我的征尘是星辰大海。。。

The dirt and dust from my pilgrimage forms oceans of stars...

-------当记忆的篇章变得零碎，当追忆的图片变得模糊，我们只能求助于数字存储的永恒的回忆

作者:黄教授

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AI对社会分工的冲击与思考

视频

音频

原始脚本

星辰与顽石，AI 浪潮下社会分工的永恒割裂与时代新困局。
蒋多多星辰落地化为顽石的比喻，精准戳破一套贯穿教育 AI 评测。
人类社会分工的根本矛盾，人人向往无拘无束、自由创造的星辰式角色，可维持社会运转的大量标准化约束型顽石工作。
终究需要人或工具承接。
人类社会天然成金字塔结构，自由创意类岗位仅占顶层极小份额，无法容纳全体人口。
过去依靠劳动力市场供需关系强制调和大众的理想期待与谋生所需的现实工作，勉强维系分工平衡。
但大模型与自动化的普及正在大规模替代原本容纳普通人的标准化执行岗位。
旧有的供需调节机制濒临失效，大众的职业期待、个人天赋与社会岗位供给之间的撕裂持续放大。
这是 AI 时代所有人共同面对的结构性难题。
目前尚无完美的化解方案。
一、星辰与顽石，一套二元评价体系的释义。
以蒋多多事件为样本，2006年河南考生蒋多多。
是当年极具争议的高考0分考生。
他反感高考唯分数、标准答案束缚个性的制度，考试全程拒绝规范作答，最终卷面成绩归零。
他留下一句极具象征性的比喻，星星高悬天际时自在闪光，一旦坠落大地，就沦为毫无灵气的石头。
这句话划分出两种完全相悖的成长评价模式。
其一天际星辰，无约束发散创造。
不存在统一标准答案，不限定输出框架，允许个体顺着自身天赋、灵感自由表达。
对应人类的文学原创、哲学思辨、先锋艺术。
对应 AI 开放式随笔、自由绘画等创意任务。
这套体系能释放独特天赋，却存在致命短板，评价高度依赖主观审美。
没有可量化统一通用的打分标准。
正如抽象现代艺术，好坏全凭个人感受，难以建立具备公信力的客观评判规则。
其二，地上玩石，强约束收敛执行。
拥有清晰任务目标，统一评判标准，自由度被严格限制，偏离规则。
达不到指标即判定失效。
人类的应试考试、流水线岗位、标准化文职、 AI 的数学运算、代码调试、数据规整、标准化客服都属于此类。
长期局限在这套体系内，发散创意会逐步萎缩。
但优势十分明确，对错、完成度、效率存在客观边界，可批量、公平的量化评测。
星辰与顽石本身无高低贵贱，只是适配两种不同社会职能，但二者无法单独存在。
一切天马行空的创意，最终都要依靠标准化执行落地。
缺少完实式的落地工作，再耀眼的星辰构想也无法产生实际社会价值。
蒋多多本人正是两种评价体系能力割裂的典型。
面对高考收敛式标准化试卷，他拒绝配合规则，考核结果近乎0分。
若给予完全自由的文字观点表达空间，他的思辨与文字天赋能够充分发挥，完全可以拿到高分。
这一现象同样复刻在当下大模型评测领域。
部分模型创意生成灵动流畅，标准化梳理工具任务跑分惨淡，也有模型统考基准分数遥遥领先。
开放式创作僵硬乏味，这道二元矛盾延伸出评测体系的固有缺陷。
收敛执行类工作可以搭建统一标准化评测，高考。
大模型通用基准数据集实现横向公平对比。
自由发散类创造无法量化打分，只能依靠市场口碑、付费意愿做滞后的主观校验。
两套评价机制只能互补，无法相互替代。
二、大模型产业现实，AI注定承接绝大多数。
顽石型工具任务从产业需求来看。
人类研发大模型的核心定位是工具执行者，而非自由创意主体。
这直接决定 AI 会包揽绝大多数低自由度的收敛工作。
