返回列表
🧠 阿头学 · 💬 讨论题

AI 时代,品味不是天赋而是高频拒绝训练

这篇文章最站得住的判断是:AI 已经把“合格执行”彻底商品化,真正稀缺的不是会做,而是能明确拒绝平均值;但它把“品味”吹成万能护城河,也明显过头了。
打开原文 ↗

2026-04-03 原文链接 ↗
阅读简报
双语对照
完整翻译
原文
讨论归档

核心观点

  • 执行贬值是大趋势 作者最站得住脚的判断是:AI 正在快速抹平写作、设计、搭建、包装的基础门槛,结果不是“人人都变大师”,而是“人人都能交出差不多能看的 7/10”,所以真正稀缺的能力从生产转向选择、删减和定标准。
  • “平均值审美”会泛滥 文章对 AI 默认输出的批评是准确的:主流模型确实倾向复刻历史上高频、低风险、被奖励过的模式,因此首稿通常不差,但高度可预测;这对需要差异化的品牌、产品、内容来说,不是优势,而是慢性同质化。
  • 把 AI 当批评训练器,比当成品机更有价值 “先生成 10-20 个版本,再逐个写出‘失败在于 X’”是全文最实用的部分,因为它强迫创作者显化自己的判断标准;这比继续堆灵感、堆模板更能练出稳定的审美与决策能力。
  • “具体性测试”是有效抓手 “如果一段话换个对象也成立,那它就没什么品味”这个测试很有操作性,尤其适合检验文案、定位、首页、叙事是否只是通用套话;它虽然不等于全部品味,但足以筛掉大量 AI 式正确废话。
  • 文章最大问题是定义混乱 作者一边说“AI 根本没有品味”,一边又说“AI 终将比多数人更有品味”,这不是小瑕疵,而是核心概念打架;后面用“带着视角的品味”补洞,实际上没有把边界讲清楚,所以整篇更像强观点写作,不是严谨理论。

跟我们的关联

  • 对 ATou 意味着什么、下一步怎么用 ATou 如果还把 AI 主要当“提效工具”,很容易只得到标准化的中上结果;下一步应该把工作流改成“多版本生成 + 明确批评 + 加约束重写”,重点训练“为什么不行”的表达能力,而不是迷信首稿。
  • 对 Neta 意味着什么、下一步怎么用 Neta 在做研究、摘要、产品判断时,最容易被 AI 的“说得通”欺骗;下一步应把“具体性测试”变成固定检查项:这段判断是否能替换到别的项目上仍成立,如果能,就说明洞察还不够深。
  • 对 Uota 意味着什么、下一步怎么用 Uota 如果做内容、品牌、表达,最大的风险不是做得差,而是做得“像所有人一样好”;下一步要有意识建立自己的禁区和偏好库,比如明确哪些句式、视觉、语气坚决不用,让模型服务于视角,而不是反过来。
  • 对三者共同意味着什么、下一步怎么用 这篇文章提醒的不是“人人都该追求艺术级杰作”,而是“该区分哪些场景只要 75 分,哪些场景必须冲 90 分”;下一步最该做的是把任务分层:内部沟通可接受均值,品牌前台、关键销售页面、核心叙事则必须投入人工判断。

讨论引子

1. AI 时代真正稀缺的到底是“品味”,还是更可落地的“评价函数/验收标准”? 2. 在商业场景里,什么时候追求“有品味的 90 分”是必要投资,什么时候“稳妥的 75 分”反而 ROI 更高? 3. 如果所有人都用同样的方法训练“反均值审美”,会不会最后只得到另一种更高级的同质化?

AI 能在一个周末帮你造出一家估值 1 亿美元的创业公司

但它帮不了你的,是

**帮你做出有品味的内容

下面就是 2026 年如何不做出 AI 垃圾 **

品味是新的核心技能

一切都始于 Brockman 那条五词推文,它迅速刷屏

  • 事实是,执行已经商品化,筛选与策展变得稀缺。

  • 这里暗藏一个陷阱:把品味当成一种被动、静态的资质。

  • 真正的品味往往只是练出来的。

  • 当一个人不再创作、不再阅读、不再观察时,品味会萎缩,或变得过时。

  • 到那时,只剩下观点了,而观点和品味是两回事。

梗图式的回复毫不留情,指出一个事实

硅谷到处都是对字体有强烈意见的人,除此之外,只有一堆话和半拉链抓绒衫。

什么是品味(以及它怎么被杀死)

品味是在不确定中做出区分。

走进一个摆着十样东西的房间,你能知道哪一样不一样,能准确说出为什么,然后用一种会改变我们看法的方式把它讲清楚。

乔布斯有这个。里克·鲁宾也有。可他们都无法把它变成框架,因为框架是给稳定世界准备的。

品味活在流动的世界里。你一把它形式化,它就化石化了。

从蒂尔的视角看:品味是删减,不是加法

蒂尔对竞争的真正洞见在这里同样适用:世界常常奖励最后出手的人,而不是第一个。在这个语境里:

