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AI isn’t replacing SREs, it’s deskilling them

💌 Hey there, it’s Elizabeth from SigNoz!

你好，我是来自 SigNoz 的 Elizabeth！

This newsletter is an honest attempt to talk about all things - observability, OpenTelemetry, open-source and the engineering in between! &This piece took 6 days, 5 hours to be cooked, hope we served. 🌚

这份通讯旨在真诚地探讨一切相关话题——可观测性、OpenTelemetry、开源以及其间的工程实践！这篇文章耗时 6 天 5 小时精心打磨，希望能够满足大家的期待。🌚

There are two popular prophecies floating around tech circles these days.

如今科技圈流传着两种流行的预言。

The first says SRE is the future of all software engineering, that as AI writes more and more code, the humans who remain will be the ones keeping systems alive. The second says AI will devour every tech job alive, SREs included. Neither is particularly useful if you’re an SRE trying to figure out what your Tuesday will look like in 2027.

第一种预言称，SRE（站点可靠性工程）是所有软件工程的未来，随着 AI 编写的代码越来越多，留下来的人类将是那些负责维持系统运行的人。第二种预言则称，AI 将吞噬所有技术岗位，包括 SRE。如果你是一名正在思考 2027 年周二工作会是什么样子的 SRE，这两种说法都没有什么实际参考价值。

Let’s ask a more grounded question by looking at what’s already happening: When AI handles 95% of your incident response, do you get worse at handling the 5% that actually matters?

让我们通过观察已经发生的事情，来提出一个更务实的问题：当 AI 处理了你 95% 的事故响应时，你处理那 5% 真正关键问题的能力是否会下降？

Most of us already use AI for our daily work (our brains a little fried!), and so do SREs. Today’s discussion agenda is not whether AI replaces SREs, but whether AI is quietly making SREs less capable and whether anyone will notice anything before the next novel outage hits. The foundational framework for this entire debate comes from a 1983 research paper that’s eerily prescient.

我们大多数人已经在日常工作中使用 AI（大脑都有点烧坏了！），SRE 也不例外。今天讨论的议题不是 AI 是否会取代 SRE，而是 AI 是否正在悄悄削弱 SRE 的能力，以及在下一次新型故障发生前，是否有人会察觉到这一点。这场辩论的基础框架源于 1983 年一篇具有惊人预见性的研究论文。

Note from author: Below is a brief precursor to History of Automation, which you might enjoy if you are into History and Cultures (like me 😉).

作者注：以下是《自动化历史》的简要前言，如果你像我一样喜欢历史和文化，可能会感兴趣。😉

In 1983, a cognitive psychologist named Lisanne Bainbridge published a paper called Ironies of Automation. It became one of the most cited papers in human factors research, and its core argument is almost uncomfortably relevant today.

1983 年，一位名叫 Lisanne Bainbridge 的认知心理学家发表了一篇名为《自动化的讽刺》（Ironies of Automation）的论文。它成为了人因工程研究中被引用次数最多的论文之一，其核心论点在今天看来依然具有令人不安的现实意义。

Bainbridge studied what happened when factories and industrial systems automated the work of blue-collar operators. The findings were paradoxical and revealed that the more you automate a process, the more critical the human operator becomes during the rare moments automation fails and the less practice they get, and the worse they become at exactly those interventions. Automation, which was inherently designed to remove humans from the loop, left them with the worst possible job, i.e., long stretches of passive monitoring punctuated by rare, high-stakes crises they were increasingly unprepared for.

Bainbridge 研究了工厂和工业系统在自动化蓝领操作员工作时所发生的情况。研究结果是矛盾的：你对流程的自动化程度越高，当自动化系统在罕见情况下失效时，人类操作员就变得越关键，但他们得到的练习机会却越少，从而在这些关键干预任务上的表现也越差。自动化本意是为了将人类从流程中解放出来，结果却让他们陷入了最糟糕的工作状态：长时间的被动监控，中间穿插着他们越来越难以应对的罕见且高风险的危机。

Ring any bells yet? 🙂

听起来是不是很耳熟？🙂

Basically, I’m drawing a parallel between the AI revolution and industrial automation. Industrial automation reshaped blue-collar work by taking over routine physical tasks, and the workers who remained had to handle exceptions they’d lost the muscle memory for. AI is doing the same thing to knowledge workers by taking over the routine cognitive tasks, the pattern matching, the triage, the known-issue resolution and leaving humans with the rare, complex, ambiguous problems.

基本上，我是在将 AI 革命与工业自动化进行类比。工业自动化通过接管常规体力劳动重塑了蓝领工作，留下的工人必须处理那些他们已经失去肌肉记忆的异常情况。AI 正在对知识工作者做同样的事情：它接管了常规的认知任务、模式匹配、分类和已知问题的解决，只把那些罕见、复杂且模糊的问题留给人类。

The exact problems that require deep expertise, the exact expertise that atrophies when you stop practising.

这些正是需要深厚专业知识的问题，而这种专业知识一旦停止实践就会萎缩。

Now we’re replaying this pattern with AI agents, and the stakes in software systems are only growing.