自由畅想、无边界创意是人类主动把控且乐于亲自完成的顶层活动。
如同企业管理者可以随意抛出天马行空的战略构想，属于轻松的新城工作。
而拆解需求、落地方案、处理重复繁琐事务等约束型执行任务，是难度更高、无人主动愿意承担的顽石工作。
最终会交由下属自动化工具、大模型完成。
社会金字塔分工天然存在总量差异，顶层纯创意、战略、原创人文岗位数量极其稀缺。
高度依赖不可复制的独特天赋，无法批量供给。
中层、底层海量岗位以重复计算、文本整理、标准化流程代办等收敛型工作为主。
任务目标清晰，适配自动化处理。
人类普遍排斥受约束、重复性的底层工作，AI自然填补这一岗位缺口。
旧时代依靠大量普通人填充标准化岗位的分工平衡被打破。
催生第一层时代矛盾，所有人都向往稀缺的星辰岗位，可顶层空间容量有限，大量人群的自我期待与现实岗位供给天然错位。
同时，当下主流大模型评测机制和高考、古代科举具备完全一致的底层逻辑与同源弊病。
统一基准评测如同高考统考。
所有模型使用同一套测试数据集打分，抹平研发团队参数量、算力投入的差距，实现横向公平对比，是算力、数据资源稀缺阶段成本最低、最客观的筛选工具。
如同古代科举打破血统贵族垄断，为底层人才提供上升通道。
但标准化标尺必然催生应试模型，大量厂商针对主流基准数据及定向微调，甚至将试题纳入训练集，实现泄题刷题。
最终榜单分数亮眼，真实场景泛化能力严重注水，和应试学生死记标准答案、脱离现实解决问题的弊病如出一辙。
行业只能依靠动态更新、离线测试集、人工生成全新考题、增加分布外对抗试题、抬高应试成本，无法彻底根除刷分现象。
只能缓解单一分数带来的评价失真。
标准化评测不可或缺，却不能作为唯一评判标准。
若彻底抛弃统一基准，各家厂商仅使用自有业务数据自测。
行业会失去通用客观对比标准，中小模型失去公平展示渠道，最终形成资本主导评价话语权的无序格局。
若只依靠基准分数优化模型，则会持续催生应试化过拟合。
落地实际业务时频繁翻车。
行业当下采取折中改良路线调和矛盾，一方面持续动态更新评测题库，拆分多维度细分指标。
破除单一总分维分数论。
另一方面打通自动化基准快速迭代，线上真实用户反馈两条数据飞轮，用低成本标准化测试完成短周期迭代筛选。
依靠长期真实业务数据校准模型真实能力，兼顾公平性与落地实用性。
三、 AI 时代的结构性危机。
旧劳动力供需平衡机制彻底失效。
在 AI 普及之前，社会依靠劳动力市场供需自发调节分工平衡。
彼时存在海量标准化底层岗位，能够容纳不擅长创意、适配收敛执行工作的普通人。
而排斥规则、拥有发散天赋的个体。
可流向文创、手工、人文服务等多元小众赛道。
薪资、岗位竞争压力会客观约束大众过高的职业期待，迫使多数人为谋生接受自身并不喜爱的约束性工作。
客观上匹配社会金字塔的分工需求，维持整体运转。
自动化与大模型的大规模落地，持续替代传统标准化底层岗位。
原本容纳大量普通人群的顽石赛道不断收缩，人类可选择的工作赛道被压缩为两类。
第一类是容量极小的顶层星辰岗位。
纯创意、深度战略、原创思辨赛道，竞争极度激烈。
第二类是 AI 无法替代，高度依赖人类共情、复杂人际博弈、临场灵活判断的特殊服务岗位。
由此产生群体性的身份撕裂。
大量普通人既没有顶尖天赋竞争稀缺的创意岗位，又失去了原本赖以生存的标准化执行工作。
而类似蒋多多，擅长自由发散却排斥规则约束的群体，在以收敛任务为主的社会评价体系中，长期得不到认可。
社会又无法供给足够多创意岗位承接这类人群。
这一困境无法依靠单一手段快速解决，根源在于旧有的劳动力供需平衡机制已经被 AI 打破。
新的分工分配体系尚未成型。