  1. 有品味的人不一定是最先发现某样东西好的人;

  2. 他们往往是最后一个发现的人,但他们发现得精确而彻底,以至于不需要再从根本上添加什么;

  3. 重点更像是在剔除无品味,而不是无品味地堆砌。

基础款 B***h 品味

AI 让设计、搭建、上线的执行速度达到光速,但按当前 AI 模型的定义

给出的,是我称为 **统计式品味。 ** 的东西。

这是我给 AI 在海量数据集上训练后,默认产出的观感与气质起的名字。

它就是平均值。它是好里最安全的版本,或者说最优化的版本,而在 2026 年,这种稳妥的好无处不在。已经见过它:

  • 白底网站上那种一模一样的紫到蓝渐变

  • 同样的大胆主张加上每个 CTA 三条要点的版式

  • 每个人邮件里那种客气、企业化的语气

AI 没有杀死品味。它只是让平均水平变得更容易拿到。

逃离均值

今天真正的问题已经不是 AI 垃圾了。真正的问题是,大多数人会用 AI,却依然永远练不出品味,因为他们停在了平均值。

  • 你可以做到前 75%,也就只是 还不错。

  • 好变成了人人可得。

  • 所以,接下来 25% 里的每一点 增量 ** 都更重要:它把 统计式品味策展式品味** 分开。

在这个时代,人们会用极端标签给世界分类:

  • 75% 及以下 -> AI 垃圾

  • 高于 75% -> 杰作

我们活在一个极端时代。这份指南讲的是怎么逃离平均。

为什么 AI 让“稳妥的好”如此容易

AI 之所以让泛化内容变得容易,是因为它做了三件事,而这三件事正是创造我称为文化平均审美所必需的:

  1. 它预测什么通常有效。

  2. 它复刻人们曾经被奖励过的模式。

  3. 它把上千个参考压缩成一个稳妥的好输出。

所以你的第一版输出往往看起来像应用商店落地页,或 Medium 博客文章,或某个网站上平均水平的首屏。它不糟,但可预测,而可预测是品味的敌人。

转变:平均成了新的平庸

2026 年真正的转变是,平均成了新的平庸。

  • AI 之前,东西平庸,是因为创作者缺技能。

  • AI 之后,东西平庸,往往是因为创作者太早停手。

AI 抬高了地板,但天花板比以往更高。

  • 品味更重要,是因为 AI 让中间层挤满了人。

  • 现在只做到高于平均已经不够了。

人人都能交付一个 7/10。

  • 唯一能脱颖而出的方式,是把判断做到 9/10,把选择做到 9/10,把具体性做到 9/10。

  • 还要有真实、真诚的视角。

AI 没有品味(所以它反而能帮你练出自己的)

人们最不舒服、但迟早要面对的真相是,AI 在根本上缺乏品味。

它是最好的镜子,会永远映照用户,因为它逼你回答一个以前可以回避的问题:

为什么这不对?

AI 之前,平庸作品的产出需要时间。 努力总会制造噪音,你可以把噪音误当成辛苦、或误当成质量。

AI 把噪音拿走了。

现在你可以在 10 分钟里生成同一份 AI 垃圾的 10 个不同版本。

AI 产出和你真正想要之间的差距,就是你的品味所在。

大多数人会一直回避这个差距。

有品味的人必须学会把它说出来。

核心循环:先生成,再摧毁

这就是用 AI 练品味的方法。

先生成,然后摧毁。

  • 对任何东西先做 10 到 20 个版本。

  • 不是用它来选出最好那一个。

  • 是用它来训练拒绝的词汇。

你真正训练的肌肉是:失败在于 X。

  • X 是结构性的,不是表面装饰性的。

做得足够多,你就会把一个此前说不清的框架内化进身体里。

找到经典,然后无视经典

用 AI 找到经典,然后无视经典。

让 AI 画出任何领域在过去 100 年、200 年或 300 年里的最佳作品谱系。

  • 去读。

  • 去看。

  • 去吸收。

用深度研究不是为了抄,而是为了理解它为什么在当时能成立

Minimax-M2.5 是个人选择 :) 用什么能用都行

今天,平均品味和策展品味之间,唯一的分水岭是好奇心。

  • 你问这些强大模型的问题越多,你就会越往深处钻。

大多数人愿意卡在平均值。

所以他们永远不会去问那些问题,也就永远做不出那种策展级的软件、写作、设计,或者任何东西的杰作。

带着赌注去做

从 Will Manidis 的文章 Against Taste 里推出来一个启发

不冒任何风险、只停留在理论里的品味,是被动的。

把文章发出去。把产品上线。把路演稿讲出来。

  • 真实产出的反馈回路不可替代。

  • 人们会回应,或不回应。

AI 能加速一切,除了那种老派的反馈。

AI 的闪电式执行让发布与公开都变得光速。

  • 也因此让品味加速成长。

  • 品味的根本前提,是知道哪些规则愿意打破。

你不可能打破自己不知道的规则。

具体性测试

每次 AI 生成一个版本,都问一句:

  • 这段话能不能套在别的东西上?

