The research field of end-user programming has largely been concerned with helping non-experts learn to code sufficiently well in order to achieve their tasks. Generative AI stands to obviate this entirely by allowing users to generate code from naturalistic language prompts. In this essay, we explore the extent to which "traditional" programming languages remain relevant for non-expert end-user programmers in a world with generative AI. We posit the "generative shift hypothesis": that generative AI will create qualitative and quantitative expansions in the traditional scope of end-user programming. We outline some reasons that traditional programming languages may still be relevant and useful for end-user programmers. We speculate whether each of these reasons might be fundamental and enduring, or whether they may disappear with further improvements and innovations in generative AI. Finally, we articulate a set of implications for end-user programming research, including the possibility of needing to revisit many well-established core concepts, such as Ko's learning barriers and Blackwell's attention investment model.

终端用户编程研究领域主要关注于帮助非专业人士掌握足够的编程知识以完成任务，然而生成式人工智能可以通过自然语言提示生成代码，可能彻底改变这一情况。本文探讨了在生成式人工智能时代，传统编程语言对非专业终端用户编程者的可用性和价值。我们提出了“生成式转变假设”，即生成式人工智能将会在终端用户编程的传统范围内带来定性和定量的扩展。同时，我们列举了一些传统编程语言对终端用户编程者仍然具有重要性和实用性的原因，并探讨了这些原因是否会随着生成式人工智能的进一步改进和创新而消失。最后，我们提出了一系列对终端用户编程研究的启示，包括需要重新审视许多已建立的核心概念，如Ko的学习障碍和Blackwell的注意力投入模型。

代码在生成式人工智能模型的终端用户编程中是否仍然是一个相关的用户界面？