《帝国AI》作者郝凯伦（Karen Hao）在接受彭博社专访时，将OpenAI、Google、Anthropic等顶尖人工智慧公司比作历史上的帝国，指出它们正以前所未有的速度攫取经济和政治权力。她强调，这些公司不仅大规模撷取个人数据与创作者的智慧财产权，还依赖一支被严重剥削的隐形劳动力——包括在肯亚负责过滤极端内容的外包工人，以及在美国从事低薪「数据标注」工作的科学家、律师和博士毕业生。与此同时，AI自动化正在压缩知识工作者的就业市场，迫使他们从全职岗位流向零工经济，美国四年制大学学历失业者占比已达历史新高的25%。

郝凯伦以马尔他与OpenAI签署全国性合作协议为例，质疑发展中国家和小型国家在AI浪潮中的角色。她认为，这类合作表面上是为公民提供数位工具，实质上却是让AI公司免费获取整个国家的用户数据来训练下一代模型——因为高品质网路数据已近枯竭，直接从用户端采集成为最佳来源。她进一步指出，人们会将病历等极度敏感的资料上传至ChatGPT或Claude，却缺乏对这些强大企业的问责机制，数十亿受影响的民众没有任何正式管道来挑战或反馈这些公司每日做出的无数决策。

在技术路线方面，郝凯伦主张应从「火箭式AI」转向「自行车式AI」。她以DeepMind的AlphaFold为典范——它用极少的数据和算力精准预测蛋白质结构、荣获诺贝尔化学奖，且不涉及劳动剥削。她同时指出中国公司DeepSeek在美国晶片禁令下，以显著更少的计算资源实现了同等能力，证明高效低耗的AI开发路径切实可行。对于Anthropic和OpenAI即将启动的IPO，她认为上市带来的治理结构或许能提升问责性，但根本的变革仍需来自公众和民选官员，而非仅靠市场力量或更换领导者。

In a Bloomberg interview, Karen Hao, author of Empire of AI, likens leading AI companies such as OpenAI, Google, and Anthropic to historical empires that have amassed extraordinary economic and political power. She highlights the exploitative labor practices underpinning large language models—from Kenyan workers forced to classify graphic content for ChatGPT's safety filters to highly educated American professionals pushed into poorly paid data-labeling gig work. At the same time, she argues, AI-driven automation is eroding the very knowledge-work job market these workers once relied on, with 25 percent of unemployed Americans now holding four-year college degrees.

Hao uses Malta's nationwide deal with OpenAI as a case study to critique how smaller nations are offering themselves as data sources in exchange for the appearance of digital advancement. She contends that such partnerships primarily serve AI companies desperate for fresh training data after exhausting high-quality internet sources, effectively harvesting an entire country's user interactions. She further warns that consumers routinely upload deeply sensitive information—including medical records—to AI chatbots, yet billions of affected people worldwide have no formal mechanism to challenge or influence the cascading decisions made by a handful of executives at the top of these corporate empires.

On the question of technological direction, Hao advocates shifting from resource-intensive "rocket" AI to efficient, purpose-built "bicycle" AI, citing DeepMind's Nobel Prize–winning AlphaFold as the model: it predicts protein structures with high accuracy using minimal data and compute, without labor exploitation. She also points to China's DeepSeek, which achieved comparable capabilities with far fewer computational resources after US chip export controls, as proof that a less extractive path is viable. Looking ahead to Anthropic's and OpenAI's anticipated IPOs, she expresses cautious hope that public-company governance structures could improve accountability, but insists that meaningful change must ultimately come from democratic institutions and the public rather than from swapping one powerful founder for another.