如果能;如果把具体对象换掉,内容却完全不变,那它就没有根本的品味。

品味按定义就是具体的。

里克·鲁宾在 *Californication * 上的制作听起来独一无二。

  • 因为它就是为那些歌做的。

  • 在那个特定时刻。

  • 由那一群人完成。

按这个逻辑,品味是不可约的语境产物。

抵触感就是信号

当 AI 产出的东西让你不舒服,但你又说不清为什么,不要跳过。

  • 和那种摩擦感待在一起。

  • 那就是品味的原材料。

大多数人会直接滑过去。

但那个会停下来、会审问、会问自己到底哪里不对的人,其实正在做最难的工作。

久而久之,这些累积的瞬间会变成你的审美免疫系统:

  • 说不清的不适

  • 不确定

  • 不理解

品味 vs “比人类更好”的品味

关于品味更深、更不舒服的真相是,AI 终将会在大多数事情上拥有比大多数人更好的品味。

  • 这不是因为品味是机械的,它不是。

  • 而是因为品味可以从人类产出里被训练出来。

AI 无法替代的,是带着视角的品味。

里克·鲁宾不只是知道什么好听。

  • 他知道在他相信音乐应该服务于什么的前提下,什么才算好听。

  • 而这种信念不可能从数据集语料里推导出来。

  • 它来自真实地生活。

  • 来自选择。

  • 来自失去。

品味是生活的产物,不是数据集。

如何用 AI 练品味的答案,是用 AI 把品味里的一切都剥离出来。

  • 让它产出称职的。

  • 平均的。

  • 站得住脚的。

而在它产出和你真正想要之间的那道缝里,品味就住在那里。

工作是学会给那道缝命名:

  • 精确

  • 正确

  • 准确

就这样。这就是品味的全部。

品味三要素:好奇心、判断、标准

当把大部分时间用在磨练观察与看见的能力上,品味成长最快。

  • 好奇心(反灵感): 好奇心不是喜欢灵感,而是追问灵感到底是什么。

  • 判断: 判断是选择与拒绝同时发生的动作。

  • 标准: 标准是你不会跨过去的线。

最刺痛人的真相是,如果你没有标准,AI 会替你提供标准。

AI 的标准是文化平均审美。

快速练出品味的 75–25 法则

每天 10 分钟的练习

每天最简单的一种练习,只要 10 分钟(如果只做一件事),就是这样:

  1. 选一个输出(一段文字、一个落地页,或一条推文),用 AI 生成 10 个版本。

  2. 给每个版本写一行批评,用这句话开头:失败在于……

  3. 用一个约束重写其中一个版本(例如:不许用形容词、不许用渐变、只讲一个观点,按媒介来定)。

  4. 发布,因为这个循环比再多看点灵感更能快速把你练出来。

最后的转变

AI 之前,你花 10 小时做出来的东西可能很平庸,但因为它需要付出大量辛苦,就会被当成质量过关。

随着 AI 的出现,过程从根本上变得更容易了。

现在,艰难的过程再也不能当作平庸质量的借口,而品味比以往任何时候都更重要。

不要让艰难的过程成为糟糕结果的借口。-Sam Altman

AI can build you a $100M startup in a weekend

AI 能在一个周末帮你造出一家估值 1 亿美元的创业公司

What it cannot help you do is

但它帮不了你的,是

**HELP YOU MAKE TASTEFUL CONTENT

**帮你做出有品味的内容

Here is how to not make AI slop in 2026 **

下面就是 2026 年如何不做出 AI 垃圾 **

Taste is the new core skill

品味是新的核心技能

It all began with Brockman's 5-word tweet, which went viral

一切都始于 Brockman 那条五词推文,它迅速刷屏

  • The matter of fact is that execution is commoditized, curation has become scarce.
  • 事实是,执行已经商品化,筛选与策展变得稀缺。
  • There is an implied trap here, the trap being that taste is some sort of a passive and static credential.
  • 这里暗藏一个陷阱:把品味当成一种被动、静态的资质。
  • Real taste is often just practice.
  • 真正的品味往往只是练出来的。
  • It atrophies or it goes out of style when the person stops building reading and stops observing things
  • 当一个人不再创作、不再阅读、不再观察时,品味会萎缩,或变得过时。
  • I think that they just become their opinions, and those are very different.
  • 到那时,只剩下观点了,而观点和品味是两回事。

The meme responses were merciless pointing to the fact

梗图式的回复毫不留情,指出一个事实

that silicon valley is full of people with strong opinions about fonts, and little else besides words and quarter-zips.

硅谷到处都是对字体有强烈意见的人,除此之外,只有一堆话和半拉链抓绒衫。

What taste is (and what kills it)

什么是品味(以及它怎么被杀死)

Taste is distinction under uncertainty.

品味是在不确定中做出区分。

It is to walk into a room of ten things and know which one is different, to know exactly why, and then articulate it in a way that changes how we see it.

