Artificial intelligence (AI) refers to the ability of machines or software to
mimic or even surpass human intelligence in a given cognitive task. While
humans learn by both induction and deduction, the success of current AI is
rooted in induction, relying on its ability to detect statistical regularities
in task input -- an ability learnt from a vast amount of training data using
enormous computation resources. We examine the performance of such a
statistical AI in a human task through the lens of four factors, including task
learnability, statistical resource, computation resource, and learning
techniques, and then propose a three-phase visual framework to understand the
evolving relation between AI and jobs. Based on this conceptual framework, we
develop a simple economic model of competition to show the existence of an
inflection point for each occupation. Before AI performance crosses the
inflection point, human workers always benefit from an improvement in AI
performance, but after the inflection point, human workers become worse off
whenever such an improvement occurs. To offer empirical evidence, we first
argue that AI performance has passed the inflection point for the occupation of
translation but not for the occupation of web development. We then study how
the launch of ChatGPT, which led to significant improvement of AI performance
on many tasks, has affected workers in these two occupations on a large online
labor platform. Consistent with the inflection point conjecture, we find that
translators are negatively affected by the shock both in terms of the number of
accepted jobs and the earnings from those jobs, while web developers are
positively affected by the very same shock. Given the potentially large
disruption of AI on employment, more studies on more occupations using data
from different platforms are urgently needed.

人工智能通过检测任务输入中的统计规律，依靠大量训练数据和计算资源而实现对人类任务的高效模仿，研究通过四个因素来探究这种统计人工智能在人类任务中的表现，提出了一个以可学习性、统计资源、计算资源和学习技术为视角的三阶段视觉框架来理解人工智能和就业之间的变化关系，得出了在每个职业中都存在一个拐点的简单经济竞争模型，贯穿性地验证了翻译和网页开发职业的 AI 性能对就业者的影响，并呼吁急需进行更多研究以应对人工智能对就业的巨大颠覆。