人工智能本应帮助初级人员脱颖而出。为什么它反而让资深人员更强大?
AI was supposed to help juniors shine. Why does it mostly make seniors stronger?

原始链接: https://elma.dev/notes/ai-makes-seniors-stronger/

## AI 与编程:资历仍然重要 最初的期望是 AI 可以让初级开发者产出高质量代码正在转变。目前的观察表明,公司现在从**将资深开发者与 AI 结合**使用中受益*更多*,而不是初级开发者。 AI 在生成样板代码、自动化例程、快速迭代和加速功能交付等任务方面表现出色——所有这些优势都因经验丰富的开发者而得到放大。然而,AI 在代码审查(缺乏推理能力)、架构设计、代码质量和安全性等关键方面存在困难。初级开发者缺乏深入理解,更容易在 AI 生成的代码中陷入陷阱,导致错误和技术债务。 AI 最适合快速原型设计、自动化已知流程、弥合知识差距和生成简单的测试。至关重要的是,**所有 AI 生成的代码仍然需要仔细的人工审查。** 作者认为 AI 并没有取代资深开发者,而是*集中*了权力在他们手中。软件开发领域仍然不成熟,缺乏其他专业领域中看到的专业化,而且削减成本的压力阻碍了适当的角色定义。需要有现实的期望;AI 是一种强大的工具,但不能取代经验丰富的判断力。

相关文章

原文

The question “Will coding be taken over entirely by AI?” has been asked to death already, and people keep trying to answer it. I’m not sure there’s anything truly new to say, but I want to share my own observations.

The early narrative was that companies would need fewer seniors, and juniors together with AI could produce quality code. At least that’s what I kept seeing. But now, partly because AI hasn’t quite lived up to the hype, it looks like what companies actually need is not junior + AI, but senior + AI.

Let’s look at where AI is good and where it falls short in coding.

Where it helps:

  • Cranking out boilerplate and scaffolding
  • Automating repetitive routines
  • Trying out different implementations
  • Validating things quickly thanks to fast iteration
  • Shipping features fast, as long as you know what you want

And who benefits most from that? Obviously seniors. In the hands of a junior, these things are harder to turn into real value. Still possible, but much tougher.

Where it backfires:

  • Code review: AI can’t really reason. Reviews can be useful, but once edge cases pop up (and they do a lot more in AI-generated code), you’re left needing a senior anyway.
  • Bad prompts: Who writes good prompts? The people who actually understand what they’re building. If someone lacks the knowledge, they might still get “okay-ish” results, but with no proper checks in place it just leads to bugs and headaches.
  • Architecture: Without solid architecture, software quickly loses value. Today AI can’t truly design good architecture; it feels like it might, but this kind of reasoning still requires humans. Projects that start with weak architecture end up drowning in technical debt.
  • Code quality: Choosing the right abstractions, applying design patterns properly, keeping things clean and context-appropriate. AI still struggles here.
  • Security: Think of it like a house without doors, or with broken locks. Security holes pop up more often with junior + AI combinations. Sure, security bugs exist everywhere, but at least with seniors you have some level of awareness and caution.
  • Wrong learning: If someone can’t really evaluate the code, they may not realize what’s wrong with what AI produces. Inside a company that can mean producing damage instead of value.

There are more examples, but the main point is this: AI is not really a threat to senior developers yet. It may even be the opposite. And this is not about criticizing juniors. It is about not throwing them into risky situations with unrealistic expectations.

Where we should use AI:

  • Fast prototyping: Perfect for trying out an idea quickly.
  • Speeding up routines: The most important use. Automate the things you already know well and repeat often.
  • Multi-disciplinary work: Filling gaps in your knowledge, suggesting useful methods or libraries, helping connect the dots when multiple domains collide.
  • Function tests: Simple, repetitive, low-risk code you can easily double-check.

From my perspective, that is the current state of things. We still have to read every line AI writes. It is far from perfect. No awareness. Reasoning is imitation. It is non-deterministic, which is why we rely on deterministic things like tests. But then, are you really going to trust the AI to write the tests that verify its own code?

It reminds me of something I tweeted: there was a prompt making AI say “I don’t know” when it didn’t know. My take was: “If such AI says ‘I don’t know,’ you can’t be sure it knows that either.”

Of course, the junior + AI pairing was tempting. It looked cheaper, and it fed the fear that “AI will take our jobs.” But when you compare software to other professions, the field still shows signs of immaturity. In construction, architects design. In software, even the architects are still laying bricks by writing code. Our roles are still not specialized or merit-driven enough, and cost-cutting dominates. That devalues the work and burns people out.

So instead of democratizing coding, AI right now has mostly concentrated power in the hands of experts. Expectations did not quite match reality. We will see what happens next. I am optimistic about AI’s future, but in the short run we should probably reset our expectations before they warp any further.

联系我们 contact @ memedata.com