#engineering #extreme programming - 9 mins read
Should we revisit Extreme Programming in the age of AI? The pace of software output has never been faster. AI tooling and decades of platform innovation have dramatically lowered the barrier to code creation. With just a few prompts or API calls, it is now possible to generate entire products, features, infrastructure, and functionality in hours rather than weeks.
#engineering #极限编程 - 9分钟阅读
在 AI 时代，我们是否应当重新审视 Extreme Programming（XP，极限编程）？
软件产出的节奏从未如此飞快。AI 工具链与数十年的平台革新，已将编码门槛压至前所未有的低点。区区几条提示词，或一次 API 调用，便能在数小时内构建出昔日需耗时数周的完整产品、功能、基础设施，甚至整套业务能力。
And yet, despite all this acceleration, delivery outcomes remain stubbornly poor. Too many initiatives underdeliver, budgets continue to overrun, and users are left underserved. If cheaper and faster code has not solved delivery, then the bottleneck must lie elsewhere. Output is not the problem Typing has never been the bottleneck.
然而，即便开发节奏愈发加速，交付成效依旧顽固低迷。太多项目货不对板，预算节节失控，用户终被辜负。若更廉价、更迅捷的代码仍未挽救交付，那么症结必不在此。产出从非难题——敲击键盘向来不是瓶颈。
We have seen successive waves of acceleration: The rise of high-level languages Widespread adoption of frameworks and package managers The move to DevOps and serverless computing Developer platforms that abstract away infrastructure And now, AI-enabled code generation. Despite this acceleration, outcomes remain stubbornly inconsistent.
我们已经历过数度提速的浪潮：
高级语言风起云涌，
框架与包管理器蔚然成风，
DevOps 与无服务器计算接棒登场，
开发者平台把基础设施悄然抽象，
而今，AI 代码生成再推波助澜。
然而，速度节节攀升，交付成效却仍然参差，难见质的飞跃。
The long-running Standish Chaos study still finds that most IT projects miss expectations, while McKinsey reports that 70% of digital transformations fail. More output has not meant better software. If faster code generation alone doesn’t deliver value, the answer is not more acceleration, but smarter constraints. For me, that’s why XP resonates now more than ever: it teaches us to slow down just enough to learn, align, and build with intent.
斯坦迪什 CHAOS 报告多年追踪显示，大多数 IT 项目仍未达成预期；麦肯锡的研究亦指出，70% 的数字化转型终以失败告终。产出增加，却未换来更优质的软件。既然更快的代码生成并不能直接创造价值，答案就不在盲目加速，而在施以更智慧的约束。正因如此，极限编程（XP，Extreme Programming）在当下愈发熠熠生辉——它提醒我们稍稍放慢脚步，留出时间去学习、对齐目标，并怀抱清晰的意图构建产品。
XP as a counterweight Unbounded acceleration leaves no time to steer. Without slowing down to notice mistakes, learn, and correct course, teams risk shipping software no one asked for. Extreme Programming (XP), developed in the late 1990s, was never intended to maximise throughput.
极限编程（XP）——高速航程中的配重
在无边无际的加速中，团队几乎没有时间调整方向。倘若不刻意放慢脚步，去发现疏漏、学习并校正航向，最终交付的往往只是无人所需的软件。诞生于 20 世纪 90 年代末的 Extreme Programming（XP，极限编程），从来就不是为了最大化吞吐量而生。
Quite the opposite: it introduced deliberate friction and constraints that enabled teams to learn, raising the probability they were moving in the right direction. One of its most radical principles, pair programming everything, halves raw output by design. The principle is simple: go slower in the small so you can go faster in the large. Take pair programming. On paper, you cut output in half. In practice, you double shared understanding.
恰恰相反：极限编程（XP）有意引入摩擦与约束，让团队在实践中不断学习，从而显著提升他们沿着正确方向前进的概率。其中最激进的一条原则，便是“全程结对编程”——所有代码由两人协同完成，设计之初就把裸产出砍掉一半。其逻辑极其朴素：小处慢下来，整体才能快起来。
以结对编程为例，纸面上产出被腰斩；而在实践中，共享认知却翻番。
You surface assumptions early. You build trust. You improve quality. You raise the baseline of capability across the team. This is the sociotechnical nature of XP practices. They shape collaboration as much as they shape code. They are an investment in learning, not just shipping. And in doing so, they provide direction, not just speed.
你能及早揭示假设，赢得信任，提升质量，并抬高团队的能力基线——这正是 Extreme Programming（XP，极限编程）的社会技术本质。XP 塑造协作的方式，正如它雕琢代码本身。它是一笔投向学习的投入，而不只是为了交付；因此，它带来的不仅是速度，更是方向。
AI magnifies the problem XP was built to solve As code generation becomes effortless, a new risk emerges: producing software faster than we can validate it. This is especially pronounced in agentic AI systems, where multiple autonomous agents generate, refine, and ship code. Without constraints, these systems can rapidly layer unvalidated logic on top of itself, entrenching assumptions and amplifying architectural complexity.
AI 正在将 XP 旨在化解的难题成倍放大。当代码生成几近零成本，新的隐患随之冒头：软件交付速度已大幅超前于验证能力。
在多智能体（agentic）AI 系统中，这一风险尤为凸显——多个自主代理同时生成、迭代并部署代码。若缺乏边界与约束，这些系统就会在未经验证的基座上层层叠加逻辑，把未经证实的假设钉死在架构里，并迅速推高整体复杂度。
Recent research reinforces the risk: LLM accuracy declines the longer the context window. In practice, this means the more you “vibe code,” the more brittle the output becomes over time, because large language models perform best at the start and end of the context window, while the middle is poorly generalised and prone to error. The result is brittle, tangled code that becomes increasingly expensive to change.
