How I’m dealing with the pressure to adopt AI as a designer
打开互动全文版（逐段中英对照 + 图/公式 + 论文问答）→本文探讨了设计师在面对 AI 技术普及时的压力与应对策略。作者认为，AI 虽然能提高效率，但无法替代设计师的创造力、同理心和批判性思维。建议设计师保持好奇心，学习 AI 工具，同时强化自身独特价值，如用户研究、故事讲述和情感设计。最终，AI 应被视为协作伙伴，而非威胁。
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作为一名设计师，在常规空间中很难不注意到关于 AI 的持续噪音。往好里说是响亮，往坏里说是自吹自擂。
It’s hard to move in the regular spaces right now as a designer without noticing the continuing noise about AI. It’s loud at best, and braggadocios at worst.
六个月前我很焦虑。午夜时分阅读 LinkedIn 上的帖子，担心我的职业生涯是否即将被自动化取代。我从事设计工作已经很长时间了，突然间这个领域变得陌生。
Six months ago I was anxious. Reading LinkedIn posts at midnight wondering if my career was about to get automated out from under me. I’ve been designing for a long time, and suddenly the space felt unfamiliar.
在事情尘埃落定之前，我感到有压力要对 AI 表明立场，而比我更聪明的人正在各个层面争论细节。我担心变化的速度，并认为每一天的损失都是需要追赶的累积工作。
I felt pressured to take a position on AI before things have settled, while smarter people than me at all levels litigate the details. I was worried about the pace of change and seeing every day lost as a compounding pile of work to do to catch back up
嗯，过去一个月我一直在努力追赶，我有消息要告诉你。事情尚未尘埃落定，地面仍在移动，与不确定性共处可能是目前最好的立场。
Well, I’ve spent the last month trying to catch up and I have news. Things haven’t settled, the ground is still moving, and sitting with uncertainty might be the best position right now.
AI 是一个大概念，信噪比极低，但透过噪音看本质——它是一系列工具的集合，当涉及采用任何新工具时，你是守门人。
AI is a big concept and the signal-to-noise ratio is terrible, but cut through the noise and boil it down – it’s a big collection of tools under a single umbrella, and when it comes to adopting any new tool, you are the gatekeeper.
你不会在确信新方法或工具能为工作带来积极结果之前就采用它。AI 也不例外，它不会因为讨论更热烈就获得免费通行证。
You wouldn’t adopt a new approach or tool without being sure it had a positive outcome for your work. AI is no different, and it doesn’t get a free pass just because the discourse is louder.
对我来说，只要我能说使用新工具后我的工作比不用更好，并且至少用户结果的质量不受影响，那么我对工作中新工具的任何立场都是有效的。
For me, any stance on a new tool in my work is valid if I can say the work I’m doing is better with it than without it, and, at the barest minimum, the quality of the outcomes for users is unaffected.
反之亦然。如果一个工具达不到这个标准，我就不会使用它。
And the opposite is true. If a tool doesn’t clear that bar, I won’t use it.
你整个职业生涯都在评估工具。这是同样的技能，只是应用于一个更嘈杂的环境。
You’ve been evaluating tools your whole career. This is the same skill applied to a noisier landscape.
我有一个理论：AI 的炒作可以用耐心来应对。听到炒作的那天，我并不担心。如果它值得了解，六个月后它还会存在。
I’ve got a theory that AI hype can be countered with patience. The day I hear the hype, I don’t worry. If it’s worth knowing it will be around in six months.
六个月后，早期采用者将吸取所有教训，分析已经完成，“我是否应该关注这个？”的答案将变得清晰。六个月并不能保证它适合我，但通过耐心过滤器是一个强烈的信号。
In six months the early adopters will have learnt all the lessons, the analysis will be done, and the answer to “should I pay attention to this?” will be clear. Six months is no guarantee it will be appropriate for me, but making it past the patience filter is a strong signal.