走进一个摆着十样东西的房间,你能知道哪一样不一样,能准确说出为什么,然后用一种会改变我们看法的方式把它讲清楚。

Jobs had that. Rubin has that. And neither could turn it into a framework, because frameworks are for stable worlds.

乔布斯有这个。里克·鲁宾也有。可他们都无法把它变成框架,因为框架是给稳定世界准备的。

Taste lives in moving ones. The moment you formalize it, you fossilize it.

品味活在流动的世界里。你一把它形式化,它就化石化了。

Thiel lens: taste as elimination, not addition

从蒂尔的视角看:品味是删减,不是加法

Thiel's real insight on competition really applies here: the world often rewards the last movers, not the first. In this context:

蒂尔对竞争的真正洞见在这里同样适用:世界常常奖励最后出手的人,而不是第一个。在这个语境里:

  1. The person with taste isn't always the first one to notice something is good;
  1. 有品味的人不一定是最先发现某样东西好的人;
  1. They're often the last person to notice it so precisely and completely that nothing needs to be fundamentally added.
  1. 他们往往是最后一个发现的人,但他们发现得精确而彻底,以至于不需要再从根本上添加什么;
  1. It is more about elimination of the tasteless rather than tasteless addition.
  1. 重点更像是在剔除无品味,而不是无品味地堆砌。

Basic B***h Taste

基础款 B***h 品味

AI made the execution of designing, building, and shipping light speed, but the current AI models, by their definitions

AI 让设计、搭建、上线的执行速度达到光速,但按当前 AI 模型的定义

give us something which I call **statistical taste. **

给出的,是我称为 **统计式品味。 ** 的东西。

That’s my name for the default look and feel AI produces when it’s trained on massive data sets.

这是我给 AI 在海量数据集上训练后,默认产出的观感与气质起的名字。

It is just average. It is the safest version, or the most optimal version, of good, and in 2026 safe-good is everywhere. We have seen it:

它就是平均值。它是好里最安全的版本,或者说最优化的版本,而在 2026 年,这种稳妥的好无处不在。已经见过它:

  • the same purple-to-blue gradient on white-coded websites
  • 白底网站上那种一模一样的紫到蓝渐变
  • the same bold claim plus three bullets per CTA layout
  • 同样的大胆主张加上每个 CTA 三条要点的版式
  • the polite and corporate voicing in everybody’s emails
  • 每个人邮件里那种客气、企业化的语气

AI did not kill taste. It made the average way more accessible.

AI 没有杀死品味。它只是让平均水平变得更容易拿到。

Escaping the Mean

逃离均值

The real problem today isn’t AI slop anymore. The real problem is that most people will use AI and still never build taste, because they stop at the average.

今天真正的问题已经不是 AI 垃圾了。真正的问题是,大多数人会用 AI,却依然永远练不出品味,因为他们停在了平均值。

  • You can reach the top 75% and be “just good.”
  • 你可以做到前 75%,也就只是 还不错。
  • “Good” has become accessible to everybody.
  • 好变成了人人可得。
  • So every bit of delta in the next 25% matters more : the part that separates statistical taste from curated taste.
  • 所以,接下来 25% 里的每一点 增量 ** 都更重要:它把 统计式品味策展式品味** 分开。

In this age, we’ll label the world in extremes:

在这个时代,人们会用极端标签给世界分类:

  • 75% and below -> AI slop
  • 75% 及以下 -> AI 垃圾
  • Above 75% -> masterpieces
  • 高于 75% -> 杰作

We are living in the age of extremes. This guide is about escaping the average.

我们活在一个极端时代。这份指南讲的是怎么逃离平均。

Why AI Makes “Safe-Good” So Easy

为什么 AI 让“稳妥的好”如此容易

AI made generic content easy because it does the three things which are required to create what I call culturally averaged aesthetics:

AI 之所以让泛化内容变得容易,是因为它做了三件事,而这三件事正是创造我称为文化平均审美所必需的:

  1. It predicts what usually works.
  1. 它预测什么通常有效。
  1. It reproduces patterns people have been rewarded for before.
  1. 它复刻人们曾经被奖励过的模式。
  1. It compresses thousands of references into one safe-good output.
  1. 它把上千个参考压缩成一个稳妥的好输出。

That is why every first output often looks like an app store landing page, or a Medium blog post, or an average hero section on a website. It is not terrible, but it is predictable, and being predictable is the enemy of taste.

所以你的第一版输出往往看起来像应用商店落地页,或 Medium 博客文章,或某个网站上平均水平的首屏。它不糟,但可预测,而可预测是品味的敌人。

The Shift: The Average Is the New Mediocre

转变:平均成了新的平庸

The real shift in 2026 is that the average is the new mediocre.

2026 年真正的转变是,平均成了新的平庸。

  • Before AI, if something was mediocre, it was because the maker lacked the skill.
  • AI 之前,东西平庸,是因为创作者缺技能。
  • After AI, if something is mediocre, it’s usually because the maker stopped early.
  • AI 之后,东西平庸,往往是因为创作者太早停手。

AI raised the floor, but the ceiling is higher than ever.