最新研究再度警示：上下文窗口越拉越长，大语言模型（LLM）的准确率便一路下滑。落到实践层面，若一味“凭感觉写代码”，时间越久，输出就越脆弱——模型只在窗口的首尾表现尚佳，而夹在中段的内容泛化乏力、错漏频生。结果是代码日益脆弱缠结，修改成本扶摇直上。
Extreme Programming was designed to prevent exactly this kind of runaway entropy. Software is still human Despite AI’s rise, software remains a profoundly human discipline. Code is written by people, for people, in organisations shaped by culture, incentives, and communication. While tools evolve, the persistent barriers to delivery remain: alignment, shared context, clarity of outcome, and user validation.
极限编程（XP）本就是为遏制这种失控的熵增而生。
软件，依旧属于人
纵然 AI 方兴未艾，软件开发仍是一门深度人本的艺术。代码出自人手，为人所用，生长于文化、激励与沟通交织的组织土壤之中。
工具日新月异，交付的顽疾却未曾改变：目标对齐、语境共享、成果清晰，以及用户验证。
XP’s values are still powerful today: Simplicity helps reduce system complexity Communication keeps teams cohesive Feedback drives learning and adaptation Respect builds safety and trust Courage empowers transparency and change From feature factories to value delivery The most successful teams do not chase speed at all costs. They prioritise flow over velocity and feedback over features.
Extreme Programming（XP，极限编程）的五大核心价值至今依旧熠熠生辉：
- 简单性：以最小必要度削减系统复杂度
- 沟通：维系团队协作与凝聚
- 反馈：驱动持续学习与迅捷适应
- 尊重：孕育安全感与信任
- 勇气：鼓励直面问题、主动求变
从“功能工厂”到真正的价值交付，最出色的团队从不盲目追逐极速。他们更看重流畅的开发节奏，而非表面的速度，更珍视持续反馈，而不是堆砌功能。
XP’s emphasis on small batches, continuous integration, automated testing, and shared ownership helps teams remain adaptable, resilient, and user-focused. As AI accelerates output, these practices will be critical to managing quality, risk, and intent.
XP 对小批量迭代、持续集成、自动化测试以及共享代码所有权的强调，使团队始终保持敏捷与韧性，并牢牢聚焦用户价值。随着 AI 带来前所未有的代码洪流，唯有这些实践才能稳控质量、化解风险，守护产品的本真意图。
Lessons from the past The CHAOS report data is telling: 1994: only 16% of software projects delivered on time and on budget 2012: improved to 37% 2020: regressed to 31% In over two decades of innovation: agile, DevOps, cloud-native platforms, and now AI, the net difference in reliable software delivery is just +14 percentage points. Toolchains alone have not solved delivery. Methodology still matters. What needs to change?
回望往昔，斯坦迪什 CHAOS 报告的数据振聋发聩：
1994 年：仅 16% 的软件项目按时且在预算内交付
2012 年：提升至 37%
2020 年：却回落到 31%
二十余载技术浪潮——敏捷、DevOps、云原生，乃至 AI——此起彼伏，可靠交付的净增幅却仅 14 个百分点。
可见，工具链虽日新月异，却未能解开交付之结；方法论的重要性依然举足轻重。
那么，我们究竟该改变什么？
As we enter the next phase of software acceleration, three things become clear: Output creation is no longer the constraint. We can produce code faster than we can validate or align it with real-world needs. We must invest in outcome-generating capabilities. Stronger feedback loops, clearer product direction, tighter team collaboration, and greater design discipline. The process needs to become more human, not less.
随着软件开发迈入新一轮加速周期，有三件事变得日益清晰：
第一，代码产出已不再是制约因素——我们的敲键速度，远快于验证其价值、对齐真实需求的脚步。
第二，必须把筹码押在真正创造结果的能力上：更紧凑的反馈回路、更明晰的产品航向、更默契的团队协作，以及更克制且考究的设计修为。
第三，流程应更像人而非机器；唯有人本节奏，方能驾驭这股加速度。
Even as AI capabilities expand, sustainable delivery will always depend on human collaboration. Our view of the Product Operating Model is built on this principle: technology only delivers when teams are optimised for collaboration, clarity, and flow. By aligning product strategy, operating rhythms, and engineering practices around people, not just platforms, you create the conditions for sustainable delivery in the AI era.
即便 AI 日益精进，可持续交付的基石仍是人的协作。我们的产品运营模型正植根于这一信念：唯有当团队在协作、清晰度与流程流畅性上臻于至善，技术才能兑现其价值。将产品战略、运营节奏与工程实践紧紧围绕“人”——而非冰冷的平台——对齐，方能在 AI 时代孕育出可持续交付的沃土。
Should we revisit XP in the age of AI? Yes. We need frameworks that anchor us to human-centred practices, even as tools become more powerful. XP provides both discipline and empathy. It centres the team. It elevates shared understanding above raw speed. It asks the most important question: are we building the right thing?
在 AI 时代，我们该不该重新拥抱极限编程（XP）？答案：当然。工具日益强大，人却始终是核心——我们需要一套将实践牢牢系于人本的框架。XP 兼具铁律与温度：它让团队站在舞台中央，将共同理解置于速度之上，并不断追问那个最重要的问题：我们究竟在打造正确的东西吗？
In an era defined by faster code and fewer constraints, XP is a rare methodology that reminds us: software is about people, not just code.
在这个崇尚极速编码、束缚日寡的时代，极限编程（XP）难能可贵地提醒我们：软件之道，在于人心，而非仅仅是代码。
Original Title: Should we revisit Extreme Programming in the age of AI?
Author: Jacob Clark
Original Source: https://www.hyperact.co.uk/blog/should-we-revisit-xp-in-the-age-of-ai
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