修正脚本

星辰与顽石，AI 浪潮下社会分工的永恒割裂与时代新困局。
蒋多多星辰落地化为顽石的比喻，精准戳破一套贯穿教育 AI 评测、人类社会分工的根本矛盾，人人向往无拘无束、自由创造的星辰式角色，可维持社会运转的大量标准化约束型顽石工作，终究需要人或工具承接。
人类社会天然成金字塔结构，自由创意类岗位仅占顶层极小份额，无法容纳全体人口。
过去依靠劳动力市场供需关系强制调和大众的理想期待与谋生所需的现实工作，勉强维系分工平衡。
但大模型与自动化的普及正在大规模替代原本容纳普通人的标准化执行岗位。
旧有的供需调节机制濒临失效，大众的职业期待、个人天赋与社会岗位供给之间的撕裂持续放大。
这是 AI 时代所有人共同面对的结构性难题。
目前尚无完美的化解方案。
一、星辰与顽石，一套二元评价体系的释义。
以蒋多多事件为样本，2006年河南考生蒋多多，是当年极具争议的高考0分考生。
他反感高考唯分数、标准答案束缚个性的制度，考试全程拒绝规范作答，最终卷面成绩归零。
他留下一句极具象征性的比喻，星星高悬天际时自在闪光，一旦坠落大地，就沦为毫无灵气的石头。
这句话划分出两种完全相悖的成长评价模式。
其一，天际星辰，无约束发散创造。
不存在统一标准答案，不限定输出框架，允许个体顺着自身天赋、灵感自由表达。
对应人类的文学原创、哲学思辨、先锋艺术。
对应 AI 开放式随笔、自由绘画等创意任务。
这套体系能释放独特天赋，却存在致命短板，评价高度依赖主观审美。
没有可量化统一通用的打分标准。
正如抽象现代艺术，好坏全凭个人感受，难以建立具备公信力的客观评判规则。
其二，地上顽石，强约束收敛执行。
拥有清晰任务目标，统一评判标准，自由度被严格限制，偏离规则、达不到指标即判定失效。
人类的应试考试、流水线岗位、标准化文职、 AI 的数学运算、代码调试、数据规整、标准化客服都属于此类。
长期局限在这套体系内，发散创意会逐步萎缩。
但优势十分明确，对错、完成度、效率存在客观边界，可批量、公平的量化评测。
星辰与顽石本身无高低贵贱，只是适配两种不同社会职能，但二者无法单独存在。
一切天马行空的创意，最终都要依靠标准化执行落地。
缺少顽石式的落地工作，再耀眼的星辰构想也无法产生实际社会价值。
蒋多多本人正是两种评价体系能力割裂的典型。
面对高考收敛式标准化试卷，他拒绝配合规则，考核结果近乎0分。
若给予完全自由的文字观点表达空间，他的思辨与文字天赋能够充分发挥，完全可以拿到高分。
这一现象同样复刻在当下大模型评测领域。
部分模型创意生成灵动流畅，标准化梳理工具任务跑分惨淡，也有模型统考基准分数遥遥领先，开放式创作僵硬乏味，这道二元矛盾延伸出评测体系的固有缺陷。
收敛执行类工作可以搭建统一标准化评测，高考、大模型通用基准数据集实现横向公平对比。
自由发散类创造无法量化打分，只能依靠市场口碑、付费意愿做滞后的主观校验。
两套评价机制只能互补，无法相互替代。
二、大模型产业现实，AI注定承接绝大多数顽石型工具任务。从产业需求来看，人类研发大模型的核心定位是工具执行者，而非自由创意主体。
这直接决定 AI 会包揽绝大多数低自由度的收敛工作。
自由畅想、无边界创意是人类主动把控且乐于亲自完成的顶层活动。
如同企业管理者可以随意抛出天马行空的战略构想，属于轻松的星辰工作。
而拆解需求、落地方案、处理重复繁琐事务等约束型执行任务，是难度更高、无人主动愿意承担的顽石工作。
