“Jevons’ Paradox, AI, and the Future of Flattened Jobs”
Jevons’ Paradox, AI, and Job Flattening Jevons’ Paradox is the idea that when efficiency increases, overall consumption or demand also tends to rise. This applies directly to AI. As AI lowers the unit cost of tasks, it enables more services, content, and analysis. Rather than eliminating work, it often reshapes jobs and increases the total volume of activity. For example, cheaper translation through AI has led to growing demand for translating documents, videos, and social media worldwide. Job flattening refers to the blurring of traditional hierarchies and specialized job boundaries. In the past, roles such as “planner–developer–operator” were rigidly divided. Now, AI tools allow non-specialists to handle tasks like coding, video editing, or data analysis. This flattens boundaries and allows individuals to perform multiple roles with greater flexibility. When Jevons’ Paradox meets job flattening, we see paradoxical effects. Efficiency leads to more tasks: a marketing team can use AI to design ads themselves, but the number of ad variations skyrockets. Designers, instead of disappearing, focus on advanced branding and editing. Similarly, while specialization may look weaker, new subfields emerge—such as data storytelling experts or AI prompt strategists. Flattening thus drives re-specialization. Work value is also reshaped. AI and non-experts absorb repetitive tasks, leaving higher value in creativity, judgment, integration, and ethics. Organizations shift from strict hierarchies to more networked collaboration, where the key question becomes: who can best leverage AI to create new value?
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