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.

翻译：人工智能（AI）是指机器或软件在特定认知任务中模仿甚至超越人类智能的能力。尽管人类通过归纳与演绎两种方式学习，但当前AI的成功根植于归纳法，依赖其从海量训练数据中利用巨大计算资源习得任务输入统计规律的能力。本文通过任务可学习性、统计资源、计算资源和学习技术四个维度审视统计型AI在人类任务中的表现，进而提出一个三阶段可视化框架以理解AI与就业之间的动态关系。基于这一概念框架，我们构建了一个简单的竞争经济模型，证明每个职业均存在拐点：在AI性能尚未跨越拐点之前，人类劳动者始终受益于AI性能的提升；然而一旦跨越拐点，此类进步反而使人类劳动者处境恶化。为提供经验证据，我们首先论证AI性能已跨越翻译职业的拐点，但尚未跨越网页开发职业的拐点。随后，我们研究了ChatGPT的发布（该技术显著提升了多项任务的AI性能）如何影响大型在线劳动平台上这两个职业的从业者。与拐点假说一致，我们发现翻译工作者在接单数量与收入两方面均受到负向冲击，而网页开发者则从同一冲击中获益。鉴于AI对就业可能产生的巨大颠覆性影响，亟需利用不同平台数据对更多职业开展深入研究。