AI 抬高了地板,但天花板比以往更高。

  • Taste becomes more important because AI made the middle crowded.
  • 品味更重要,是因为 AI 让中间层挤满了人。
  • Now being above average is not enough.
  • 现在只做到高于平均已经不够了。

Everyone can ship a 7/10.

人人都能交付一个 7/10。

  • The only way to stand out is to become a 9/10 in judgment, a 9/10 in selection, and a 9/10 in specificity.
  • 唯一能脱颖而出的方式,是把判断做到 9/10,把选择做到 9/10,把具体性做到 9/10。
  • And have real authentic perspective.
  • 还要有真实、真诚的视角。

AI Has No Taste (That’s Why It Helps You Build Yours)

AI 没有品味(所以它反而能帮你练出自己的)

The most uncomfortable truth that people come to face is that the AI fundamentally lacks taste.

人们最不舒服、但迟早要面对的真相是,AI 在根本上缺乏品味。

It is the greatest mirror; it will always mirror the user, because it forces a question you could avoid before:

它是最好的镜子,会永远映照用户,因为它逼你回答一个以前可以回避的问题:

“Why is this wrong?”

为什么这不对?

Before AI, mediocre work took time to produce. Effort always created noise that you could confuse with hard work or quality.

AI 之前,平庸作品的产出需要时间。 努力总会制造噪音,你可以把噪音误当成辛苦、或误当成质量。

AI removed that noise.

AI 把噪音拿走了。

Now you can generate 10 different versions of the same AI slop in 10 minutes.

现在你可以在 10 分钟里生成同一份 AI 垃圾的 10 个不同版本。

The gap between what AI produces and what you actually want is where your taste lives.

AI 产出和你真正想要之间的差距,就是你的品味所在。

Most people will always avoid that gap.

大多数人会一直回避这个差距。

People with taste have to learn to name it.

有品味的人必须学会把它说出来。

The Core Loop: Generate, Then Destroy

核心循环:先生成,再摧毁

This is how you use AI to build taste.

这就是用 AI 练品味的方法。

You generate, and then destroy.

先生成,然后摧毁。

  • You produce 10 to 20 versions of anything.
  • 对任何东西先做 10 到 20 个版本。
  • You don’t use it to pick the best one.
  • 不是用它来选出最好那一个。
  • You use it to develop a rejection vocabulary.
  • 是用它来训练拒绝的词汇。

The muscle that you are fundamentally training is: “This fails because X.”

你真正训练的肌肉是:失败在于 X。

  • X is structural, not cosmetic.
  • X 是结构性的,不是表面装饰性的。

Do that enough times, and you’ve internalized a framework you couldn’t have articulated beforehand.

做得足够多,你就会把一个此前说不清的框架内化进身体里。

Find the Canon, Then Ignore the Canon

找到经典,然后无视经典

Use AI to find the canon. Then ignore the canon.

用 AI 找到经典,然后无视经典。

Ask AI to map the best work in any field across the last 100, 200, or 300 years.

让 AI 画出任何领域在过去 100 年、200 年或 300 年里的最佳作品谱系。

  • Read it.
  • 去读。
  • Watch it.
  • 去看。
  • Absorb it.
  • 去吸收。

Use deep research not to copy it, but to understand why it worked there

用深度研究不是为了抄,而是为了理解它为什么在当时能成立

Minimax-M2.5 is my personal choice :) You can use anything which works

Minimax-M2.5 是个人选择 :) 用什么能用都行

Curiosity is the only differentiator today between average taste and curated taste.

今天,平均品味和策展品味之间,唯一的分水岭是好奇心。

  • The more you ask these incredibly powerful models, the more you will dive deep in.
  • 你问这些强大模型的问题越多,你就会越往深处钻。

Most people are willing to be stuck at average.

大多数人愿意卡在平均值。

Hence why they will never ask those questions to go make that curated masterpiece of software, writing, design anything.

所以他们永远不会去问那些问题,也就永远做不出那种策展级的软件、写作、设计,或者任何东西的杰作。

Make With Stakes

带着赌注去做

I got inferred insight from Will Manidis's article "Against Taste"

从 Will Manidis 的文章 Against Taste 里推出来一个启发

Taste that never risks anything and stays theoretical is passive.

不冒任何风险、只停留在理论里的品味,是被动的。

Publish the essay. Ship the product. Present the deck.

把文章发出去。把产品上线。把路演稿讲出来。

  • The feedback loop of real output is irreplaceable.
  • 真实产出的反馈回路不可替代。
  • People respond, or don’t.
  • 人们会回应,或不回应。

AI can accelerate everything except good old-fashioned feedback.

AI 能加速一切,除了那种老派的反馈。

The lightning execution of AI has allowed us to ship and publish at light speed.

AI 的闪电式执行让发布与公开都变得光速。

  • And that has allowed us to accelerate our taste.
  • 也因此让品味加速成长。
  • The fundamental prerequisite for taste is knowing what rules you are willing to break.
  • 品味的根本前提,是知道哪些规则愿意打破。

And you cannot break rules which you do not know.