例如，我最初在 2025 年初夏听说 Claude Code。当时有很多噪音，网络上充斥着评论文章和反应帖子，看起来很有趣。
For example, I first heard about Claude Code in early summer 2025. There was a lot of noise, the web was thick with think pieces and reaction posts, and it looked interesting.
六个月后，在圣诞节假期前夕，我再次注意到它，因为人们仍在谈论 Claude Code。我现实生活中认识的人也在谈论 Claude Code。是时候看看了。
Six months later, in the run-up to the Christmas break, I took notice again, because people were still talking about Claude Code. People I knew in real life were talking about Claude Code. It was time for a look.
评论文章和喧嚣已经消失。等待我的是成熟的教程和共享的经验教训，最棒的是，一个比刚发布时更好的全新模型。
The think pieces and bluster were gone. What was waiting for me was established tutorials and shared lessons, and best of all, a fresh new model that was even better than when it first launched.
而那些在盛大宣传中登场却迅速消失的数千种其他工具呢？它们消失了，而我却没有浪费一分钟的时间。
And the thousands of other tools that landed with great fanfare, only to disappear? Gone, without me having spent a minute of my time unnecessarily.
六个月法则告诉了我何时该关注，而由于等待，我能够站在早期采用者的肩膀上，对其进行适当的评估。
The six-month rule told me when to look, and thanks to the wait I was able to stand on the shoulders of the early adopters and give it a proper evaluation.
将工作置于所有思考的中心，并退出炒作周期。AI 话语是一团由营销、焦虑和地位信号交织而成的乱麻，几乎不可能将信号与噪音分开。
Put the work at the centre of all your thinking, and opt out of the hype cycle. AI discourse is a tangle of marketing, anxiety, and status signalling, and it’s nearly impossible to separate the signal from the noise.
像 LinkedIn 这样的地方充斥着反应诱饵帖子，很容易被卷入其中，感觉其他人都在掌握最新进展，而你却被抛在后面。
Places like LinkedIn are full of reaction-bait posts, and it’s easy to get sucked in and feel like everyone else is on top of the latest developments while you’re being left behind.
“每个人都在用它”并不是一个设计论点。速度并不自动等于改进。
“Everyone’s using it” is not a design argument. Speed is not automatically improvement.
重新聚焦于你的工作。如果你需要退出那些话语最嘈杂的空间，那就退出。重要的信息——那些真正能帮助你的成果——会通过同事、项目以及你信任的空间自然浮现。
Refocus on your work. If you need to opt out of the spaces where the discourse is at its noisiest, do. The important information — the outcomes that will genuinely help you — will bubble up naturally through colleagues, projects, and the spaces you trust.
你应该多玩 AI。实验能积累词汇、塑造直觉，并帮助你在客户提出关于 AI 适用性的重大问题时形成自己的观点。
You should be playing with AI. Experimenting builds vocabulary, shapes instinct, and helps form the opinions you’ll need when clients ask the big questions about where AI fits.
但实验风险低，生产环境则是责任。不要混淆两者。
But experimentation is low risk. Production is responsibility. Don’t confuse the two.
用图像生成工具随便玩玩，用 AI 编码工具搭建一个一次性原型，用 Claude 来压力测试内容策略。这些都很好，这是你做好准备的方式。
Mess around with image generation. Build a throwaway prototype with an AI coding tool. Use Claude to stress-test a content strategy. All of that is good. That’s how you show up prepared.
但将 AI 部署到实际交付的工作、客户关系、人们依赖的产品中——那是另一回事，它应该得到与你将任何工具引入关键工作流程时同样的审视。
But deploying AI into work that ships, into client relationships, into products people depend on — that’s a different conversation, and it deserves the same scrutiny you’d give any tool entering a critical workflow.
如果你现在完全不使用 AI，那么将其用作“个人助理”是风险最低、争议最小的切入点。从这里开始，建立你的理解，感受它擅长什么，以及在哪里失败。
If you’re not using AI at all right now, using it as a ‘personal assistant’ is the lowest stakes, least controversial entry point. Start here and build your understanding, get a feel for what it’s good at, and where it fails.