现在，我们正在 AI 智能体身上重演这一模式，而软件系统的风险却在不断增加。

Let’s take stock of where things stand today in the world of site reliability engineering.

让我们盘点一下当今站点可靠性工程领域的现状。

Alert noise reduction and intelligent grouping, runbook execution for known issues, log pattern detection and anomaly flagging, and basic root-cause suggestions from historical incident data, and auto-remediation for well-understood failure modes like restarting a crashed pod or scaling up a service that’s running hot, are all fairly automated today.

告警降噪与智能分组、已知问题的运行手册执行、日志模式检测与异常标记、基于历史事故数据的基本根本原因建议，以及针对已知故障模式（如重启崩溃的 Pod 或扩容过载的服务）的自动修复，在今天都已经相当自动化了。

Some immediate targets include multi-signal correlation across metrics, logs, and traces, autonomous root-cause analysis for partially understood failures, predictive incident detection before users are affected, AI-driven change risk assessment and automated rollbacks.

目前的一些直接目标包括：跨指标、日志和追踪的多信号关联，针对部分已知故障的自主根本原因分析，在用户受影响前的预测性事故检测，以及 AI 驱动的变更风险评估和自动回滚。

PagerDuty frames this as a tiered model.

PagerDuty 将其构建为一个分层模型。

Tier 1 incidents: Known issues with known fixes get fully automated.

一级事故：具有已知修复方法的已知问题实现完全自动化。

Tier 2 incidents: Partially understood problems receive AI recommendations with human validation.

二级事故：部分已知的问题由 AI 提供建议，并由人工进行验证。

Tier 3 incidents: Novel, complex, cascading failures stay human-led, with AI providing supporting context.

三级事故：新颖、复杂、级联式的故障仍由人工主导，AI 提供辅助背景信息。

But here’s the catch.

但问题就在这里。

If human SREs (okay, now we have to use adjectives like human 🥹) only engage with Tier 3 incidents, i.e. the novel, never-before-seen outages, where do they build the intuition to handle them? Intuition is usually developed from years of hands-on incident response, pattern recognition built through repetition, and the kind of gut-level understanding of a system that only develops from painfully waking up at the odd hour to solve the bug that brought the system down.

如果人类 SRE（好吧，现在我们必须加上“人类”这个形容词 🥹）只处理三级事故，即那些新颖的、从未见过的故障，他们从哪里建立处理这些问题的直觉呢？直觉通常源于多年的实战事故响应、通过重复建立的模式识别，以及那种只有在深夜被叫醒去修复导致系统崩溃的 Bug 时，才能培养出的对系统的深刻理解。

Subscribe if you wish to read some more hot takes. We are cooking some great ones!

如果你想阅读更多犀利观点，请订阅。我们正在筹备一些精彩内容！

This is where the picture starts getting blurry. Let’s look at some emerging research on AI-induced deskilling across multiple fields, which paints a consistent and concerning picture.

这就是情况开始变得模糊的地方。让我们看看一些关于 AI 导致多领域技能退化的新兴研究，这些研究描绘了一幅一致且令人担忧的图景。

In medicine, a recent study found that endoscopists who used AI assistance for polyp detection saw their unassisted detection rates drop from 28% to 22% after a period of AI use. They got worse at the thing they were supposed to be experts in, not because they forgot the theory, but because they stopped exercising the skill.

在医学领域，最近的一项研究发现，使用 AI 辅助进行息肉检测的内窥镜医生，在经过一段时间的 AI 使用后，其独立检测率从 28% 下降到了 22%。他们在自己本应是专家的领域表现变差了，不是因为忘记了理论，而是因为他们停止了对该技能的练习。

In aviation, research has shown that long-haul pilots who rely heavily on autopilot systems experience measurable degradation in situational awareness and manual flying ability. The problem got serious enough that the FAA now mandates more manual flying time to counteract the effect.

在航空领域，研究表明，严重依赖自动驾驶系统的长途飞行员，其态势感知能力和手动飞行能力会出现可测量的退化。这个问题已经严重到美国联邦航空管理局（FAA）现在强制要求增加手动飞行时间来抵消这种影响。

Somewhere over the past year, AI stopped being a tool I occasionally reached for and became the first thing I reach for, always. My instinct now is to offload as much as possible and apply my own thinking only where it’s absolutely unavoidable. The problem is that these moments are becoming the only exercises my brain gets, and I can feel the rust.

在过去的一年里，AI 不再是我偶尔使用的工具，而成了我总是第一个想到的东西。我现在本能地想要尽可能多地卸载工作，只在绝对无法避免的情况下才动用自己的思考。问题在于，这些时刻成了我大脑仅有的锻炼机会，我能感觉到自己正在生锈。

We can draw a pattern here. The more you let the system handle, the worse you get at handling things yourself and here’s the truly dangerous part, you don’t feel it happening. It gets masked as hyper-productivity. Cognitive research suggests that because AI tools make tasks feel easier and en

我们可以从中总结出一个规律：你让系统处理得越多，你自己处理事情的能力就越差。而真正危险的部分在于，你根本感觉不到这种变化。它被伪装成了超高生产力。认知研究表明，由于 AI 工具让任务感觉更简单且……