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报告个人档案

十家 AI 机构在美国、写有本科经历的 AI 员工中，近四分之一本科就读于中国大陆院校

2026 年 9 月 22 日 Metix AI Platform 上，OpenAI、Anthropic、Google DeepMind、xAI、Meta、NVIDIA、Google、Microsoft、Apple、Amazon 的 AI 岗位，按本科院校所在地统计。

运行这个案例复现需 104 API Credits复现只计数，不读取任何档案

23.9%

十家机构在美国、档案里写有本科经历的 AI 员工中，本科院校在中国大陆的比例（19,178 人中 4,582 人）。

统计的是谁

职位
当前在十家机构之一担任 AI 岗位：职位名称含 machine learning、research scientist、member of technical staff 等 12 个词之一。
国家
档案所在国家为美国。
学历
档案里写有本科经历。
计入条件
本科院校位于中国大陆，按一份由 37 个匹配词（城市、省份和独有校名）和 145 个院校名称组成的清单判断。清单包括外国和香港高校在中国大陆设立的校区（如昆山杜克大学、香港中文大学（深圳）），按设计不含港澳台院校。
不做推断
只看院校所在地，不从姓名、语言或国籍推断任何东西。
数字的含义
人数都是 Metix AI Platform 上可见档案的计数，是下限，不是这些机构的人数；比例是这些档案内部的比例，可能高于也可能低于全部员工中的实际比例。

要点

  1. 十家机构合计 23.9%，至少是全美 AI 岗位整体水平（按同一份清单不超过 12.2%）的 1.9 倍。各家差别很大，从 Anthropic 的 11.2% 到 Meta 的 36.0%。
  2. 当前工作开始于最近 24 个月的人里，这一比例是 21.7%，当前工作开始得更早的人是 25.8%。按机构分别比较后差距是 2.6 个百分点，超出偶然波动的只有 Meta、Microsoft；其余机构的差距有高有低，都在偶然波动范围内。
  3. 职位名称含 scientist 或 researcher 的是 32.2%，同几家机构的其他 AI 职位是 17.9%。差距在 Amazon、Meta、Microsoft 明显，在 Apple、Google DeepMind、Google（不含 DeepMind） 看不出来。
  4. 十二所大学里人数最多的依次是清华大学、上海交通大学、中国科学技术大学、浙江大学、北京大学。中国科学技术大学排在北京大学之前，而且中国科学技术大学这一行是严格计数。

01

从 Anthropic 的 11.2% 到 Meta 的 36.0%，9 家高于全美 AI 岗位的上限

在美国、写有本科经历的 AI 员工中，本科在中国大陆院校的比例。刻度 0% 到 50%；虚线是全美 AI 岗位的上限，实线是十家合计

Meta36.0%1,672 / 4,643
xAI26.4%99 / 375
Amazon23.9%1,052 / 4,393
Google DeepMind22.2%165 / 743
NVIDIA20.2%243 / 1,203
Apple18.6%371 / 1,995
OpenAI17.9%273 / 1,523
Microsoft17.7%355 / 2,011
Google（不含 DeepMind）17.0%279 / 1,640
Anthropic11.2%73 / 652

图中所见

全美当前职位属于 AI 岗位、写有本科经历的人超过 10 万，其中 12,194 人本科在中国大陆院校，所以全美整体水平最多是 12.2%。9 家机构高于这个上限；Anthropic（11.2%）低于上限，数据无法判断它是否高于全美实际水平。

