AI Layoffs or 'AI-Washing'?

Editor's Note: The Double-Edged Mirror of AI

As the AI wave sweeps the globe, news of layoffs by tech giants frequently makes headlines. From Silicon Valley to the world, tens of thousands of employees have been 'optimized' out, with the culprit often blamed on 'AI efficiency gains.' But what is the truth? Has AI genuinely disrupted the employment landscape, or are companies engaging in 'AI-washing'—a marketing gimmick akin to 'greenwashing,' using AI to package traditional layoffs? As editors of AI tech news, we believe this is not just an employment crisis but a clash between corporate responsibility and technology narrative. This article, based on a TechCrunch report combined with industry data and analysis, delves into this phenomenon.

The AI Excuse Amid the Layoff Wave

Since 2025, layoffs in the tech industry have exceeded 100,000. According to Layoffs.fyi data, AI-related companies such as Inflection AI and Stability AI, despite funding, have also joined the layoff ranks. Even more striking are traditional giants: Google announced cuts of 12,000 employees, Microsoft optimized 10,000 positions, both citing 'AI-driven organizational restructuring.' In a February 2, 2026 article, TechCrunch author Anthony Ha hit the nail on the head:

'How many companies that have recently laid off employees are simply using AI as an excuse?'

Looking back, the post-pandemic tech bubble burst in 2022-2023 led to layoffs due to over-hiring, with the excuse at the time being 'macroeconomic uncertainty.' Now, AI has become the new shield. Duolingo CEO Luis von Ahn publicly stated that using AI to generate 80% of language course content led to the dismissal of hundreds of employees; Chegg directly laid off 4% of staff due to the impact of ChatGPT. These cases seem to be solid evidence of AI-driven change, but they require careful examination.

What Is 'AI-Washing'?

The term 'AI-washing' derives from 'greenwashing' and refers to companies exaggerating AI applications to beautify business decisions. A Gartner report shows that 85% of AI projects struggle to deliver results, with most corporate AI deployments still in the pilot phase. AI that truly replaces white-collar jobs, such as generative models, can automate routine tasks but is far from achieving general intelligence. McKinsey Global Institute predicts that by 2030, AI will automate 45% of media jobs, but only if infrastructure matures.

Take Google as an example. While its Gemini model is powerful, internal employee feedback indicates that AI has only optimized 10% of coding tasks; the large-scale layoffs are more due to competition in cloud business and declining advertising revenue. Although Microsoft Azure AI is growing rapidly, it also faces soaring costs from the OpenAI partnership. Data shows that in Q4 2025, the profit margin of US tech companies was only 3.2%, far below the pandemic peak of 15%. Layoffs are essentially cost control, and AI is merely the perfect narrative.

Industry Context: AI Rise and the Employment Paradox

Since the breakout of ChatGPT in 2023, the AI revolution has attracted over $200 billion in investment. NVIDIA's stock price surged, fueling the 'AI infrastructure' boom. But the job market presents a paradox: on one hand, low-skill jobs are being lost, such as customer service and data labeling; on the other hand, high-skill demand is soaring, with AI engineer median salaries reaching $300,000 per year. A LinkedIn report shows that AI-related positions grew 74% in 2025, yet overall tech unemployment rose to 5.1%.

China's market has also been affected. Giants like Alibaba and Tencent laid off over 20,000 employees in 2025, officially explained as 'AI + organizational upgrades.' ByteDance's Feishu platform integrated AI assistants, replacing some product managers. However, experts such as Professor Yao Banzhi of Tsinghua University point out: 'The current average ROI of AI is only 1.2x, far below expectations. Most layoffs are preemptive moves, not immediate replacements.'

Real Case Analysis

Take Scale AI, the data labeling unicorn that laid off 20% of its staff in 2025. CEO Alexandr Wang said 'AI self-improvement reduces the need for human labor.' But internal leaks reveal that the layoffs targeted non-core business and were actually paving the way for an IPO. Another example is IBM, which claimed to use Watsonx to replace paralegals but admitted in its earnings report that AI contributed only 2% of revenue.

In contrast, Anthropic refused mass layoffs and instead invested in employee retraining, proving that AI transformation can coexist with employment. The EU AI Act also requires companies to disclose the relevance of AI to layoffs, promoting transparency.

Analytical Perspectives: Transparency and Responsibility

The editors believe that 'AI-washing' boosts stock prices in the short term—after the layoff announcement, Google's stock price once rose 5%—but undermines trust in the long run. Investors should be cautious: Layoffs.fyi data shows that companies citing 'AI layoffs' have a subsequent rehiring rate of 60%, exposing the excuse. On the policy level, the US Department of Labor plans to legislate requiring AI-related layoff reports, similar to the EU's GDPR.

For practitioners, it is recommended to shift toward AI literacy training. Coursera data shows demand for AI skills certificates has grown 300%. Companies need to balance: AI is not a panacea, and over-reliance risks creating a talent gap.

Conclusion: The Future Test of AI

AI layoffs or AI-washing—the ultimate answer lies in data, not declarations. In 2026, as multimodal AI matures, the truth will come to light. The tech industry must move from 'washing' to 'empowering' to move forward sustainably. (Approx. 1050 words)

This article is compiled from TechCrunch by Anthony Ha, February 2, 2026.