你不可能打破自己不知道的规则。

The Specificity Test

具体性测试

Every time AI generates a version, ask the question:

每次 AI 生成一个版本,都问一句:

  • Could this have been written about anything else?
  • 这段话能不能套在别的东西上?

If yes ; if you could swap the exact subject out and it would change absolutely nothing - it has no fundamental taste.

如果能;如果把具体对象换掉,内容却完全不变,那它就没有根本的品味。

Taste, by its definition, is specific.

品味按定义就是具体的。

Rick Rubin’s production on *Californication * sounds like nothing else.

里克·鲁宾在 *Californication * 上的制作听起来独一无二。

  • Because it was made for those songs.
  • 因为它就是为那些歌做的。
  • At that particular moment.
  • 在那个特定时刻。
  • By that group of people.
  • 由那一群人完成。

By that logic, taste is irreducibly contextual.

按这个逻辑,品味是不可约的语境产物。

Resistance as a Signal

抵触感就是信号

When something AI produces bothers you, and you cannot understand why, don’t move on.

当 AI 产出的东西让你不舒服,但你又说不清为什么,不要跳过。

  • Sit with that friction.
  • 和那种摩擦感待在一起。
  • That is the raw material of taste.
  • 那就是品味的原材料。

Most people will scroll past it.

大多数人会直接滑过去。

But the person who stops, and interrogates, and asks themselves what is exactly wrong here is actually doing the hard work.

但那个会停下来、会审问、会问自己到底哪里不对的人,其实正在做最难的工作。

Over time, those cumulative moments become your aesthetic immune system:

久而久之,这些累积的瞬间会变成你的审美免疫系统:

  • unexplained discomfort
  • 说不清的不适
  • uncertainty
  • 不确定
  • lack of understanding
  • 不理解

Taste vs “Better Than Humans” Taste

品味 vs “比人类更好”的品味

The deeper, uncomfortable truth about taste is that AI will eventually have better taste than most humans at most things.

关于品味更深、更不舒服的真相是,AI 终将会在大多数事情上拥有比大多数人更好的品味。

  • This is not because taste is mechanical; it isn’t.
  • 这不是因为品味是机械的,它不是。
  • It’s because taste is trainable on human output.
  • 而是因为品味可以从人类产出里被训练出来。

What AI cannot replace is taste with a perspective.

AI 无法替代的,是带着视角的品味。

Rick Rubin doesn’t just know what sounds good.

里克·鲁宾不只是知道什么好听。

  • He knows what sounds good given what he believes music is for.
  • 他知道在他相信音乐应该服务于什么的前提下,什么才算好听。
  • And that belief is not derivable from a corpus of data sets.
  • 而这种信念不可能从数据集语料里推导出来。
  • It comes from active living.
  • 它来自真实地生活。
  • From choosing.
  • 来自选择。
  • From loss.
  • 来自失去。

Taste is a product of life, and not a data set.

品味是生活的产物,不是数据集。

The answer to how you build taste using AI is to use AI to strip everything that is in taste.

如何用 AI 练品味的答案,是用 AI 把品味里的一切都剥离出来。

  • Let it produce the competent.
  • 让它产出称职的。
  • The average.
  • 平均的。
  • And the defensible.
  • 站得住脚的。

And in that gap between what it produces and what you actually want is where taste lives.

而在它产出和你真正想要之间的那道缝里,品味就住在那里。

The work is learning to name that gap:

工作是学会给那道缝命名:

  • precisely
  • 精确
  • correctly
  • 正确
  • accurately
  • 准确

That’s it. That’s the whole thing for taste.

就这样。这就是品味的全部。

Taste Triad: Curiosity, Judgment, Standards

品味三要素:好奇心、判断、标准

Your taste grows fastest when you spend most of your time sharpening your ability to see and observe.

当把大部分时间用在磨练观察与看见的能力上,品味成长最快。

  • Curiosity (anti-inspiration): Curiosity is not liking inspiration. It is interrogating what is inspiration.
  • 好奇心(反灵感): 好奇心不是喜欢灵感,而是追问灵感到底是什么。
  • Judgment: Judgment is the act of selection and rejection at the same time.
  • 判断: 判断是选择与拒绝同时发生的动作。
  • Standards: Standards are the lines you won’t cross.
  • 标准: 标准是你不会跨过去的线。

The truth, which hurts most people, is that if you don’t have standards, AI will supply them for you.

最刺痛人的真相是,如果你没有标准,AI 会替你提供标准。

AI standards are culturally averaged aesthetics.