方法与局限

一个人如果同时在两家机构有当前职位，只算在清单中靠前的那一家，所以各行不重叠，十家合计就是各行合并计算的结果。十家在全球（不限美国）的比例是 19.0%。

来源：Metix AI Platform 个人档案数据，2026-09-22。

查询population.json · institutions.json
POST /v1/people/query · queries/population.json
{  "note": "Who is counted. An AI role is a current job (experience.is_current eq true) whose title matches any of ai_title_terms; the lab and the recent-hire window sit in the same has_experience entry, so they describe that one job. US-based uses the profile's location.country: every profile with a mainland-China bachelor's in an AI role lists one (countries.json has no profile without a location), while the job-level location is often missing. The denominator is people with any Bachelor entry; the numerator is those whose Bachelor entry names an institution in institutions.json.",  "ai_title_terms": [    "machine learning",    "research scientist",    "research engineer",    "deep learning",    "member of technical staff",    "applied scientist",    "artificial intelligence",    "AI engineer",    "AI researcher",    "LLM",    "NLP",    "computer vision"  ],  "research_title_terms": [    "scientist",    "researcher"  ],  "recent_hire_window": "now-24m",  "us": {    "field": "location.country",    "eq": "United States"  },  "labs": [    {      "id": "openai",      "name": "OpenAI",      "companies": [        "OpenAI"      ]    },    {      "id": "anthropic",      "name": "Anthropic",      "companies": [        "Anthropic"      ]    },    {      "id": "google-deepmind",      "name": "Google DeepMind",      "companies": [        "Google DeepMind",        "DeepMind"      ]    },    {      "id": "xai",      "name": "xAI",      "companies": [        "xAI"      ]    },    {      "id": "meta",      "name": "Meta",      "companies": [        "Meta"      ]    },    {      "id": "nvidia",      "name": "NVIDIA",      "companies": [        "NVIDIA"      ]    },    {      "id": "google",      "name": "Google, other than Google DeepMind",      "companies": [        "Google"      ],      "note": "The company name Google also matches people whose current job is at Google DeepMind, because the Platform matches company names word by word. Google DeepMind comes earlier in the order, so its staff count there and not here."    },    {      "id": "microsoft",      "name": "Microsoft",      "companies": [        "Microsoft"      ]    },    {      "id": "apple",      "name": "Apple",      "companies": [        "Apple"      ]    },    {      "id": "amazon",      "name": "Amazon",      "companies": [        "Amazon",        "Amazon Web Services (AWS)",        "AWS"      ],      "note": "Amazon Web Services adds 254 profiles with a Bachelor entry to the 6,098 under Amazon."    }  ],  "institutions": [    {      "id": "tsinghua",      "name": "Tsinghua University",      "match": [        "Tsinghua"      ],      "exact": [        "清华大学"      ]    },    {      "id": "peking",      "name": "Peking University",      "match": [        "Peking University"      ],      "exact": [        "北京大学"      ]    },    {      "id": "sjtu",      "name": "Shanghai Jiao Tong University",      "match": [        "Shanghai Jiao Tong"      ],      "exact": [        "Shanghai Jiaotong University",        "上海交通大学"      ]    },    {      "id": "zju",      "name": "Zhejiang University",      "exact": [        "Zhejiang University",        "浙江大学"      ],      "exclude": [        "Sci-Tech",        "Normal",        "Gongshang",        "Finance",        "Media"      ]    },    {      "id": "ustc",      "name": "University of Science and Technology of China",      "exact": [        "University of Science and Technology of China",        "USTC",        "中国科学技术大学"      ],      "exclude": [        "Electronic"      ]    },    {      "id": "fudan",      "name": "Fudan University",      "match": [        "Fudan"      ],      "exact": [        "复旦大学"      ]    },    {      "id": "nju",      "name": "Nanjing University",      "exact": [        "Nanjing University",        "南京大学"      ],      "exclude": [        "Aeronautics",        "Posts",        "Information",        "Normal",        "Tech",        "Medical",        "Forestry",        "Agricultural",        "Audit",        "Finance",        "Science Technology"      ]    },    {      "id": "hust",      "name": "Huazhong University of Science and Technology",      "match": [        "Huazhong Science Technology"      ],      "exact": [        "华中科技大学"      ]    },    {      "id": "hit",      "name": "Harbin Institute of Technology",      "match": [        "Harbin Institute Technology"      ],      "exact": [        "哈尔滨工业大学"      ]    },    {      "id": "beihang",      "name": "Beihang University",      "match": [        "Beihang"      ],      "exact": [        "Beijing University of Aeronautics and Astronautics",        "北京航空航天大学"      ]    },    {      "id": "xjtu",      "name": "Xi'an Jiaotong University",      "exact": [        "Xi'an Jiaotong University",        "Xian Jiaotong University",        "西安交通大学"      ],      "exclude": [        "Liverpool"      ]    },    {      "id": "uestc",      "name": "University of Electronic Science and Technology of China",      "exact": [        "University of Electronic Science and Technology of China",        "UESTC",        "电子科技大学"      ]    }  ],  "institutions_note": "One institution per count. The Platform matches names word by word, so Nanjing University also matches Nanjing University of Aeronautics and Astronautics, and University of Science and Technology of China also matches University of Electronic Science and Technology of China. A row with exclude words leaves out entries that contain them, which makes it a strict count: entries such as Nanjing University, Department of Computer Science and Technology drop out too. The twelve are large mainland universities named in the prompt before any count was run; they are not a ranking of every institution, and others outside the twelve are not counted one by one.",  "countries": [    "United States",    "China",    "Canada",    "United Kingdom",    "Singapore",    "Germany",    "Australia",    "Switzerland",    "France",    "Japan",    "Hong Kong"  ],  "lab_order_note": "Rows do not overlap. A lab's row counts people whose current AI-titled job is at that lab and who hold no current job at a lab listed before it, so someone with current jobs at two labs counts once, at the first. The ten-lab row is the sum of the ten rows.",  "countries_note": "Values of the profile's location.country. Hong Kong is its own value on the Platform, listed here as a region."}
POST /v1/people/query · queries/institutions.json
{  "note": "Institutions located in mainland China, used on education.school.name inside one has_education entry together with education.degree eq Bachelor. A profile counts when one Bachelor entry names an institution on this list and none of the exclude words; nothing is inferred from personal names or languages. The Platform matches both match terms and the names in exact word by word: a name counts when every one of its words appears in the entry, in any order, so a longer name that contains a listed one also counts. Match terms are words that only mainland institutions use in their names (cities, provinces, distinctive names). The exclude words remove entries whose names contain a listed name but belong to an institution elsewhere: National Sun Yat-sen University in Taiwan contains Sun Yat-sen University, and Southeast Missouri State University contains Southeast University. Mainland campuses of foreign and Hong Kong universities count, because the rule is where the institution is. The list is built to leave out institutions in Hong Kong, Macau, and Taiwan. The school country recorded on an entry cannot be queried, so it cannot serve as the rule. The query limit of 64 conditions keeps the match terms to 37; less common institutions are listed by name.",  "how_built": "Built from the Bachelor entries of 250 profiles with a current AI title and a current location in China, read in full. The school country recorded on each entry is missing for many mainland institutions (Beihang University, Huazhong University of Science and Technology, and some Tsinghua University entries among them), so each institution's location was decided from its name and reviewed by hand. The list catches 173 of the 173 mainland Bachelor entries in that pool.",  "audit": "Two independent samples, read in full. 250 profiles with a current AI title at the ten labs, 25 per lab: the list caught 28 of the 28 mainland Bachelor entries. 200 US-based AI staff with a Bachelor entry at Meta, Amazon, NVIDIA, and Microsoft: the list caught 60 of 63 and matched one institution in Taiwan; the three it missed (Capital Medical University, Southern Medical University, Jimei University) are now listed and the Taiwan match is excluded. On the ten labs' US-based staff the exclude words remove 6 entries and the three added names add 5. Less common institutions still slip through, so shares built on the list are probably slightly low.",  "match": [    "Tsinghua",    "Peking University",    "Fudan",    "Tongji",    "Nankai",    "Beihang",    "Renmin",    "Huazhong",    "Jiaotong",    "Jiao Tong",    "Xidian",    "ShanghaiTech",    "SUSTech",    "Kunshan",    "Ningbo",    "Beijing",    "Shanghai",    "Tianjin",    "Chongqing",    "Nanjing",    "Wuhan",    "Harbin",    "Hangzhou",    "Hefei",    "Xiamen",    "Dalian",    "Qingdao",    "Guangzhou",    "Shenzhen",    "Chengdu",    "Zhengzhou",    "Jinan",    "Zhejiang",    "Sichuan",    "Shandong",    "Jilin",    "Hunan"  ],  "exact": [    "Anhui Agricultural University",    "Anhui University",    "Capital Medical University",    "Capital Normal University",    "Capital University of Economics and Business",    "Central South University",    "Central University of Finance and Economics",    "China Agricultural University",    "China Pharmaceutical University",    "China University of Geosciences",    "China University of Mining and Technology",    "China University of Petroleum",    "Communication University of China",    "Donghua University",    "Duke Kunshan University",    "East China Normal University",    "East China University of Science and Technology",    "Fujian Normal University",    "Fuzhou University",    "Guangdong University of Technology",    "Guangxi University",    "Guizhou University",    "Hainan University",    "Hebei University of Technology",    "Heilongjiang University",    "Henan University",    "Hohai University",    "Hubei University",    "Inner Mongolia University",    "Jiangnan University",    "Jiangsu University",    "Jiangxi University of Finance and Economics",    "Jilin Agricultural University",    "Jimei University",    "Kunming University of Science and Technology",    "Lanzhou University",    "Liaoning University",    "Minzu University of China",    "Nanchang University",    "Nanjing Forestry University",    "National University of Defense Technology",    "New York University Shanghai",    "North China Electric Power University",    "Northeast Forestry University",    "Northeast Normal University",    "Northwest A&F University",    "Northwest University",    "Northwestern Polytechnical University",    "Ocean University of China",    "Peking Union Medical College",    "Shaanxi Normal University",    "Shenyang University of Technology",    "Soochow University (CN)",    "South China Agricultural University",    "South China Normal University",    "South China University of Technology",    "Southeast University",    "Southern Medical University",    "Southern University of Science and Technology",    "Southwest University",    "Sun Yat-Sen University",    "Sun Yat-sen University",    "Taiyuan University of Technology",    "The Chinese University of Hong Kong, Shenzhen",    "USTC",    "University of Electronic Science and Technology of China",    "University of International Business and Economics",    "University of Science and Technology of China",    "Westlake University",    "Xi'an Jiaotong-Liverpool University",    "Xinjiang University",    "Yunnan University",    "上海交通大学",    "上海大学",    "上海科技大学",    "上海财经大学",    "东北大学",    "东华大学",    "东南大学",    "中南大学",    "中国人民大学",    "中国农业大学",    "中国海洋大学",    "中国科学技术大学",    "中央民族大学",    "中央财经大学",    "中山大学",    "佛山大学",    "兰州大学",    "北京交通大学",    "北京大学",    "北京师范大学",    "北京理工大学",    "北京科技大学",    "北京航空航天大学",    "北京邮电大学",    "华东师范大学",    "华东理工大学",    "华中科技大学",    "华南师范大学",    "华南理工大学",    "南京大学",    "南京理工大学",    "南京航空航天大学",    "南京邮电大学",    "南开大学",    "南方科技大学",    "厦门大学",    "合肥工业大学",    "吉林大学",    "同济大学",    "哈尔滨工业大学",    "哈尔滨工程大学",    "四川大学",    "国防科技大学",    "复旦大学",    "大连理工大学",    "天津大学",    "对外经济贸易大学",    "山东大学",    "山东理工大学",    "暨南大学",    "杭州电子科技大学",    "武汉大学",    "武汉理工大学",    "武汉科技学院",    "河海大学",    "济南大学",    "浙江大学",    "深圳大学",    "清华大学",    "湖南大学",    "电子科技大学",    "苏州大学",    "西交利物浦大学",    "西北农林科技大学",    "西北工业大学",    "西南交通大学",    "西安交通大学",    "西安电子科技大学",    "西湖大学",    "郑州大学",    "重庆大学",    "首都师范大学",    "香港中文大学（深圳）"  ],  "exclude": [    "National Sun Yat-sen",    "Taiwan",    "Malaysia",    "Missouri"  ]}

