For the past two centuries, the term “Luddites” has been used to dismiss those who question technological progress as backward or fearful of innovation. However, the original Luddites were not opposed to technology itself; they were concerned about the social and economic impacts of new technologies on people and the concentration of power in the hands of a few. These skilled workers—craftsmen and artisans with deep technical expertise—saw the industrial weaving machines as tools used to extract wealth and consolidate control, backed by the state.
Today, as artificial intelligence (AI) transforms labor markets, classrooms, and newsrooms, it is crucial to remember the Luddites. They were people who refused to accept that the deployment of new technology should be dictated unilaterally by corporations or in collaboration with the government, especially when it undermines workers’ livelihoods, social cohesion, public goods, and democratic institutions.
Journalists, academics, policymakers, and educators, whose work shapes public understanding and policy responses, have a special responsibility in this moment. They must avoid reproducing AI hype by uncritically accepting corporate narratives about the benefits or inevitability of AI innovation. Instead, they should focus on human agency and the choices made by corporations, governments, and civil society that shape the trajectory of AI development. This is not just about AI’s capabilities; it’s about who decides what those capabilities are used for, who benefits, and who pays the price.
Journalists should avoid reporting from inside the hype machine. Too much AI coverage focuses on the stages of innovation, reciting hallucination rates or benchmark scores. Journalism should interrogate innovation rather than merely chronicle it. Journalists must help people and policymakers understand technology by avoiding AI industry jargon that lets those in power control the narrative and avoid scrutiny. For example, the term “hallucination” is often used to describe predictive errors, but “error” would be more accurate. Error rates can be measured, debated, and regulated. Just as journalists avoid obscuring terms like “collateral damage” to refer to civilian victims of armed conflict, they should avoid the anthropomorphic industry term “hallucination” to improve our understanding and governance of AI systems.
Every time a news story frames AI competition as a race between companies or countries, it obscures the fact that ordinary people have been drafted into that race without their consent. Their jobs, data, and cognitive space have all been put on the line. The new weaving machines are being installed—infinitely more capable of performing a far vaster set of tasks than any prior technology—at the behest of the state and the tech corporations whose platforms we use every day. Meanwhile, workers in white- and blue-collar jobs alike are watching the terms of their employment erode. Entire sectors of the economy risk being devalued, destroying livelihoods and upending families. AI is not just a tech story; it’s a story about people, their choices, who benefits, and why.
For these reasons, journalists must understand and resist the pressure to normalize corporate narratives about “inevitability,” parroting nebulous claims about innovation without asking about its objectives and human impacts.
Academics studying AI’s labor effects often focus on narrow metrics: firm-level productivity, task replacement probabilities, skill-biased technical change. However, these frames miss the broader picture. They treat technology as something that “happens to” firms and workers, rather than something that is strategically deployed by capital. Instead, academics should ask: What kinds of labor markets are being designed around AI? What is shaping the choices about adoption? Who has bargaining power in those decisions? How will the value created by as yet unfounded promises of productivity growth be distributed? Who has power and how is it wielded to promote certain interests and negate other futures?
The Luddites understood that mechanization wasn’t just an economic shift; it was a political one. The loss of control over their tools meant the loss of autonomy over their livelihoods. It meant more monitoring, less agency, and new precarity for laborers. It also meant alliances between business owners and the state that sentenced those who protested to death. If academics are to offer useful insights today, they need to ask not just how technology is transforming work, but also how it will impact employer-provided health insurance, the social safety net, and profit-sharing arrangements.
Despite the push by some policymakers to restrict regulations on AI, state and foreign policymakers are scrambling to write rules for AI development, including transparency mandates, safety protocols, and risk-assessment frameworks. These are necessary, but they don’t touch the core challenge: What is the system that we want to create and who has the power in that system? Today, AI is being integrated into workplaces, schools, and public services with little democratic oversight and even less attention to economic justice or its environmental trajectory. The real policy questions aren’t just technical; rather, they are distributive. Will the value generated by AI productivity be used to deskill and disempower workers, or to enable new approaches to how we value data labor and forms of collective governance? Will it centralize decision-making, or support pluralism and human autonomy? Will the U.S. and the global community make choices about AI that deepen inequality or help rectify it?
La regulación ya se aplica a tecnologías desplegadas en interés público, desde leyes de zonificación hasta evaluaciones de impacto ambiental. Lo mismo debe hacerse con la inteligencia artificial, comenzando con reglas claras sobre dónde y cómo pueden ser monitoreados los trabajadores, dónde los sistemas de AI pueden reemplazar a los tomadores de decisiones humanos o creativos, y fuertes protecciones para los trabajadores en sectores que enfrentan la disrupción impulsada por la AI.
El impacto de la AI en la educación
La explosión de la AI en la educación—como herramientas que prometen tutoría personalizada, calificación automática y curricula generados por AI—ha sido impulsada por empresas de tecnología que ya dominan el mercado EdTech. Pero lo que está en juego no es solo los resultados educativos: es la formación de mentes y cómo aprendemos a razonar, a discernir la verdad, a interactuar entre nosotros. Cuando la investigación humana y la creatividad se transfieren a bots de AI antropomórficos, existe el riesgo de desvalorizar el pensamiento crítico mientras se promueve el desplazamiento cognitivo. Si nos damos la vuelta a la formación intelectual de la próxima generación a los motores probabilísticos opacos entrenados en un batido de contenido raspado, con poca transparencia y aún menos responsabilidad, no estamos mejorando la educación; estamos comercializando, corporatizando y reemplazando la pedagogía con la productividad. Lo que se pierde no es la eficiencia, sino el encuentro. No el contenido, sino el contexto. Una generación educada por AI puede ganar conveniencia, pero con el riesgo de perder la curiosidad y la creatividad.
El legado de los Ludditas
Es hora de rehabilitar a los Ludditas como guías para el presente. Entendían que el futuro no está escrito por la máquina, sino por aquellos que la manejan. Ser un Luddita hoy en día es rechazar el fatalismo de la inevitabilidad tecnológica y exigir que la tecnología sirva a muchos, no solo a unos pocos. Es afirmar que las preguntas sobre el trabajo, la agencia y la justicia deben venir antes que la velocidad, la eficiencia y la escala. Los periodistas, académicos, políticos y educadores deben dejar de preguntar solo qué puede hacer la AI y comenzar a preguntar qué debe hacer y para quién. Si no lo hacen, alguien más responderá por ellos. Y como sabían bien los Ludditas, pueden no gustarles la respuesta.
