IA y la fuerza laboral: un otoño brutal y un futuro incierto

El final de 2025 ha sido convulso para los trabajadores que intentan interpretar las promesas de una automatización impulsada por inteligencia artificial que avanza más rápido que su capacidad para asimilarla. Los anuncios de despidos, los debates sobre recualificación y las propuestas legislativas se han sucedido a un ritmo vertiginoso. Según datos de la firma de recolocación Challenger, Gray & Christmas, las empresas estadounidenses anunciaron las empresas estadounidenses anunciaron 1,17 millones de despidos hasta noviembre de 2025, un 54 % más que en el mismo periodo del año anteriorSolo octubre fue el peor mes para los despidos en más de dos décadas en los Estados Unidos, con 153.074 recortes, incluidos 31.000 clasificados como impulsados por la IA. 153,074 job cuts, including 31,000 classified as AI-driven. The impact has not been confined to the tech sector: warehousing and logistics firms announced more than 47,000 layoffs in late 2025, highlighting how automation pressures and cost-cutting efforts are now rippling across traditionally “non-tech” industries. Together, the figures underscore how quickly the narrative has shifted from optimistic hype to hard-edged restructuring

A December survey from KPMG, reported via the Ohio CPA Association, found that 52 percent of U.S. workers now fear AI-driven job displacement, nearly double last year’s figure. That anxiety coexists with widespread ambivalence: 77 percent of employees say AI helps them focus on higher-value work, yet many also worry the technology could take over more than half of their job responsibilities within two years, the same survey indicates.

Upskilling efforts remain uneven. The 2025 KPMG American Worker Survey found that 84 percent of employees say they want more training to work effectively with AI, underscoring a widening skills gap even as many employers emphasize adoption. Public reporting and industry commentary suggest that while numerous companies claim to offer some form of AI instruction, mandatory participation remains inconsistent and poorly documented across sectors, limiting the reach of formal reskilling programs

Corporate leadership remains divided on how to measure returns from AI investment. Surveys and executive commentary indicate an ongoing debate over whether workforce reductions constitute a legitimate indicator of financial success versus whether long-term productivity gains depend more on workforce development and job redesign. This uncertainty reflects a broader tension in the AI transition: the benefits of automation are often captured at the organizational and shareholder level, while the risks and adjustment costs are borne primarily by workers.

Official statistics may undercount automation’s role. When companies restructure to “flatten” organizations while simultaneously investing billions into generative-AI systems, job losses can be difficult to categorize, even when technology adoption plays a central role. Many workforce reductions are attributed to broad “restructuring” or “efficiency initiatives” rather than explicitly labeled as automation-driven

The tech sector provides stark examples. In late 2025, Amazon announced layoffs totaling roughly 14,000 corporate workers, with reports indicating that engineering and product teams were heavily affected. Mid-level software engineers and program managers have faced mounting pressure as firms retain a small cohort of highly paid senior architects while cutting experienced middle-tier roles and increasing reliance on junior hires supported by automation and AI tools

Beyond tech, the disruption has reached traditionally non-digital sectors. Warehousing and logistics firms announced more than 47,000 job cuts in late 2025, according to Challenger, Gray & Christmas, signaling that cost-cutting and productivity automation are reshaping blue-collar work as well as office jobs.

Despite aggressive corporate adoption, automation remains far from seamless. Research from MIT and McKinsey shows that current AI deployments struggle to translate experimental success into real-world productivity — with most enterprise pilots failing to deliver measurable returns and human supervision remaining essential for complex work.

Independent of AI adoption itself, companies have also contended with a growing “boomerang hiring” trend, in which firms rehire employees they had previously laid off during periods of aggressive restructuring. Former employees who return often command higher compensation than when they left, reflecting tight labor markets, lost institutional expertise, and the high costs associated with replacing experienced workers. The pattern highlights how rapid or poorly planned layoffs — regardless of their original motivation — can ultimately prove expensive for organizations.

Sensational claims that AI will eliminate 40 percent of jobs distort more nuanced research findings. A December 2025 analysis published by Catalyst Connection, referencing the McKinsey Global Institute’s Agents, Robots and Us report, clarifies that up to 57 percent of U.S. work hours could technically be automated, but that exposure occurs at the task level rather than across entire occupations. McKinsey estimates that around 40 percent of jobs contain highly automatable tasks, meaning most roles will evolve rather than disappear altogether.

AI can increasingly handle database entry, document formatting, and routine summarization. However, core human functions, such as relationship-building, complex problem-solving, ethical judgment, leadership, and quality oversight,  remain indispensable. The objective in many of these studies is framed not as full replacement but as job redesign.

Two late-2025 research streams add further complexity. Project Iceberg, summarized by consulting firm Christian & Timbers based on MIT-affiliated research, estimates that current AI tools could technically perform portions of approximately 16 percent of labor tasks, exposing roughly 11.7 percent of total U.S. wage value (about $1.2 trillion annually) to automation at the task level. Importantly, the study emphasizes that this exposure reflects technical capability rather than actual job displacement, and that most roles incorporate many tasks that remain reliant on human judgment.

At the same time, media summaries of a study attributed to MIT’s NANDA initiative have reported that a large share of generative-AI pilot projects are failing to produce measurable business returns, highlighting challenges in moving experimental deployments into sustained operational use. The underlying report, however, is not publicly accessible, and detailed methodologies or datasets have not been released for independent verification.

Taken together, these findings suggest a persistent gap between AI’s technical potential and the difficulty organizations face in translating that capability into durable economic value — a divide that many employers are still struggling to close.

Lawmakers are beginning to confront the mismatch between corporate narratives and worker experiences. In December, U.S. senators introduced S. 3108, legislation that would require publicly traded companies and federal agencies to report AI-related layoffs to the Department of Labor, a move detailed by the Ohio CPA Association. The proposal aims to create a public tracking system for AI-driven workforce shifts, helping policymakers assess the impacts and design mitigation measures.

At the state level, labor departments are reporting rising numbers of WARN layoff notices, while employment-law experts caution that opaque AI-centered restructuring raises potential concerns over disclosure obligations and discrimination risk.

The final months of 2025 reveal a complex reality: AI’s impact on work is neither apocalyptic nor utopian. Automation is advancing rapidly, yet its benefits remain unevenly distributed. Companies are reducing headcounts faster than they are redesigning jobs, while current AI tools continue to struggle with complex tasks. Workers remain anxious, according to Ohio CPA–reported KPMG surveys, even as many also see opportunities to concentrate on higher-value, more meaningful work.

For workers, navigating this transition means staying both vigilant and proactive. As outlined in our Jobs at Risk from AI article, keeping an eye out for early warning signals, such as hiring freezes, sharp new investments in automation software, repeated “restructuring” messages, or the consolidation of teams, can help employees anticipate change rather than be blindsided by it. Moreover, our site’s Worker Well-Being analysis further highlights the emotional and psychological toll of sustained uncertainty and underscores the importance of transparent communication, mental-health support, and access to meaningful retraining.

Preparing for what comes next also means cultivating transferable skills that complement automation rather than compete with it — including communication, cross-team coordination, problem framing, quality oversight, and ethical or compliance judgment. Maintaining professional networks, documenting institutional knowledge, and staying informed about labor protections and retraining programs can further strengthen individual positioning. In an era shaped by continual technological change, workers benefit most by treating adaptability not as a temporary response to disruption, but as an ongoing career strategy, thereby ensuring that the transition to an AI-enabled economy enhances human dignity rather than diminishing it.