02

当前工作开始于最近 24 个月的人，比例低 4.1 个百分点，差距最大的是 Meta、Microsoft

当前工作开始得更早的人（空心）和开始于最近 24 个月的人（实心）中，本科在中国大陆院校的比例。紫色是差距超出偶然波动范围的机构，黑色一行是十家合计

Meta−8.539.5% → 31.0%
Microsoft−6.020.8% → 14.8%
NVIDIA−2.521.3% → 18.7%
Amazon−2.224.9% → 22.8%
Apple−1.219.0% → 17.8%
Google（不含 DeepMind）+1.416.5% → 17.9%
OpenAI+2.216.4% → 18.6%
Google DeepMind+3.620.1% → 23.8%
Anthropic+3.98.2% → 12.1%
十家合计−4.125.8% → 21.7%

图中所见

十家合计的差距是 4.1 个百分点，其中一部分是因为最近 24 个月开始当前工作的人较多的机构，本身比例较低；按机构分别比较、再按这些人数加权，差距是 2.6 个百分点。只有 Meta、Microsoft 的差距大到不像偶然（把可见档案当作样本做双比例检验，|z| 至少 1.96）；其余机构的 |z| 不超过 1.7。xAI 的某一组不足 100 人，没有画。

方法与局限

这里看的是当前这份工作的开始时间，内部转岗也算，所以它描述的是现在的在职结构，不是招聘流程。更早的一组有更长时间流失离开的人：两类人离职快慢如果不同，差距反映的也可能是谁留下，而不只是招了谁。当前工作没有开始时间的档案归在更早的一组。这个比较也没有区分职位类型：职位名称含 scientist 或 researcher 的比例更高（见图 03），而数据看不出最近开始当前工作的人担任这类职位的比例是否一样，影响的方向也可能相反。