最终会交由下属、自动化工具、大模型完成。
社会金字塔分工天然存在总量差异，顶层纯创意、战略、原创人文岗位数量极其稀缺。
高度依赖不可复制的独特天赋，无法批量供给。
中层、底层海量岗位以重复计算、文本整理、标准化流程代办等收敛型工作为主。
任务目标清晰，适配自动化处理。
人类普遍排斥受约束、重复性的底层工作，AI自然填补这一岗位缺口。
旧时代依靠大量普通人填充标准化岗位的分工平衡被打破。
催生第一层时代矛盾，所有人都向往稀缺的星辰岗位，可顶层空间容量有限，大量人群的自我期待与现实岗位供给天然错位。
同时，当下主流大模型评测机制和高考、古代科举具备完全一致的底层逻辑与同源弊病。
统一基准评测如同高考统考。
所有模型使用同一套测试数据集打分，抹平研发团队参数量、算力投入的差距，实现横向公平对比，是算力、数据资源稀缺阶段成本最低、最客观的筛选工具。
如同古代科举打破血统贵族垄断，为底层人才提供上升通道。
但标准化标尺必然催生应试模型，大量厂商针对主流基准数据集定向微调，甚至将试题纳入训练集，实现泄题刷题。
最终榜单分数亮眼，真实场景泛化能力严重注水，和应试学生死记标准答案、脱离现实解决问题的弊病如出一辙。
行业只能依靠动态更新、离线测试集、人工生成全新考题、增加分布外对抗试题、抬高应试成本，无法彻底根除刷分现象。
只能缓解单一分数带来的评价失真。
标准化评测不可或缺，却不能作为唯一评判标准。
若彻底抛弃统一基准，各家厂商仅使用自有业务数据自测。
行业会失去通用客观对比标准，中小模型失去公平展示渠道，最终形成资本主导评价话语权的无序格局。
若只依靠基准分数优化模型，则会持续催生应试化过拟合。
落地实际业务时频繁翻车。
行业当下采取折中改良路线调和矛盾，一方面持续动态更新评测题库，拆分多维度细分指标，破除单一总分唯分数论。
另一方面打通自动化基准快速迭代、线上真实用户反馈两条数据飞轮，用低成本标准化测试完成短周期迭代筛选。
依靠长期真实业务数据校准模型真实能力，兼顾公平性与落地实用性。
三、 AI 时代的结构性危机。
旧劳动力供需平衡机制彻底失效。
在 AI 普及之前，社会依靠劳动力市场供需自发调节分工平衡。
彼时存在海量标准化底层岗位，能够容纳不擅长创意、适配收敛执行工作的普通人。
而排斥规则、拥有发散天赋的个体，可流向文创、手工、人文服务等多元小众赛道。
薪资、岗位竞争压力会客观约束大众过高的职业期待，迫使多数人为谋生接受自身并不喜爱的约束性工作。
客观上匹配社会金字塔的分工需求，维持整体运转。
自动化与大模型的大规模落地，持续替代传统标准化底层岗位。
原本容纳大量普通人群的顽石赛道不断收缩，人类可选择的工作赛道被压缩为两类。
第一类是容量极小的顶层星辰岗位。
纯创意、深度战略、原创思辨赛道，竞争极度激烈。
第二类是 AI 无法替代，高度依赖人类共情、复杂人际博弈、临场灵活判断的特殊服务岗位。
由此产生群体性的身份撕裂。
大量普通人既没有顶尖天赋竞争稀缺的创意岗位，又失去了原本赖以生存的标准化执行工作。
而类似蒋多多，擅长自由发散却排斥规则约束的群体，在以收敛任务为主的社会评价体系中，长期得不到认可。
社会又无法供给足够多创意岗位承接这类人群。
这一困境无法依靠单一手段快速解决，根源在于旧有的劳动力供需平衡机制已经被 AI 打破。
新的分工分配体系尚未成型。