AI 的标准是文化平均审美。

The 75–25 Rule for Building Taste Fast

快速练出品味的 75–25 法则

A 10-Minute Daily Practice

每天 10 分钟的练习

One of the simplest daily practices that I do, for 10 minutes (if you do only one thing), is this:

每天最简单的一种练习,只要 10 分钟(如果只做一件事),就是这样:

  1. Pick one output (a paragraph, a landing page, or a tweet) and generate 10 versions with AI.
  1. 选一个输出(一段文字、一个落地页,或一条推文),用 AI 生成 10 个版本。
  1. Write one critique line for each version using the phrase “fails because…”
  1. 给每个版本写一行批评,用这句话开头:失败在于……
  1. Rewrite one version using one constraint (for example: no adjectives, no gradients, one idea, depending on the medium).
  1. 用一个约束重写其中一个版本(例如:不许用形容词、不许用渐变、只讲一个观点,按媒介来定)。
  1. Publish, because that loop will build faster than more inspiration.
  1. 发布,因为这个循环比再多看点灵感更能快速把你练出来。

The Final Shift

最后的转变

Before AI, what took you 10 hours could be mediocre work that got passed off as quality because of the sheer hard work it required.

AI 之前,你花 10 小时做出来的东西可能很平庸,但因为它需要付出大量辛苦,就会被当成质量过关。

With the advent of AI, the process has fundamentally become easier.

随着 AI 的出现,过程从根本上变得更容易了。

Now no longer can hard process be used as an excuse for mediocre quality and taste matters more than ever.

现在,艰难的过程再也不能当作平庸质量的借口,而品味比以往任何时候都更重要。

“Do not let hard process excuse bad results.” -Sam Altman

不要让艰难的过程成为糟糕结果的借口。-Sam Altman

AI can build you a $100M startup in a weekend

What it cannot help you do is

**HELP YOU MAKE TASTEFUL CONTENT

Here is how to not make AI slop in 2026 **

Taste is the new core skill

It all began with Brockman's 5-word tweet, which went viral

  • The matter of fact is that execution is commoditized, curation has become scarce.

  • There is an implied trap here, the trap being that taste is some sort of a passive and static credential.

  • Real taste is often just practice.

  • It atrophies or it goes out of style when the person stops building reading and stops observing things

  • I think that they just become their opinions, and those are very different.

The meme responses were merciless pointing to the fact

that silicon valley is full of people with strong opinions about fonts, and little else besides words and quarter-zips.

What taste is (and what kills it)

Taste is distinction under uncertainty.

It is to walk into a room of ten things and know which one is different, to know exactly why, and then articulate it in a way that changes how we see it.

Jobs had that. Rubin has that. And neither could turn it into a framework, because frameworks are for stable worlds.

Taste lives in moving ones. The moment you formalize it, you fossilize it.

Thiel lens: taste as elimination, not addition

Thiel's real insight on competition really applies here: the world often rewards the last movers, not the first. In this context:

  1. The person with taste isn't always the first one to notice something is good;

  2. They're often the last person to notice it so precisely and completely that nothing needs to be fundamentally added.

  3. It is more about elimination of the tasteless rather than tasteless addition.

Basic B***h Taste

AI made the execution of designing, building, and shipping light speed, but the current AI models, by their definitions

give us something which I call **statistical taste. **

That’s my name for the default look and feel AI produces when it’s trained on massive data sets.

It is just average. It is the safest version, or the most optimal version, of good, and in 2026 safe-good is everywhere. We have seen it:

  • the same purple-to-blue gradient on white-coded websites

  • the same bold claim plus three bullets per CTA layout

  • the polite and corporate voicing in everybody’s emails

AI did not kill taste. It made the average way more accessible.

Escaping the Mean

The real problem today isn’t AI slop anymore. The real problem is that most people will use AI and still never build taste, because they stop at the average.

  • You can reach the top 75% and be “just good.”

  • “Good” has become accessible to everybody.

  • So every bit of delta in the next 25% matters more : the part that separates statistical taste from curated taste.

In this age, we’ll label the world in extremes:

  • 75% and below -> AI slop

  • Above 75% -> masterpieces

We are living in the age of extremes. This guide is about escaping the average.

Why AI Makes “Safe-Good” So Easy

AI made generic content easy because it does the three things which are required to create what I call culturally averaged aesthetics:

  1. It predicts what usually works.

  2. It reproduces patterns people have been rewarded for before.

  3. It compresses thousands of references into one safe-good output.

That is why every first output often looks like an app store landing page, or a Medium blog post, or an average hero section on a website. It is not terrible, but it is predictable, and being predictable is the enemy of taste.

The Shift: The Average Is the New Mediocre

The real shift in 2026 is that the average is the new mediocre.

  • Before AI, if something was mediocre, it was because the maker lacked the skill.

  • After AI, if something is mediocre, it’s usually because the maker stopped early.

AI raised the floor, but the ceiling is higher than ever.

  • Taste becomes more important because AI made the middle crowded.

  • Now being above average is not enough.

Everyone can ship a 7/10.

  • The only way to stand out is to become a 9/10 in judgment, a 9/10 in selection, and a 9/10 in specificity.

  • And have real authentic perspective.

AI Has No Taste (That’s Why It Helps You Build Yours)

The most uncomfortable truth that people come to face is that the AI fundamentally lacks taste.

