正如迈克尔·斯宾塞（Michael Spence）和笔者最近联合发表的一篇论文中所提到的那样，偏向技能且取代劳动力的智能机器和自动化技术正在以其他几种方式推动收入差距的扩大，其中包括为超级巨星和最幸运的一小撮人带来巨大收益的赢家通吃效应，以及网络系统中因竞争不足和先发优势而产生的租金。
As Michael Spence and I argue in a recent paper, skill-biased and labor-displacing intelligent machines and automation drive income inequality in several other ways, including winner-take-all effects that bring massive benefits to superstars and the luckiest few, as well as rents from imperfect competition and first-mover advantages in networked systems.