来源：Metix AI Platform 个人档案数据，2026-09-22。

查询population.json · institutions.json
POST /v1/people/query · queries/population.json
{  "note": "Who is counted. An AI role is a current job (experience.is_current eq true) whose title matches any of ai_title_terms; the lab and the recent-hire window sit in the same has_experience entry, so they describe that one job. US-based uses the profile's location.country: every profile with a mainland-China bachelor's in an AI role lists one (countries.json has no profile without a location), while the job-level location is often missing. The denominator is people with any Bachelor entry; the numerator is those whose Bachelor entry names an institution in institutions.json.",  "ai_title_terms": [    "machine learning",    "research scientist",    "research engineer",    "deep learning",    "member of technical staff",    "applied scientist",    "artificial intelligence",    "AI engineer",    "AI researcher",    "LLM",    "NLP",    "computer vision"  ],  "research_title_terms": [    "scientist",    "researcher"  ],  "recent_hire_window": "now-24m",  "us": {    "field": "location.country",    "eq": "United States"  },  "labs": [    {      "id": "openai",      "name": "OpenAI",      "companies": [        "OpenAI"      ]    },    {      "id": "anthropic",      "name": "Anthropic",      "companies": [        "Anthropic"      ]    },    {      "id": "google-deepmind",      "name": "Google DeepMind",      "companies": [        "Google DeepMind",        "DeepMind"      ]    },    {      "id": "xai",      "name": "xAI",      "companies": [        "xAI"      ]    },    {      "id": "meta",      "name": "Meta",      "companies": [        "Meta"      ]    },    {      "id": "nvidia",      "name": "NVIDIA",      "companies": [        "NVIDIA"      ]    },    {      "id": "google",      "name": "Google, other than Google DeepMind",      "companies": [        "Google"      ],      "note": "The company name Google also matches people whose current job is at Google DeepMind, because the Platform matches company names word by word. Google DeepMind comes earlier in the order, so its staff count there and not here."    },    {      "id": "microsoft",      "name": "Microsoft",      "companies": [        "Microsoft"      ]    },    {      "id": "apple",      "name": "Apple",      "companies": [        "Apple"      ]    },    {      "id": "amazon",      "name": "Amazon",      "companies": [        "Amazon",        "Amazon Web Services (AWS)",        "AWS"      ],      "note": "Amazon Web Services adds 254 profiles with a Bachelor entry to the 6,098 under Amazon."    }  ],  "institutions": [    {      "id": "tsinghua",      "name": "Tsinghua University",      "match": [        "Tsinghua"      ],      "exact": [        "清华大学"      ]    },    {      "id": "peking",      "name": "Peking University",      "match": [        "Peking University"      ],      "exact": [        "北京大学"      ]    },    {      "id": "sjtu",      "name": "Shanghai Jiao Tong University",      "match": [        "Shanghai Jiao Tong"      ],      "exact": [        "Shanghai Jiaotong University",        "上海交通大学"      ]    },    {      "id": "zju",      "name": "Zhejiang University",      "exact": [        "Zhejiang University",        "浙江大学"      ],      "exclude": [        "Sci-Tech",        "Normal",        "Gongshang",        "Finance",        "Media"      ]    },    {      "id": "ustc",      "name": "University of Science and Technology of China",      "exact": [        "University of Science and Technology of China",        "USTC",        "中国科学技术大学"      ],      "exclude": [        "Electronic"      ]    },    {      "id": "fudan",      "name": "Fudan University",      "match": [        "Fudan"      ],      "exact": [        "复旦大学"      ]    },    {      "id": "nju",      "name": "Nanjing University",      "exact": [        "Nanjing University",        "南京大学"      ],      "exclude": [        "Aeronautics",        "Posts",        "Information",        "Normal",        "Tech",        "Medical",        "Forestry",        "Agricultural",        "Audit",        "Finance",        "Science Technology"      ]    },    {      "id": "hust",      "name": "Huazhong University of Science and Technology",      "match": [        "Huazhong Science Technology"      ],      "exact": [        "华中科技大学"      ]    },    {      "id": "hit",      "name": "Harbin Institute of Technology",      "match": [        "Harbin Institute Technology"      ],      "exact": [        "哈尔滨工业大学"      ]    },    {      "id": "beihang",      "name": "Beihang University",      "match": [        "Beihang"      ],      "exact": [        "Beijing University of Aeronautics and Astronautics",        "北京航空航天大学"      ]    },    {      "id": "xjtu",      "name": "Xi'an Jiaotong University",      "exact": [        "Xi'an Jiaotong University",        "Xian Jiaotong University",        "西安交通大学"      ],      "exclude": [        "Liverpool"      ]    },    {      "id": "uestc",      "name": "University of Electronic Science and Technology of China",      "exact": [        "University of Electronic Science and Technology of China",        "UESTC",        "电子科技大学"      ]    }  ],  "institutions_note": "One institution per count. The Platform matches names word by word, so Nanjing University also matches Nanjing University of Aeronautics and Astronautics, and University of Science and Technology of China also matches University of Electronic Science and Technology of China. A row with exclude words leaves out entries that contain them, which makes it a strict count: entries such as Nanjing University, Department of Computer Science and Technology drop out too. The twelve are large mainland universities named in the prompt before any count was run; they are not a ranking of every institution, and others outside the twelve are not counted one by one.",  "countries": [    "United States",    "China",    "Canada",    "United Kingdom",    "Singapore",    "Germany",    "Australia",    "Switzerland",    "France",    "Japan",    "Hong Kong"  ],  "lab_order_note": "Rows do not overlap. A lab's row counts people whose current AI-titled job is at that lab and who hold no current job at a lab listed before it, so someone with current jobs at two labs counts once, at the first. The ten-lab row is the sum of the ten rows.",  "countries_note": "Values of the profile's location.country. Hong Kong is its own value on the Platform, listed here as a region."}
POST /v1/people/query · queries/institutions.json
{  "note": "Institutions located in mainland China, used on education.school.name inside one has_education entry together with education.degree eq Bachelor. A profile counts when one Bachelor entry names an institution on this list and none of the exclude words; nothing is inferred from personal names or languages. The Platform matches both match terms and the names in exact word by word: a name counts when every one of its words appears in the entry, in any order, so a longer name that contains a listed one also counts. Match terms are words that only mainland institutions use in their names (cities, provinces, distinctive names). The exclude words remove entries whose names contain a listed name but belong to an institution elsewhere: National Sun Yat-sen University in Taiwan contains Sun Yat-sen University, and Southeast Missouri State University contains Southeast University. Mainland campuses of foreign and Hong Kong universities count, because the rule is where the institution is. The list is built to leave out institutions in Hong Kong, Macau, and Taiwan. The school country recorded on an entry cannot be queried, so it cannot serve as the rule. The query limit of 64 conditions keeps the match terms to 37; less common institutions are listed by name.",  "how_built": "Built from the Bachelor entries of 250 profiles with a current AI title and a current location in China, read in full. The school country recorded on each entry is missing for many mainland institutions (Beihang University, Huazhong University of Science and Technology, and some Tsinghua University entries among them), so each institution's location was decided from its name and reviewed by hand. The list catches 173 of the 173 mainland Bachelor entries in that pool.",  "audit": "Two independent samples, read in full. 250 profiles with a current AI title at the ten labs, 25 per lab: the list caught 28 of the 28 mainland Bachelor entries. 200 US-based AI staff with a Bachelor entry at Meta, Amazon, NVIDIA, and Microsoft: the list caught 60 of 63 and matched one institution in Taiwan; the three it missed (Capital Medical University, Southern Medical University, Jimei University) are now listed and the Taiwan match is excluded. On the ten labs' US-based staff the exclude words remove 6 entries and the three added names add 5. Less common institutions still slip through, so shares built on the list are probably slightly low.",  "match": [    "Tsinghua",    "Peking University",    "Fudan",    "Tongji",    "Nankai",    "Beihang",    "Renmin",    "Huazhong",    "Jiaotong",    "Jiao Tong",    "Xidian",    "ShanghaiTech",    "SUSTech",    "Kunshan",    "Ningbo",    "Beijing",    "Shanghai",    "Tianjin",    "Chongqing",    "Nanjing",    "Wuhan",    "Harbin",    "Hangzhou",    "Hefei",    "Xiamen",    "Dalian",    "Qingdao",    "Guangzhou",    "Shenzhen",    "Chengdu",    "Zhengzhou",    "Jinan",    "Zhejiang",    "Sichuan",    "Shandong",    "Jilin",    "Hunan"  ],  "exact": [    "Anhui Agricultural University",    "Anhui University",    "Capital Medical University",    "Capital Normal University",    "Capital University of Economics and Business",    "Central South University",    "Central University of Finance and Economics",    "China Agricultural University",    "China Pharmaceutical University",    "China University of Geosciences",    "China University of Mining and Technology",    "China University of Petroleum",    "Communication University of China",    "Donghua University",    "Duke Kunshan University",    "East China Normal University",    "East China University of Science and Technology",    "Fujian Normal University",    "Fuzhou University",    "Guangdong University of Technology",    "Guangxi University",    "Guizhou University",    "Hainan University",    "Hebei University of Technology",    "Heilongjiang University",    "Henan University",    "Hohai University",    "Hubei University",    "Inner Mongolia University",    "Jiangnan University",    "Jiangsu University",    "Jiangxi University of Finance and Economics",    "Jilin Agricultural University",    "Jimei University",    "Kunming University of Science and Technology",    "Lanzhou University",    "Liaoning University",    "Minzu University of China",    "Nanchang University",    "Nanjing Forestry University",    "National University of Defense Technology",    "New York University Shanghai",    "North China Electric Power University",    "Northeast Forestry University",    "Northeast Normal University",    "Northwest A&F University",    "Northwest University",    "Northwestern Polytechnical University",    "Ocean University of China",    "Peking Union Medical College",    "Shaanxi Normal University",    "Shenyang University of Technology",    "Soochow University (CN)",    "South China Agricultural University",    "South China Normal University",    "South China University of Technology",    "Southeast University",    "Southern Medical University",    "Southern University of Science and Technology",    "Southwest University",    "Sun Yat-Sen University",    "Sun Yat-sen University",    "Taiyuan University of Technology",    "The Chinese University of Hong Kong, Shenzhen",    "USTC",    "University of Electronic Science and Technology of China",    "University of International Business and Economics",    "University of Science and Technology of China",    "Westlake University",    "Xi'an Jiaotong-Liverpool University",    "Xinjiang University",    "Yunnan University",    "上海交通大学",    "上海大学",    "上海科技大学",    "上海财经大学",    "东北大学",    "东华大学",    "东南大学",    "中南大学",    "中国人民大学",    "中国农业大学",    "中国海洋大学",    "中国科学技术大学",    "中央民族大学",    "中央财经大学",    "中山大学",    "佛山大学",    "兰州大学",    "北京交通大学",    "北京大学",    "北京师范大学",    "北京理工大学",    "北京科技大学",    "北京航空航天大学",    "北京邮电大学",    "华东师范大学",    "华东理工大学",    "华中科技大学",    "华南师范大学",    "华南理工大学",    "南京大学",    "南京理工大学",    "南京航空航天大学",    "南京邮电大学",    "南开大学",    "南方科技大学",    "厦门大学",    "合肥工业大学",    "吉林大学",    "同济大学",    "哈尔滨工业大学",    "哈尔滨工程大学",    "四川大学",    "国防科技大学",    "复旦大学",    "大连理工大学",    "天津大学",    "对外经济贸易大学",    "山东大学",    "山东理工大学",    "暨南大学",    "杭州电子科技大学",    "武汉大学",    "武汉理工大学",    "武汉科技学院",    "河海大学",    "济南大学",    "浙江大学",    "深圳大学",    "清华大学",    "湖南大学",    "电子科技大学",    "苏州大学",    "西交利物浦大学",    "西北农林科技大学",    "西北工业大学",    "西南交通大学",    "西安交通大学",    "西安电子科技大学",    "西湖大学",    "郑州大学",    "重庆大学",    "首都师范大学",    "香港中文大学（深圳）"  ],  "exclude": [    "National Sun Yat-sen",    "Taiwan",    "Malaysia",    "Missouri"  ]}

03

职位名称含 scientist 或 researcher 的是 32.2%，同几家机构的其他 AI 职位是 17.9%

职位名称含 scientist 或 researcher（实心），同一机构的其他 AI 职位（空心）。只画两类都至少 100 人的机构

Amazon29.9%966 / 3,229
Meta46.2%1,084 / 2,346
Microsoft24.7%233 / 943
NVIDIA24.1%92 / 382
Apple19.9%67 / 336
Google DeepMind22.7%85 / 374
Google（不含 DeepMind）17.6%97 / 551
这几家合计32.2%2,624 / 8,161

图中所见

Applied Scientist 也算在内：Amazon 写有本科经历的 4,393 名 AI 员工中有 3,229 人是这类职位。Amazon、Meta、Microsoft 的差距远超偶然范围，NVIDIA 勉强超过（这里比较了 7 家，按多重比较校正后不算）；Apple、Google DeepMind、Google（不含 DeepMind） 看不出差距。

方法与局限

OpenAI、Anthropic、xAI 大多用 member of technical staff 之类的职位名称，这类职位不到 100 人，没有画；有几家的人数太少不能公开，所以这里不给十家合计，汇总行只合计图中这几家。