但请记住，它是助理，而非创造者。它处理的是工作周边的事务，而非工作本身。
But remember, it’s an assistant, not a creator. It’s the stuff around the work, not the work itself.
检查这封邮件在发送前是否合理。回顾这份项目协议，找出我们尚未履行的承诺。给出处理此任务的三种典型方法，以便与我的直觉进行比较。
Check this email makes sense before I send it. Go back to this project agreement and find where we haven’t met a commitment yet. Give me three typical ways this task is approached so I can compare to my own instinct.
我不会带着空白页来找 AI。我带着一堆笔记、要点和未成形的想法，请求帮助整理它们或发现我遗漏的主题。原始材料是我的。AI 帮我整理桌面。
I don’t come to AI with a blank page. I come with a mess of notes and bullet points and half-formed thoughts, and I ask for help organising them or spotting themes I’ve missed. The raw material is mine. AI helps me tidy the desk.
这是采用 AI 时应该感到轻松的部分，因为它风险低。你并没有交出判断权。你是在释放精力，以便将其更多地用于重要的决策。
This is the part of AI adoption that should feel easy, because it’s low stakes. You’re not handing over judgement. You’re freeing up energy so you can spend more of it on the decisions that matter.
每项设计工作都有三个层次。有输入：研究、证据、数据、生活经验、简报、业务背景。有输出：人工制品、用户界面、文案、代码、最终交付的东西。而在两者之间有一个中间层——诠释、综合、判断。
Every piece of design work has three layers. There are inputs: research, evidence, data, lived experience, the brief, the business context. There are outputs: the artefacts, the UI, the copy, the code, the thing that ships. And in between there’s a middle layer — the interpretation, the synthesis, the judgement.
中间层，那就是设计。那是你决定输入意味着什么、输出应该是什么的地方。那是完成工作的地方。
The middle layer, that’s design. That’s where you decide what the inputs mean and what the outputs should be. That’s where the work is done.
AI 在边缘处表现良好。它可以总结研究。它可以生成 UI 模式。它可以写出初稿文案。但中间层是你的技能所在，我认为它需要保护。
AI is fine at the edges. It can summarise research. It can generate UI patterns. It can write first-draft copy. But the middle layer is where your skill lives, and I think it needs protecting.
当我用 AI 处理触及中间层的事情时，我感受到了向均值回归。不是糟糕的输出——而是平庸的输出。那些在所有项目上都始终有效的常规方向，但无法在特定特殊案例中脱颖而出。
When I’ve worked with AI on something that touches that middle layer, I’ve felt the regression to the mean. Not bad output — average output. Conventional directions that would work on all projects all the time, but don’t stand out for a specific special case.
那种在技术上正确但没有考虑项目政治、客户关系动态、你在用户研究中注意到的与简报相矛盾的事情的思考。AI 向均值回归的倾向会限制你的创新。
The kind of thinking that’s technically correct but doesn’t account for the politics of the project, the dynamics of the client relationship, the thing you noticed in user research that contradicted the brief. AI’s tendency to regress to the mean will limit your innovation.
有新兴证据支持这种直觉。Anthropic 在 2026 年初发表了一项研究，“AI 辅助如何影响编程技能的形成”，发现使用 AI 辅助的开发者在理解测试中的得分比手工操作的开发者低 17%。那些将思考委托给 AI 的人完成了工作——但对他们所构建的东西理解得更少。那些保持认知参与、用 AI 提问而非生成答案的人，保持了他们的技能。
There’s emerging evidence to support this instinct. Anthropic published a study in early 2026, “How AI assistance impacts the formation of coding skills”, which found that developers who used AI assistance scored 17% lower on comprehension tests than those who worked by hand. The people who delegated the thinking to AI got the job done — but understood less about what they’d built. The people who stayed cognitively engaged, who used AI to ask questions rather than generate answers, kept their skills intact.