英文翻译

Stars and Stones: The Eternal Division of Labor under the AI Wave and the New Dilemmas of the Era.  
Jiang Duoduo's metaphor of stars falling to earth and turning into stones precisely punctures a fundamental contradiction that runs through educational AI evaluation and the division of labor in human society. Everyone yearns for the star-like role of unrestrained, free creation, yet the vast number of standardized, constraining stone-like jobs that sustain societal operations ultimately require humans or tools to take on.

Human society is naturally structured as a pyramid, with free creative positions occupying only a tiny fraction at the top, unable to accommodate the entire population.

In the past, the labor market's supply-demand relationship forcibly reconciled the public's ideal expectations with the real-world work needed for survival, barely maintaining a balance in the division of labor.

However, the proliferation of large models and automation is systematically replacing the standardized execution roles that once absorbed ordinary people.

The old supply-demand adjustment mechanism is on the verge of失效. The gap between the public's career expectations, individual talents, and the supply of social positions continues to widen.

This is a structural challenge faced by everyone in the AI era.

Currently, no perfect solution exists.

### I. Stars and Stones: An Interpretation of a Binary Evaluation System

Using the Jiang Duoduo incident as a case study: In 2006, Jiang Duoduo was a highly controversial candidate who scored zero on the college entrance exam.

He resented the system that prioritized scores and standard answers, which he believed constrained individuality. He refused to follow standard answering procedures throughout the exam, ultimately receiving a score of zero.

He left behind a highly symbolic metaphor: Stars shine freely when suspended high in the sky, but once they fall to earth, they become lifeless stones.

This metaphor delineates two completely opposing models of growth evaluation.

**First, the celestial star: unconstrained, divergent creation.**  
There is no unified standard answer, no prescribed output framework. Individuals are allowed to express themselves freely based on their talents and inspiration.  
This corresponds to human literary originality, philosophical speculation, and avant-garde art.  
It also corresponds to AI's open-ended essays, free drawing, and other creative tasks.

This system can unleash unique talents but has a fatal flaw: evaluation is highly dependent on subjective aesthetics.  
There is no quantifiable, universal scoring standard.  
Just like abstract modern art, quality is entirely based on personal feelings, making it difficult to establish credible, objective evaluation rules.

**Second, the earthly stone: strongly constrained, convergent execution.**  
Clear task objectives, unified evaluation standards, and strictly limited freedom. Deviation from rules or failure to meet indicators results in rejection.  
Human exam-based assessments, assembly-line jobs, standardized clerical work, and AI's mathematical operations, code debugging, data normalization, and standardized customer service all fall into this category.

Long-term confinement within this system causes divergent creativity to atrophy.  
But the advantages are clear: there are objective boundaries for right/wrong, completeness, and efficiency, allowing for批量 and fair quantitative evaluation.

Stars and stones themselves have no inherent hierarchy of value; they simply suit two different social functions. However, neither can exist alone.  
All wild creativity ultimately depends on standardized execution to materialize.  
Without stone-like落地 work, even the most brilliant star-like ideas cannot generate real social value.

Jiang Duoduo himself is a typical example of the rift between these two evaluation systems.  
Faced with the convergent, standardized college entrance exam, he refused to comply with the rules, resulting in a score near zero.  
If given complete freedom to express his views in writing, his speculative and literary talents could have flourished, and he could have achieved a high score.

This phenomenon is replicated in the current field of large model evaluation.  
Some models are agile and fluent in creative generation but perform poorly on standardized sorting tasks; others score far ahead on unified benchmark tests but are rigid and dull in open-ended creation. This binary contradiction reveals the inherent flaws in the evaluation system.

Convergent execution tasks can be evaluated with unified standardized tests: the college entrance exam and large model general benchmark datasets enable横向公平比较.  
Free, divergent creation cannot be quantitatively scored; it can only be subjectively validated through market口碑 and willingness to pay, with a time lag.  
The two evaluation mechanisms can only complement each other, not replace one another.

### II. The Industry Reality of Large Models: AI Is Destined to Undertake the Vast Majority of Stone-Type Tool Tasks

From an industry demand perspective, the core positioning of human-developed large models is as tool executors, not free creative subjects.  
This directly determines that AI will take on the vast majority of low-freedom convergent tasks.

Free thinking and unbounded creativity are top-level activities that humans actively control and enjoy completing themselves.  
Just like corporate managers can casually throw out bold strategic visions—this is the轻松 work of stars.  
But breaking down requirements, implementing plans, and handling repetitive, tedious tasks are constraint-based execution tasks that are more difficult and that no one willingly takes on—these are the work of stones.  
Ultimately, these tasks will be delegated to subordinates, automation tools, and large models.

The pyramid-like division of labor in society inherently has total quantity differences: the top-level positions for pure creativity, strategy, and original humanities are extremely scarce.  
They rely heavily on unique, unrepeatable talents and cannot be supplied in bulk.

The middle and bottom layers consist of vast numbers of positions dominated by convergent tasks such as repetitive calculations, text sorting, and standardized process handling.  
Task objectives are clear, making them suitable for automated processing.