来源：Metix AI Platform 个人档案数据，2026-09-22。

查询population.json · institutions.json
POST /v1/people/query · queries/population.json
{  "note": "Who is counted. An AI role is a current job (experience.is_current eq true) whose title matches any of ai_title_terms; the lab and the recent-hire window sit in the same has_experience entry, so they describe that one job. US-based uses the profile's location.country: every profile with a mainland-China bachelor's in an AI role lists one (countries.json has no profile without a location), while the job-level location is often missing. The denominator is people with any Bachelor entry; the numerator is those whose Bachelor entry names an institution in institutions.json.",  "ai_title_terms": [    "machine learning",    "research scientist",    "research engineer",    "deep learning",    "member of technical staff",    "applied scientist",    "artificial intelligence",    "AI engineer",    "AI researcher",    "LLM",    "NLP",    "computer vision"  ],  "research_title_terms": [    "scientist",    "researcher"  ],  "recent_hire_window": "now-24m",  "us": {    "field": "location.country",    "eq": "United States"  },  "labs": [    {      "id": "openai",      "name": "OpenAI",      "companies": [        "OpenAI"      ]    },    {      "id": "anthropic",      "name": "Anthropic",      "companies": [        "Anthropic"      ]    },    {      "id": "google-deepmind",      "name": "Google DeepMind",      "companies": [        "Google DeepMind",        "DeepMind"      ]    },    {      "id": "xai",      "name": "xAI",      "companies": [        "xAI"      ]    },    {      "id": "meta",      "name": "Meta",      "companies": [        "Meta"      ]    },    {      "id": "nvidia",      "name": "NVIDIA",      "companies": [        "NVIDIA"      ]    },    {      "id": "google",      "name": "Google, other than Google DeepMind",      "companies": [        "Google"      ],      "note": "The company name Google also matches people whose current job is at Google DeepMind, because the Platform matches company names word by word. Google DeepMind comes earlier in the order, so its staff count there and not here."    },    {      "id": "microsoft",      "name": "Microsoft",      "companies": [        "Microsoft"      ]    },    {      "id": "apple",      "name": "Apple",      "companies": [        "Apple"      ]    },    {      "id": "amazon",      "name": "Amazon",      "companies": [        "Amazon",        "Amazon Web Services (AWS)",        "AWS"      ],      "note": "Amazon Web Services adds 254 profiles with a Bachelor entry to the 6,098 under Amazon."    }  ],  "institutions": [    {      "id": "tsinghua",      "name": "Tsinghua University",      "match": [        "Tsinghua"      ],      "exact": [        "清华大学"      ]    },    {      "id": "peking",      "name": "Peking University",      "match": [        "Peking University"      ],      "exact": [        "北京大学"      ]    },    {      "id": "sjtu",      "name": "Shanghai Jiao Tong University",      "match": [        "Shanghai Jiao Tong"      ],      "exact": [        "Shanghai Jiaotong University",        "上海交通大学"      ]    },    {      "id": "zju",      "name": "Zhejiang University",      "exact": [        "Zhejiang University",        "浙江大学"      ],      "exclude": [        "Sci-Tech",        "Normal",        "Gongshang",        "Finance",        "Media"      ]    },    {      "id": "ustc",      "name": "University of Science and Technology of China",      "exact": [        "University of Science and Technology of China",        "USTC",        "中国科学技术大学"      ],      "exclude": [        "Electronic"      ]    },    {      "id": "fudan",      "name": "Fudan University",      "match": [        "Fudan"      ],      "exact": [        "复旦大学"      ]    },    {      "id": "nju",      "name": "Nanjing University",      "exact": [        "Nanjing University",        "南京大学"      ],      "exclude": [        "Aeronautics",        "Posts",        "Information",        "Normal",        "Tech",        "Medical",        "Forestry",        "Agricultural",        "Audit",        "Finance",        "Science Technology"      ]    },    {      "id": "hust",      "name": "Huazhong University of Science and Technology",      "match": [        "Huazhong Science Technology"      ],      "exact": [        "华中科技大学"      ]    },    {      "id": "hit",      "name": "Harbin Institute of Technology",      "match": [        "Harbin Institute Technology"      ],      "exact": [        "哈尔滨工业大学"      ]    },    {      "id": "beihang",      "name": "Beihang University",      "match": [        "Beihang"      ],      "exact": [        "Beijing University of Aeronautics and Astronautics",        "北京航空航天大学"      ]    },    {      "id": "xjtu",      "name": "Xi'an Jiaotong University",      "exact": [        "Xi'an Jiaotong University",        "Xian Jiaotong University",        "西安交通大学"      ],      "exclude": [        "Liverpool"      ]    },    {      "id": "uestc",      "name": "University of Electronic Science and Technology of China",      "exact": [        "University of Electronic Science and Technology of China",        "UESTC",        "电子科技大学"      ]    }  ],  "institutions_note": "One institution per count. The Platform matches names word by word, so Nanjing University also matches Nanjing University of Aeronautics and Astronautics, and University of Science and Technology of China also matches University of Electronic Science and Technology of China. A row with exclude words leaves out entries that contain them, which makes it a strict count: entries such as Nanjing University, Department of Computer Science and Technology drop out too. The twelve are large mainland universities named in the prompt before any count was run; they are not a ranking of every institution, and others outside the twelve are not counted one by one.",  "countries": [    "United States",    "China",    "Canada",    "United Kingdom",    "Singapore",    "Germany",    "Australia",    "Switzerland",    "France",    "Japan",    "Hong Kong"  ],  "lab_order_note": "Rows do not overlap. A lab's row counts people whose current AI-titled job is at that lab and who hold no current job at a lab listed before it, so someone with current jobs at two labs counts once, at the first. The ten-lab row is the sum of the ten rows.",  "countries_note": "Values of the profile's location.country. Hong Kong is its own value on the Platform, listed here as a region."}
POST /v1/people/query · queries/institutions.json
{  "note": "Institutions located in mainland China, used on education.school.name inside one has_education entry together with education.degree eq Bachelor. A profile counts when one Bachelor entry names an institution on this list and none of the exclude words; nothing is inferred from personal names or languages. The Platform matches both match terms and the names in exact word by word: a name counts when every one of its words appears in the entry, in any order, so a longer name that contains a listed one also counts. Match terms are words that only mainland institutions use in their names (cities, provinces, distinctive names). The exclude words remove entries whose names contain a listed name but belong to an institution elsewhere: National Sun Yat-sen University in Taiwan contains Sun Yat-sen University, and Southeast Missouri State University contains Southeast University. Mainland campuses of foreign and Hong Kong universities count, because the rule is where the institution is. The list is built to leave out institutions in Hong Kong, Macau, and Taiwan. The school country recorded on an entry cannot be queried, so it cannot serve as the rule. The query limit of 64 conditions keeps the match terms to 37; less common institutions are listed by name.",  "how_built": "Built from the Bachelor entries of 250 profiles with a current AI title and a current location in China, read in full. The school country recorded on each entry is missing for many mainland institutions (Beihang University, Huazhong University of Science and Technology, and some Tsinghua University entries among them), so each institution's location was decided from its name and reviewed by hand. The list catches 173 of the 173 mainland Bachelor entries in that pool.",  "audit": "Two independent samples, read in full. 250 profiles with a current AI title at the ten labs, 25 per lab: the list caught 28 of the 28 mainland Bachelor entries. 200 US-based AI staff with a Bachelor entry at Meta, Amazon, NVIDIA, and Microsoft: the list caught 60 of 63 and matched one institution in Taiwan; the three it missed (Capital Medical University, Southern Medical University, Jimei University) are now listed and the Taiwan match is excluded. On the ten labs' US-based staff the exclude words remove 6 entries and the three added names add 5. Less common institutions still slip through, so shares built on the list are probably slightly low.",  "match": [    "Tsinghua",    "Peking University",    "Fudan",    "Tongji",    "Nankai",    "Beihang",    "Renmin",    "Huazhong",    "Jiaotong",    "Jiao Tong",    "Xidian",    "ShanghaiTech",    "SUSTech",    "Kunshan",    "Ningbo",    "Beijing",    "Shanghai",    "Tianjin",    "Chongqing",    "Nanjing",    "Wuhan",    "Harbin",    "Hangzhou",    "Hefei",    "Xiamen",    "Dalian",    "Qingdao",    "Guangzhou",    "Shenzhen",    "Chengdu",    "Zhengzhou",    "Jinan",    "Zhejiang",    "Sichuan",    "Shandong",    "Jilin",    "Hunan"  ],  "exact": [    "Anhui Agricultural University",    "Anhui University",    "Capital Medical University",    "Capital Normal University",    "Capital University of Economics and Business",    "Central South University",    "Central University of Finance and Economics",    "China Agricultural University",    "China Pharmaceutical University",    "China University of Geosciences",    "China University of Mining and Technology",    "China University of Petroleum",    "Communication University of China",    "Donghua University",    "Duke Kunshan University",    "East China Normal University",    "East China University of Science and Technology",    "Fujian Normal University",    "Fuzhou University",    "Guangdong University of Technology",    "Guangxi University",    "Guizhou University",    "Hainan University",    "Hebei University of Technology",    "Heilongjiang University",    "Henan University",    "Hohai University",    "Hubei University",    "Inner Mongolia University",    "Jiangnan University",    "Jiangsu University",    "Jiangxi University of Finance and Economics",    "Jilin Agricultural University",    "Jimei University",    "Kunming University of Science and Technology",    "Lanzhou University",    "Liaoning University",    "Minzu University of China",    "Nanchang University",    "Nanjing Forestry University",    "National University of Defense Technology",    "New York University Shanghai",    "North China Electric Power University",    "Northeast Forestry University",    "Northeast Normal University",    "Northwest A&F University",    "Northwest University",    "Northwestern Polytechnical University",    "Ocean University of China",    "Peking Union Medical College",    "Shaanxi Normal University",    "Shenyang University of Technology",    "Soochow University (CN)",    "South China Agricultural University",    "South China Normal University",    "South China University of Technology",    "Southeast University",    "Southern Medical University",    "Southern University of Science and Technology",    "Southwest University",    "Sun Yat-Sen University",    "Sun Yat-sen University",    "Taiyuan University of Technology",    "The Chinese University of Hong Kong, Shenzhen",    "USTC",    "University of Electronic Science and Technology of China",    "University of International Business and Economics",    "University of Science and Technology of China",    "Westlake University",    "Xi'an Jiaotong-Liverpool University",    "Xinjiang University",    "Yunnan University",    "上海交通大学",    "上海大学",    "上海科技大学",    "上海财经大学",    "东北大学",    "东华大学",    "东南大学",    "中南大学",    "中国人民大学",    "中国农业大学",    "中国海洋大学",    "中国科学技术大学",    "中央民族大学",    "中央财经大学",    "中山大学",    "佛山大学",    "兰州大学",    "北京交通大学",    "北京大学",    "北京师范大学",    "北京理工大学",    "北京科技大学",    "北京航空航天大学",    "北京邮电大学",    "华东师范大学",    "华东理工大学",    "华中科技大学",    "华南师范大学",    "华南理工大学",    "南京大学",    "南京理工大学",    "南京航空航天大学",    "南京邮电大学",    "南开大学",    "南方科技大学",    "厦门大学",    "合肥工业大学",    "吉林大学",    "同济大学",    "哈尔滨工业大学",    "哈尔滨工程大学",    "四川大学",    "国防科技大学",    "复旦大学",    "大连理工大学",    "天津大学",    "对外经济贸易大学",    "山东大学",    "山东理工大学",    "暨南大学",    "杭州电子科技大学",    "武汉大学",    "武汉理工大学",    "武汉科技学院",    "河海大学",    "济南大学",    "浙江大学",    "深圳大学",    "清华大学",    "湖南大学",    "电子科技大学",    "苏州大学",    "西交利物浦大学",    "西北农林科技大学",    "西北工业大学",    "西南交通大学",    "西安交通大学",    "西安电子科技大学",    "西湖大学",    "郑州大学",    "重庆大学",    "首都师范大学",    "香港中文大学（深圳）"  ],  "exclude": [    "National Sun Yat-sen",    "Taiwan",    "Malaysia",    "Missouri"  ]}

