Generative AI is reshaping how computing systems are designed, optimized, and built, yet research remains fragmented across software, architecture, and chip design communities. This paper takes a cross-stack perspective, examining how generative models are being applied from code generation and distributed runtimes through hardware design space exploration to RTL synthesis, physical layout, and verification. Rather than reviewing each layer in isolation, we analyze how the same structural difficulties and effective responses recur across the stack. Our central finding is one of convergence. Despite the diversity of domains and tools, the field keeps encountering five recurring challenges (the feedback loop crisis, the tacit knowledge problem, trust and validation, co-design across boundaries, and the shift from determinism to dynamism) and keeps arriving at five design principles that independently emerge as effective responses (embracing hybrid approaches, designing for continuous feedback, separating concerns by role, matching methods to problem structure, and building on decades of systems knowledge). We organize these into a challenge--principle map that serves as a diagnostic and design aid, showing which principles have proven effective for which challenges across layers. Through concrete cross-stack examples, we show how systems navigate this map as they mature, and argue that the field needs shared engineering methodology, including common vocabularies, cross-layer benchmarks, and systematic design practices, so that progress compounds across communities rather than being rediscovered in each one. Our analysis covers more than 275 papers spanning eleven application areas across three layers of the computing stack, and distills open research questions that become visible only from a cross-layer vantage point.

翻译：生成式人工智能正在重塑计算系统的设计、优化与构建方式，然而相关研究在软件、体系结构和芯片设计社区之间仍呈碎片化。本文采用跨栈视角，审视生成模型如何从代码生成与分布式运行时，通过硬件设计空间探索，直至RTL综合、物理布局和验证等环节得到应用。我们并非孤立地审视各个层级，而是分析相同的结构性难题与有效应对策略如何在整条技术栈中反复出现。我们的核心发现是趋同性：尽管领域与工具多样，该领域持续面临五大共性挑战（反馈循环危机、隐性知识问题、信任与验证、跨边界协同设计、从确定性到动态性的转变），并不断归结出五大独立涌现的有效应对设计原则（采用混合方法、设计持续反馈机制、按角色分离关注点、使方法匹配问题结构、基于数十年系统知识积累）。我们将这些归纳为“挑战-原则”映射图，作为诊断与设计辅助工具，展示哪些原则在跨层级应对特定挑战时已被证明有效。通过具体的跨栈案例，我们阐明系统在成熟过程中如何运用此映射图，并论证该领域需要建立共享的工程方法论，包括通用词汇表、跨层基准测试和系统化设计实践，以使进展能在各社区间积累而非各自重复发现。我们的分析涵盖计算栈三个层级中十一个应用领域的275余篇文献，并提炼出仅从跨层视角才能显现的开放研究问题。