Humans generally dislike constrained, repetitive low-level work, so AI naturally fills this gap.

The old division of labor, which relied on大量普通人 to fill standardized positions, has been disrupted.  
This gives rise to the first layer of era contradiction: everyone aspires to the scarce star positions, but the top-level space is limited. The self-expectations of the masses are naturally misaligned with the supply of actual positions.

At the same time, the current mainstream evaluation mechanisms for large models share the exact same underlying logic and inherent flaws as the college entrance exam and the ancient imperial examinations.

Unified benchmark evaluations are like the college entrance exam.  
All models are scored on the same test dataset, leveling the playing field in terms of parameter count and computing power, enabling横向公平比较. This is the lowest-cost, most objective screening tool in an era of scarce computing and data resources.  
Just as the ancient imperial examinations broke the monopoly of bloodline nobility and provided a channel for底层 talent to rise.

However, standardized yardsticks inevitably give rise to exam-oriented models. A large number of manufacturers fine-tune specifically for mainstream benchmark datasets, even incorporating test questions into the training set, equivalent to leaking questions and doing practice exams.  
The result is impressive榜单 scores but severely inflated real-world generalization capabilities—the same malady as students who memorize standard answers but fail to solve real problems.

The industry can only rely on dynamic updates, offline test sets, manually generated new questions, adding out-of-distribution adversarial questions, and raising the cost of exam-oriented behavior to mitigate the problem. But it cannot completely eradicate the phenomenon of gaming the system.  
It can only reduce the evaluation distortion caused by a single score.

Standardized evaluation is indispensable but cannot be the sole criterion.  
If unified benchmarks are completely abandoned, and each manufacturer only uses its own business data for self-evaluation, the industry would lose common objective comparison standards. Small and medium-sized models would lose a fair platform to showcase their capabilities, ultimately leading to a chaotic landscape dominated by capital.

Relying solely on benchmark scores to optimize models would continuously foster exam-oriented overfitting.  
When deployed in real business scenarios, failures would become frequent.

Currently, the industry adopts a compromised improvement route to reconcile the contradiction:  
- On one hand, continuously update the evaluation question bank dynamically, split into multiple dimensional sub-indicators, and break the single total score主义.  
- On the other hand, connect two data flywheels: automated benchmark rapid iteration and online real user feedback. Use low-cost standardized tests for short-cycle iteration and screening, and rely on long-term real business data to calibrate the model's true capability, balancing fairness and practical applicability.

### III. The Structural Crisis of the AI Era

The old mechanism for balancing labor supply and demand has completely失效.

Before the普及 of AI, society relied on the spontaneous adjustment of labor market supply and demand to maintain the division of labor balance.  
At that time, there were vast numbers of standardized底层 positions that could accommodate ordinary people who were not good at creativity but suited for convergent execution work.  
Meanwhile, individuals who rejected rules and had divergent talents could flow into diverse niche tracks such as cultural creativity, handicrafts, and human services.  
Salary and job competition pressure objectively constrained the public's overly high career expectations, forcing most people to accept constrained work they did not enjoy in order to make a living.  
Objectively, this matched the division-of-labor needs of the social pyramid and maintained overall operations.

The large-scale implementation of automation and large models continuously replaces traditional standardized底层 positions.  
The stone track that once absorbed大量普通人 is shrinking. The work tracks available to humans are压缩 into two types:

1. **Top-level star positions** with extremely limited capacity: pure creativity, deep strategy, original speculative tracks—the competition is fierce.  
2. **Special service positions** that AI cannot replace, heavily reliant on human empathy, complex interpersonal bargaining, and on-the-spot flexible judgment.

This produces a collective identity撕裂.  
Many ordinary people lack the top-tier talent to compete for scarce creative positions, yet they have lost the standardized execution work that once sustained them.

Meanwhile, groups like Jiang Duoduo, who excel at free divergence but reject rule constraints, have long received no recognition in a social evaluation system dominated by convergent tasks.  
Society also cannot supply enough creative positions to accommodate these people.

This困境 cannot be quickly resolved by any single means. The root cause is that the old labor supply-demand balance mechanism has already been broken by AI.  
A new system for division of labor and distribution has not yet taken shape.