The accelerating advancement of generative artificial intelligence (AI) systems is reshaping the nature, distribution and meaning of work, creativity, and economic security. This paper investigates four inter-related phenomena in the current AI era: (1) the evolving landscape of employment and the future of work; (2) the diverse patterns of AI adoption across socio-demographic groups, sectors, and geographies; (3) whether universal basic income (UBI) should become a compulsory policy response to the AI revolution; and (4) the implications of AI content policies and model behaviours for human creativity, wellbeing, and everyday decision-making. Furthermore, the paper tests the hypothesis that newer model generations may perform worse than their predecessors, and examines how users' interactions with AI systems may produce echo chambers through sycophantic model alignment. Using a mixed methodology that integrates labour market task-exposure modelling, sectoral diffusion mapping, policy-framework analysis, and qualitative discourse critique, this study develops a comprehensive framework for understanding the societal consequences of AI systems beyond productivity gains. It argues that to foster an inclusive, meaningful, and creative environment, policymakers must treat UBI as one dimension within a broader ecosystem of governance, skills development, creativity preservation, and model design. The paper concludes by outlining future research directions, including systematic evaluation of AI's creative performance across model generations, construction of a taxonomy of AI-usage distribution and equity, and formulation of governance criteria to balance content restrictions with creative freedom.
翻译:生成式人工智能系统的加速发展正在重塑工作、创造力和经济安全性的本质、分布与意义。本文探讨当前人工智能时代的四个相互关联的现象:(1) 就业格局的演变与工作的未来;(2) 人工智能在不同社会人口群体、行业和地域的多样化采纳模式;(3) 全民基本收入是否应成为应对人工智能革命的强制性政策回应;(4) 人工智能内容政策与模型行为对人类创造力、福祉及日常决策的影响。此外,本文检验了新一代模型可能比其前代表现更差的假设,并探讨用户与人工智能系统的互动如何通过迎合性模型对齐形成信息茧房。通过整合劳动力市场任务暴露建模、行业扩散映射、政策框架分析和质性话语批判的混合方法,本研究构建了一个理解人工智能系统超越生产率提升之外的社会影响的综合框架。研究主张,为培育包容、有意义且富有创造力的环境,政策制定者应将全民基本收入视为涵盖治理体系、技能发展、创造力保护和模型设计的更广泛生态系统中的一个维度。文章最后展望了未来研究方向,包括系统评估跨代人工智能模型的创造性表现、构建人工智能使用分布与公平性的分类体系,以及制定平衡内容限制与创作自由的治理标准。