04

十二所大学各行合计 2,854 人次，中国科学技术大学排在北京大学之前

十家机构在美国的 AI 员工中，本科在各院校的人数和占十家 4,582 人的比例，刻度 0% 到 40%。平台按词匹配院校名称；斜线条是严格计数：排除了名称里还含其他大学用词的记录

  1. 清华大学11%520
  2. 上海交通大学9%409
  3. 中国科学技术大学8%385
  4. 浙江大学8%345
  5. 北京大学7%332
  6. 复旦大学4%191
  7. 华中科技大学4%173
  8. 南京大学3%154
  9. 北京航空航天大学2%96
  10. 西安交通大学2%94
  11. 哈尔滨工业大学2%92
  12. 电子科技大学1%63
  13. 清单上的其他院校，至少38%1,728

图中所见

条形按占十家总数 4,582 人的比例画。人数按院校分别计数，一个人如果在两所院校读过本科会在两所都出现，所以十二行合计 2,854 人次，清单上其他院校的人数至少是 1,728。这十二所是提示词里事先列出的大型院校，不是全部院校的排名。

方法与局限

平台对院校名称按词匹配：名称的每个词都出现在记录里就算，所以"南京大学"也会匹配南京航空航天大学，"中国科学技术大学"也会匹配电子科技大学。斜线条的几行排除了含这些用词的记录，是严格计数，像"南京大学计算机科学与技术系"这样的写法也会被排除。