It is the greatest mirror; it will always mirror the user, because it forces a question you could avoid before:

“Why is this wrong?”

Before AI, mediocre work took time to produce. Effort always created noise that you could confuse with hard work or quality.

AI removed that noise.

Now you can generate 10 different versions of the same AI slop in 10 minutes.

The gap between what AI produces and what you actually want is where your taste lives.

Most people will always avoid that gap.

People with taste have to learn to name it.

The Core Loop: Generate, Then Destroy

This is how you use AI to build taste.

You generate, and then destroy.

  • You produce 10 to 20 versions of anything.

  • You don’t use it to pick the best one.

  • You use it to develop a rejection vocabulary.

The muscle that you are fundamentally training is: “This fails because X.”

  • X is structural, not cosmetic.

Do that enough times, and you’ve internalized a framework you couldn’t have articulated beforehand.

Find the Canon, Then Ignore the Canon

Use AI to find the canon. Then ignore the canon.

Ask AI to map the best work in any field across the last 100, 200, or 300 years.

  • Read it.

  • Watch it.

  • Absorb it.

Use deep research not to copy it, but to understand why it worked there

Minimax-M2.5 is my personal choice :) You can use anything which works

Curiosity is the only differentiator today between average taste and curated taste.

  • The more you ask these incredibly powerful models, the more you will dive deep in.

Most people are willing to be stuck at average.

Hence why they will never ask those questions to go make that curated masterpiece of software, writing, design anything.

Make With Stakes

I got inferred insight from Will Manidis's article "Against Taste"

Taste that never risks anything and stays theoretical is passive.

Publish the essay. Ship the product. Present the deck.

  • The feedback loop of real output is irreplaceable.

  • People respond, or don’t.

AI can accelerate everything except good old-fashioned feedback.

The lightning execution of AI has allowed us to ship and publish at light speed.

  • And that has allowed us to accelerate our taste.

  • The fundamental prerequisite for taste is knowing what rules you are willing to break.

And you cannot break rules which you do not know.

The Specificity Test

Every time AI generates a version, ask the question:

  • Could this have been written about anything else?

If yes ; if you could swap the exact subject out and it would change absolutely nothing - it has no fundamental taste.

Taste, by its definition, is specific.

Rick Rubin’s production on *Californication * sounds like nothing else.

  • Because it was made for those songs.

  • At that particular moment.

  • By that group of people.

By that logic, taste is irreducibly contextual.

Resistance as a Signal

When something AI produces bothers you, and you cannot understand why, don’t move on.

  • Sit with that friction.

  • That is the raw material of taste.

Most people will scroll past it.

But the person who stops, and interrogates, and asks themselves what is exactly wrong here is actually doing the hard work.

Over time, those cumulative moments become your aesthetic immune system:

  • unexplained discomfort

  • uncertainty

  • lack of understanding

Taste vs “Better Than Humans” Taste

The deeper, uncomfortable truth about taste is that AI will eventually have better taste than most humans at most things.

  • This is not because taste is mechanical; it isn’t.

  • It’s because taste is trainable on human output.

What AI cannot replace is taste with a perspective.

Rick Rubin doesn’t just know what sounds good.

  • He knows what sounds good given what he believes music is for.

  • And that belief is not derivable from a corpus of data sets.

  • It comes from active living.

  • From choosing.

  • From loss.

Taste is a product of life, and not a data set.

The answer to how you build taste using AI is to use AI to strip everything that is in taste.

  • Let it produce the competent.

  • The average.

  • And the defensible.

And in that gap between what it produces and what you actually want is where taste lives.

The work is learning to name that gap:

  • precisely

  • correctly

  • accurately

That’s it. That’s the whole thing for taste.

Taste Triad: Curiosity, Judgment, Standards

Your taste grows fastest when you spend most of your time sharpening your ability to see and observe.

  • Curiosity (anti-inspiration): Curiosity is not liking inspiration. It is interrogating what is inspiration.

  • Judgment: Judgment is the act of selection and rejection at the same time.

  • Standards: Standards are the lines you won’t cross.

The truth, which hurts most people, is that if you don’t have standards, AI will supply them for you.

AI standards are culturally averaged aesthetics.

The 75–25 Rule for Building Taste Fast

A 10-Minute Daily Practice

One of the simplest daily practices that I do, for 10 minutes (if you do only one thing), is this:

  1. Pick one output (a paragraph, a landing page, or a tweet) and generate 10 versions with AI.

  2. Write one critique line for each version using the phrase “fails because…”

  3. Rewrite one version using one constraint (for example: no adjectives, no gradients, one idea, depending on the medium).

  4. Publish, because that loop will build faster than more inspiration.

The Final Shift

Before AI, what took you 10 hours could be mediocre work that got passed off as quality because of the sheer hard work it required.

With the advent of AI, the process has fundamentally become easier.

Now no longer can hard process be used as an excuse for mediocre quality and taste matters more than ever.

“Do not let hard process excuse bad results.” -Sam Altman

📋 讨论归档

讨论进行中…