Artificial intelligence does not generate unique thought but rather memorizes and replicates the patterns of human social interaction. As companies rush to automate roles and reduce headcounts to cut costs, they risk destroying the very social complexity that fuels AI's capabilities. This paradox, known as the Social Edge, suggests that widespread reliance on AI to eliminate human interaction may lead to a gradual degradation of collective intelligence. Recent studies highlight a troubling trend. While AI assistance can boost individual creativity, it simultaneously causes a collective homogenization where diverse perspectives vanish. A 2024 study showed that while AI-aided writers produced creative stories individually, the group output became increasingly similar, converging on statistical averages. This mirrors the risk of model collapse, where training data generated by AI leads to a narrowing of minority viewpoints and factual degradation. Researchers warn that if the human-generated content feeding these systems becomes scarce or shallow, AI performance will stall. Furthermore, the automation of routine cognitive tasks is eroding the entry-level positions that serve as essential training grounds for future experts. By removing junior roles, organizations save money in the short term but starve the pipeline of tacit knowledge and institutional memory required for advanced judgment. When knowledge workers rely on AI without critical engagement, they suffer from overconfidence, mistaking fluent AI outputs for genuine understanding. This leads to a system where individual productivity rises while the collective capacity for complex problem-solving declines. The intelligence exhibited by large language models is not a result of superior architecture alone but a reflection of the social institutions, arguments, and negotiations that created the training data. Civilizations advance through collective intent and social friction, processes that are often stripped away when AI replaces human dialogue. Without the rich, argumentative environment of human interaction, AI cannot innovate but can only recirculate existing patterns. Consequently, the most successful organizations of the coming decade will likely be those that reject the human redundancy narrative. Instead of using AI to replace workers, they will use it to scaffold learning and increase human interaction. Leaders must recognize that investing in high-interaction roles and preserving entry-level opportunities is a strategic necessity for long-term innovation. The future of AI depends not just on scaling compute, but on maintaining a vibrant, diverse, and socially complex human ecosystem that continues to generate the high-quality data required for the technology to evolve.