来源：Metix AI Platform 个人档案数据，2026-09-22。

查询population.json · institutions.json
POST /v1/people/query · queries/population.json
{  "note": "Who is counted. An AI role is a current job (experience.is_current eq true) whose title matches any of ai_title_terms; the lab and the recent-hire window sit in the same has_experience entry, so they describe that one job. US-based uses the profile's location.country: every profile with a mainland-China bachelor's in an AI role lists one (countries.json has no profile without a location), while the job-level location is often missing. The denominator is people with any Bachelor entry; the numerator is those whose Bachelor entry names an institution in institutions.json.",  "ai_title_terms": [    "machine learning",    "research scientist",    "research engineer",    "deep learning",    "member of technical staff",    "applied scientist",    "artificial intelligence",    "AI engineer",    "AI researcher",    "LLM",    "NLP",    "computer vision"  ],  "research_title_terms": [    "scientist",    "researcher"  ],  "recent_hire_window": "now-24m",  "us": {    "field": "location.country",    "eq": "United States"  },  "labs": [    {      "id": "openai",      "name": "OpenAI",      "companies": [        "OpenAI"      ]    },    {      "id": "anthropic",      "name": "Anthropic",      "companies": [        "Anthropic"      ]    },    {      "id": "google-deepmind",      "name": "Google DeepMind",      "companies": [        "Google DeepMind",        "DeepMind"      ]    },    {      "id": "xai",      "name": "xAI",      "companies": [        "xAI"      ]    },    {      "id": "meta",      "name": "Meta",      "companies": [        "Meta"      ]    },    {      "id": "nvidia",      "name": "NVIDIA",      "companies": [        "NVIDIA"      ]    },    {      "id": "google",      "name": "Google, other than Google DeepMind",      "companies": [        "Google"      ],      "note": "The company name Google also matches people whose current job is at Google DeepMind, because the Platform matches company names word by word. Google DeepMind comes earlier in the order, so its staff count there and not here."    },    {      "id": "microsoft",      "name": "Microsoft",      "companies": [        "Microsoft"      ]    },    {      "id": "apple",      "name": "Apple",      "companies": [        "Apple"      ]    },    {      "id": "amazon",      "name": "Amazon",      "companies": [        "Amazon",        "Amazon Web Services (AWS)",        "AWS"      ],      "note": "Amazon Web Services adds 254 profiles with a Bachelor entry to the 6,098 under Amazon."    }  ],  "institutions": [    {      "id": "tsinghua",      "name": "Tsinghua University",      "match": [        "Tsinghua"      ],      "exact": [        "清华大学"      ]    },    {      "id": "peking",      "name": "Peking University",      "match": [        "Peking University"      ],      "exact": [        "北京大学"      ]    },    {      "id": "sjtu",      "name": "Shanghai Jiao Tong University",      "match": [        "Shanghai Jiao Tong"      ],      "exact": [        "Shanghai Jiaotong University",        "上海交通大学"      ]    },    {      "id": "zju",      "name": "Zhejiang University",      "exact": [        "Zhejiang University",        "浙江大学"      ],      "exclude": [        "Sci-Tech",        "Normal",        "Gongshang",        "Finance",        "Media"      ]    },    {      "id": "ustc",      "name": "University of Science and Technology of China",      "exact": [        "University of Science and Technology of China",        "USTC",        "中国科学技术大学"      ],      "exclude": [        "Electronic"      ]    },    {      "id": "fudan",      "name": "Fudan University",      "match": [        "Fudan"      ],      "exact": [        "复旦大学"      ]    },    {      "id": "nju",      "name": "Nanjing University",      "exact": [        "Nanjing University",        "南京大学"      ],      "exclude": [        "Aeronautics",        "Posts",        "Information",        "Normal",        "Tech",        "Medical",        "Forestry",        "Agricultural",        "Audit",        "Finance",        "Science Technology"      ]    },    {      "id": "hust",      "name": "Huazhong University of Science and Technology",      "match": [        "Huazhong Science Technology"      ],      "exact": [        "华中科技大学"      ]    },    {      "id": "hit",      "name": "Harbin Institute of Technology",      "match": [        "Harbin Institute Technology"      ],      "exact": [        "哈尔滨工业大学"      ]    },    {      "id": "beihang",      "name": "Beihang University",      "match": [        "Beihang"      ],      "exact": [        "Beijing University of Aeronautics and Astronautics",        "北京航空航天大学"      ]    },    {      "id": "xjtu",      "name": "Xi'an Jiaotong University",      "exact": [        "Xi'an Jiaotong University",        "Xian Jiaotong University",        "西安交通大学"      ],      "exclude": [        "Liverpool"      ]    },    {      "id": "uestc",      "name": "University of Electronic Science and Technology of China",      "exact": [        "University of Electronic Science and Technology of China",        "UESTC",        "电子科技大学"      ]    }  ],  "institutions_note": "One institution per count. The Platform matches names word by word, so Nanjing University also matches Nanjing University of Aeronautics and Astronautics, and University of Science and Technology of China also matches University of Electronic Science and Technology of China. A row with exclude words leaves out entries that contain them, which makes it a strict count: entries such as Nanjing University, Department of Computer Science and Technology drop out too. The twelve are large mainland universities named in the prompt before any count was run; they are not a ranking of every institution, and others outside the twelve are not counted one by one.",  "countries": [    "United States",    "China",    "Canada",    "United Kingdom",    "Singapore",    "Germany",    "Australia",    "Switzerland",    "France",    "Japan",    "Hong Kong"  ],  "lab_order_note": "Rows do not overlap. A lab's row counts people whose current AI-titled job is at that lab and who hold no current job at a lab listed before it, so someone with current jobs at two labs counts once, at the first. The ten-lab row is the sum of the ten rows.",  "countries_note": "Values of the profile's location.country. Hong Kong is its own value on the Platform, listed here as a region."}
POST /v1/people/query · queries/institutions.json
{  "note": "Institutions located in mainland China, used on education.school.name inside one has_education entry together with education.degree eq Bachelor. A profile counts when one Bachelor entry names an institution on this list and none of the exclude words; nothing is inferred from personal names or languages. The Platform matches both match terms and the names in exact word by word: a name counts when every one of its words appears in the entry, in any order, so a longer name that contains a listed one also counts. Match terms are words that only mainland institutions use in their names (cities, provinces, distinctive names). The exclude words remove entries whose names contain a listed name but belong to an institution elsewhere: National Sun Yat-sen University in Taiwan contains Sun Yat-sen University, and Southeast Missouri State University contains Southeast University. Mainland campuses of foreign and Hong Kong universities count, because the rule is where the institution is. The list is built to leave out institutions in Hong Kong, Macau, and Taiwan. The school country recorded on an entry cannot be queried, so it cannot serve as the rule. The query limit of 64 conditions keeps the match terms to 37; less common institutions are listed by name.",  "how_built": "Built from the Bachelor entries of 250 profiles with a current AI title and a current location in China, read in full. The school country recorded on each entry is missing for many mainland institutions (Beihang University, Huazhong University of Science and Technology, and some Tsinghua University entries among them), so each institution's location was decided from its name and reviewed by hand. The list catches 173 of the 173 mainland Bachelor entries in that pool.",  "audit": "Two independent samples, read in full. 250 profiles with a current AI title at the ten labs, 25 per lab: the list caught 28 of the 28 mainland Bachelor entries. 200 US-based AI staff with a Bachelor entry at Meta, Amazon, NVIDIA, and Microsoft: the list caught 60 of 63 and matched one institution in Taiwan; the three it missed (Capital Medical University, Southern Medical University, Jimei University) are now listed and the Taiwan match is excluded. On the ten labs' US-based staff the exclude words remove 6 entries and the three added names add 5. Less common institutions still slip through, so shares built on the list are probably slightly low.",  "match": [    "Tsinghua",    "Peking University",    "Fudan",    "Tongji",    "Nankai",    "Beihang",    "Renmin",    "Huazhong",    "Jiaotong",    "Jiao Tong",    "Xidian",    "ShanghaiTech",    "SUSTech",    "Kunshan",    "Ningbo",    "Beijing",    "Shanghai",    "Tianjin",    "Chongqing",    "Nanjing",    "Wuhan",    "Harbin",    "Hangzhou",    "Hefei",    "Xiamen",    "Dalian",    "Qingdao",    "Guangzhou",    "Shenzhen",    "Chengdu",    "Zhengzhou",    "Jinan",    "Zhejiang",    "Sichuan",    "Shandong",    "Jilin",    "Hunan"  ],  "exact": [    "Anhui Agricultural University",    "Anhui University",    "Capital Medical University",    "Capital Normal University",    "Capital University of Economics and Business",    "Central South University",    "Central University of Finance and Economics",    "China Agricultural University",    "China Pharmaceutical University",    "China University of Geosciences",    "China University of Mining and Technology",    "China University of Petroleum",    "Communication University of China",    "Donghua University",    "Duke Kunshan University",    "East China Normal University",    "East China University of Science and Technology",    "Fujian Normal University",    "Fuzhou University",    "Guangdong University of Technology",    "Guangxi University",    "Guizhou University",    "Hainan University",    "Hebei University of Technology",    "Heilongjiang University",    "Henan University",    "Hohai University",    "Hubei University",    "Inner Mongolia University",    "Jiangnan University",    "Jiangsu University",    "Jiangxi University of Finance and Economics",    "Jilin Agricultural University",    "Jimei University",    "Kunming University of Science and Technology",    "Lanzhou University",    "Liaoning University",    "Minzu University of China",    "Nanchang University",    "Nanjing Forestry University",    "National University of Defense Technology",    "New York University Shanghai",    "North China Electric Power University",    "Northeast Forestry University",    "Northeast Normal University",    "Northwest A&F University",    "Northwest University",    "Northwestern Polytechnical University",    "Ocean University of China",    "Peking Union Medical College",    "Shaanxi Normal University",    "Shenyang University of Technology",    "Soochow University (CN)",    "South China Agricultural University",    "South China Normal University",    "South China University of Technology",    "Southeast University",    "Southern Medical University",    "Southern University of Science and Technology",    "Southwest University",    "Sun Yat-Sen University",    "Sun Yat-sen University",    "Taiyuan University of Technology",    "The Chinese University of Hong Kong, Shenzhen",    "USTC",    "University of Electronic Science and Technology of China",    "University of International Business and Economics",    "University of Science and Technology of China",    "Westlake University",    "Xi'an Jiaotong-Liverpool University",    "Xinjiang University",    "Yunnan University",    "上海交通大学",    "上海大学",    "上海科技大学",    "上海财经大学",    "东北大学",    "东华大学",    "东南大学",    "中南大学",    "中国人民大学",    "中国农业大学",    "中国海洋大学",    "中国科学技术大学",    "中央民族大学",    "中央财经大学",    "中山大学",    "佛山大学",    "兰州大学",    "北京交通大学",    "北京大学",    "北京师范大学",    "北京理工大学",    "北京科技大学",    "北京航空航天大学",    "北京邮电大学",    "华东师范大学",    "华东理工大学",    "华中科技大学",    "华南师范大学",    "华南理工大学",    "南京大学",    "南京理工大学",    "南京航空航天大学",    "南京邮电大学",    "南开大学",    "南方科技大学",    "厦门大学",    "合肥工业大学",    "吉林大学",    "同济大学",    "哈尔滨工业大学",    "哈尔滨工程大学",    "四川大学",    "国防科技大学",    "复旦大学",    "大连理工大学",    "天津大学",    "对外经济贸易大学",    "山东大学",    "山东理工大学",    "暨南大学",    "杭州电子科技大学",    "武汉大学",    "武汉理工大学",    "武汉科技学院",    "河海大学",    "济南大学",    "浙江大学",    "深圳大学",    "清华大学",    "湖南大学",    "电子科技大学",    "苏州大学",    "西交利物浦大学",    "西北农林科技大学",    "西北工业大学",    "西南交通大学",    "西安交通大学",    "西安电子科技大学",    "西湖大学",    "郑州大学",    "重庆大学",    "首都师范大学",    "香港中文大学（深圳）"  ],  "exclude": [    "National Sun Yat-sen",    "Taiwan",    "Malaysia",    "Missouri"  ]}

