在AI热潮下，不少Java团队认为“接入大模型=完成AI应用”，但实际开发中却深陷代码耦合、服务不稳定、成本失控的困境。其实，Java做AI的关键，是用工程化思维构建“可用级别”系统，而非后期被动优化——这才是企业级AI应用落地的核心逻辑。

A：Oracle的Java团队在2026年将致力于实现多个重要里程碑，包括预览值类型、孵化代码反射、交付AOT（提前编译）代码编译，以及最终确定结构化并发API。这些工作涉及多个OpenJDK项目，如Project Loom、Project Valhalla、Project Leyden、Project Amber、Project Panama和Project Babylon。

在AI技术深度渗透企业级应用的当下，Java作为长期占据企业开发主流的技术栈，正面临着一场转型挑战。传统Java系统的架构设计、交互模式与AI能力的融合过程中，往往会暴露出大模型整合混乱、数据治理失控、接口调用不规范、业务流程不可追溯等问题。这些痛点不仅拉高了AI应用的开发门槛，更让企业的智能化转型陷入“投入高、落地难、效果不可控”的困境。

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