at reduced compensation
• For companies: Cost savings compound as AI and offshoring work together
• For the economy: Aggregate demand could suffer if wages decline even as employment stabilizes

The Bright Spots

Not all news from the AI employment front is negative. Demand for workers with AI and machine learning skills remains robust, even as generalist tech roles disappear.

The Dice 2025 Tech Jobs Report found that 53% of U.S. tech job postings in November required AI/ML skills, up from 48% in October and just 29% a year earlier. For workers who can position themselves as AI specialists rather than potential AI targets, the job market looks quite different.

New roles are also emerging in areas such as:

• AI development and deployment: Building and maintaining AI systems
• Data governance: Ensuring AI systems use data appropriately
• System monitoring: Overseeing AI outputs and catching errors
• Ethical oversight: Evaluating AI decisions for bias and fairness
• Human-AI integration: Designing workflows that combine human and machine capabilities

The challenge is that these emerging roles often require different—and typically more advanced—skills than the jobs they're replacing. The transition is not seamless.

What Workers Should Do

The Goldman analysis includes recommendations for workers navigating the AI-disrupted job market:

• Develop AI fluency: Understanding how to work with AI tools is becoming table stakes
• Focus on non-routine skills: Complex problem-solving, creativity, and relationship-building remain human advantages
• Build domain expertise: Deep knowledge of specific industries is harder to automate than generic skills
• Consider adjacent moves: Roles that complement AI (like AI training and oversight) may offer more security
• Maintain financial flexibility: Building savings and reducing debt provides a buffer during transitions

Policy Implications

The potential for another layoff wave raises questions for policymakers. Existing unemployment insurance and retraining programs were designed for temporary, cyclical unemployment—not structural displacement from technological change.

Potential policy responses under discussion include:

• Extended unemployment benefits: For workers displaced by automation
• Skills training funding: Subsidizing education in AI-adjacent fields
• Transition assistance: Support for workers moving to new industries or regions
• Universal basic income: More radical proposals to decouple income from employment

None of these solutions are without controversy or tradeoffs, and the political will to implement them remains uncertain.

The Bottom Line

Goldman Sachs' warning about a potential AI layoff wave 2.0 serves as a reality check for anyone assuming the technology revolution will be painless. While AI will undoubtedly create new opportunities and drive economic growth, the transition period may be rocky for millions of workers whose skills no longer match employer needs.

For individual workers, the message is clear: proactive adaptation is essential. Those who wait for the layoff notice before developing new skills may find themselves competing with a rapidly growing pool of displaced workers for a limited number of positions.

The AI revolution is happening whether we're ready or not. The question is whether individuals, companies, and policymakers can manage the transition in ways that distribute both the benefits and the burdens fairly.