Monday.com is the latest tech company to blame AI for layoffs — here are 20 others

The recent wave of tech layoffs, with AI consistently cited as a contributing factor, paints a complex and frankly, somewhat unsettling picture of the current landscape. It’s not simply about AI replacing jobs wholesale, though that’s certainly a contributing element. Rather, it reflects a broader strategic realignment within companies grappling with the rapid evolution of AI capabilities and the massive infrastructure investments required to support them. The article highlighting Monday.com's recent announcement is just the latest example in a growing list—a list that underscores a pivotal shift; the initial exuberance surrounding AI's transformative potential is giving way to a more pragmatic assessment of its operational realities and long-term cost implications. Consider, for instance, the challenges revealed in [One fallen power line exposed a growing AI data center problem. Here’s how to fix it.], a stark reminder of the immense physical infrastructure demands of AI models. These layoffs, therefore, aren’t necessarily a condemnation of AI itself, but rather a consequence of companies recalibrating their expectations and resource allocation in response to these new demands.

The narrative that AI is *causing* layoffs is a simplification. It’s more accurate to say companies are restructuring to *incorporate* AI, and that process inevitably involves redundancies in some areas while simultaneously creating demand in others. Many roles focused on manual data manipulation or routine analysis are being automated, which is a natural progression. However, this also highlights the urgent need for upskilling and reskilling initiatives. The burgeoning interest in workshops like those described in [Librarians are hosting viral ‘Avoiding AI’ workshops for people who are fed up with Big Tech] reveals a growing anxiety and a desire for agency in a world increasingly shaped by automated systems. It’s a sign that simply accepting displacement isn’t a viable long-term strategy. Furthermore, the vulnerabilities exposed in [A Complete Guide to AI Red-Teaming (With Garak Tutorial)] demonstrate that the focus needs to shift from simply building AI to rigorously testing and safeguarding it, which requires a new skill set and a different kind of expertise.

What makes this situation particularly interesting is the underlying tension between the perceived promise of AI-driven efficiency and the very real costs of achieving it. Companies invested heavily in AI, believing it would unlock unprecedented productivity gains. However, the reality is proving more nuanced. Developing, training, and maintaining large language models and other AI systems is incredibly expensive, both in terms of computational resources and skilled personnel. Some companies are realizing that their initial AI strategies were overly ambitious, or that the return on investment isn't materializing as quickly as they hoped. This isn't to say that AI is a failure; rather, it signifies a period of maturation, where hype is giving way to a more sober understanding of the challenges and opportunities. The current layoffs are a symptom of this reassessment, a sign that companies are prioritizing sustainable AI implementation over rapid, potentially unsustainable growth.

Looking ahead, the key question isn’t whether AI will continue to impact the job market, but *how* that impact will be managed. We can anticipate a continued shift in required skill sets, with a greater emphasis on AI literacy, prompt engineering, data governance, and ethical considerations. The focus will likely move from building AI models to effectively integrating them into existing workflows and ensuring responsible deployment. Will companies prioritize investing in retraining their existing workforce, or will they continue to rely on layoffs as a cost-cutting measure? The answer to that question will ultimately determine whether the AI revolution empowers workers or exacerbates existing inequalities.