这个原则可以转化并突显出一个明确的风险。如果 AI 进行意义建构，你就会失去练习机会。久而久之，你就不再了解自己的手艺。
The principle translates and highlights a clear risk. If AI does the sense-making, you lose the reps. Over time, you stop knowing your own craft.
我还没有感受到那种侵蚀，但每次使用 AI 工具时，它都萦绕在我的心头。
I haven’t felt that erosion yet, but it’s at front of mind every time I use an AI tool.
使用 AI 的诱惑确实存在。这是一种同辈压力。一个微弱的声音说：“我敢打赌其他人都在用 AI 做这个。”
The temptation to use AI is there. There’s a peer pressure to it. A quiet voice that says “I bet everyone else is using AI to do this”.
那个声音也不是一个设计论据。
That voice is not a design argument either.
当你决定 AI 是否应该出现在交付的工作中时，问题从“这对我来说有趣吗？”转变为“这能让为我服务的人变得更好吗？”
When you’re deciding whether AI belongs in work that ships, the question changes from “is this interesting to me?” to “does this make things better for the people I’m serving?”
有些情境适合 AI，有些则不然。你的客户可能根本没有准备好进行 AI 对话——公司治理进展缓慢，获得使用 AI 工具的许可可能会增加项目时间，而不是节省时间。将 AI 引入客户关系的实际现实往往比讨论所暗示的要复杂得多。
Some contexts are right for AI. Others aren’t. Your client may not be in a place to have the AI conversation at all — corporate governance is slow, and getting permission to use AI tools may add time to a project rather than save it. The practical reality of introducing AI into a client relationship is often more complicated than the discourse suggests.
有时正确的做法是保持沉默。不提及 AI。看到 AI 可能有所帮助的机会，但由于情境、关系或项目的政治现实而选择不利用它。察言观色也是一种技能。
Sometimes the right call is silence. Not mentioning AI. Seeing an opportunity where AI could help, and choosing not to take it because of the context, the relationship, or the political reality of the project. Reading the room is a skill too.
你的价值在于你的判断力。将你的品味、你的专长、你的经验带入工作，这才是你获得报酬的原因。
Your value is in your judgement. Bringing your taste, your specialism, your experience to the work is what you’re paid for.
你不必追逐一切，也不必全盘拒绝。
You don’t have to chase everything. You don’t have to reject everything either.
尝试，保护质量，坚持自己的判断。在 AI 真正能提升工作质量的地方使用它，在它无益之处则弃之不顾。工具会不断变化——它们向来如此。你的职责一如既往：做好工作，服务好你的服务对象，并对你的技艺有足够了解，以便察觉异常。
Experiment. Protect quality. Keep hold of your judgement. Use AI where it genuinely makes the work better, and leave it alone where it doesn’t. The tools will keep changing — they always have. Your job is the same as it’s always been: do good work, serve the people you’re working for, and know enough about your craft to notice when something’s off.
深思熟虑的节奏并非落后。这正是你一贯的工作方式。相信它。
Thoughtful pacing is not falling behind. It’s how you’ve always worked. Trust it.
我是否即将被自动化取代？或许吧。我现在不会向年轻人推荐数字领域的职业，而且我认为假装一切安好对任何人都没有帮助。
Am I about to be automated out of a job? Perhaps. I wouldn’t recommend a career in digital to a young person right now, and I think pretending everything’s fine helps nobody.
但我从事这一行已久，至今我所发现的并非威胁，而是工具。只要用心使用，它们就是有用的工具。至于其他，我仍在摸索。我想你也是如此。
But I’ve been doing this a long time, and so far what I’ve found isn’t threat, it’s tools. Useful ones, when used mindfully. The rest, I’m still figuring out. I suspect you are too.
上一篇：执行很廉价，思考不是。下一篇：当心快速工具。
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Martin 是一名设计师，致力于为政府、医疗、慈善和私营部门设计复杂的数字服务和产品。
Martin is a designer working on complex digital services and products across government, healthcare, charity, and the private sector.