他们的档案所在地

不限机构，在 AI 岗位、本科在中国大陆院校的档案共 15,802 份，其中 12,194 份所在国家是美国，其后是新加坡（722）、加拿大（577）和英国（399）。这些数字说不出有多少人在中国工作：整个 Metix AI Platform 上所在国家写中国的 AI 岗位档案只有 32 份，中国本土机构也几乎看不到（2026-09-22，Tencent 当前职位为 AI 岗位的可见档案为 92 份）。

运行这个案例

三种方式，每一种都先告诉你要花多少。

1 API Credit 可以买 25 个搜索结果或 5 条完整记录；1 美元可以买 30 API Credits。

交给你的 agent 来跑

235 到 265 API Credits7.83 到 8.83 美元超过新账户赠送的 100 API Credits

花费超过 300 API Credits 之前，agent 会先停下来问你。

你的 agent 按提示词一步步执行：先读规则，再计数，按提示词的要求检查定义，最后写出文件和图表。请使用能写文件的 agent，比如 Claude Code 或 Codex。

一次性配置key、连接和一次免费检查。如果你的 agent 已经接入 Metix AI Platform，可以跳过。

  1. 1获取 key

    在 Metix AI Platform 上创建 key →

    新账户一次性赠送 100 API Credits，30 天内有效。在启动 agent 的终端里设置，或者把这一行写进 ~/.zshrc 或 ~/.bashrc，新开的终端也能用：

    终端
    export METIX_KEY=metix_xxxxxxxx

  2. 2连接你的 agent

    Claude Code

    为所有项目注册 Metix AI Platform。在任意目录启动 claude，就能看到十个 metix 工具。

    终端
    : "${METIX_KEY:?run step 1 first}" &&
claude mcp add --scope user --transport http metix \
  https://mira-api.metix.ai/mcp \
  --header "Authorization: Bearer $METIX_KEY"
    MCP 配置指南 →

    Codex

    注册同一个服务。key 留在环境变量里，不写进配置文件。

    终端
    codex mcp add metix \
  --url https://mira-api.metix.ai/mcp \
  --bearer-token-env-var METIX_KEY
    MCP 配置指南 →

    Skills

    四个 skill，教任何 agent 使用 Metix AI Platform 的接口和查询规则，适合不支持 MCP 的 agent。安装程序默认一项都不勾选：在每一项上按空格，再按回车。

    终端
    npx skills add MetixAI-Official/metix-skills
    Skills 安装说明 →

    其他 MCP

    让客户端通过 streamable HTTP 连接这个地址，并带上这两个请求头；缺少 Accept 请求头时服务器会返回 406。较旧的客户端使用同一主机上的 /sse。

    地址和请求头
    https://mira-api.metix.ai/mcp
Authorization: Bearer <your key>
Accept: application/json, text/event-stream
    MCP 配置指南 →

  3. 3检查配置

    先问这一句。它只读取余额和字段列表，不做任何搜索，不花 API Credits：

    发给 agent 的提示
    使用 Metix AI Platform：查询我的 key 状态并读取 contract，这两项都免费。然后告诉我我的 API Credit 余额和可以查询哪些数据集。不要做任何搜索。

4粘贴提示词

在一个空文件夹里启动 agent，再粘贴。它会把文件写在那里。

要回答的问题

用 Metix AI Platform 回答一个问题：十家大型 AI 机构里，从事 AI 岗位的人有多大比例本科就读于中国大陆院校？有这种背景的 AI 从业者现在的档案写在哪个国家或地区？只通过公开的 Platform 访问（REST 地址 https://mira-api.metix.ai、MCP 服务或 metix-skills），密钥从 METIX_KEY 读取，任何时候都不要打印密钥。

  1. 01先读规则再查询

    调用 GET /contract（免费），所有条件只用 querySpecByEntity.profile 里的字段。同一份工作的条件放在同一个 has_experience 里，同一个学位的条件放在同一个 has_education 里，这样它们说的才是同一份工作、同一个学位。一次查询最多 64 个条件，嵌套最多 6 层。size 1 的计数花 1 API Credit，所以每个要发布的数字都按计数来设计。开始和结束时各调用一次 GET /auth/key/status（免费）查余额，总花费超过 300 API Credits 之前先停下来问我。

  2. 02人群

    AI 岗位指当前的一份工作（experience.is_current eq true），职位名称 experience.title 匹配以下任意一个：machine learning、research scientist、research engineer、deep learning、member of technical staff、applied scientist、artificial intelligence、AI engineer、AI researcher、LLM、NLP、computer vision。十家机构按这个顺序是 OpenAI、Anthropic、Google DeepMind（公司名 "Google DeepMind" 和 "DeepMind"）、xAI、Meta、NVIDIA、Google、Microsoft、Apple 和 Amazon（加上 "Amazon Web Services (AWS)" 和 "AWS"）。每个公司名和变体都先用计数核对。平台按词匹配公司名称，所以 Google 也会匹配到 Google DeepMind。各行要互不重叠：每家机构只算当前在这里做 AI 岗位、且当前不在顺序更靠前的机构任职的人，这样十家合计就是各行之和。

  3. 03学历口径

    一条教育经历的 education.degree eq "Bachelor"，且院校位于中国大陆，这个人就计入。只看院校，不看人：不用姓名，不用语言。分母是有任意一条 Bachelor 经历的人。外国和香港高校在中国大陆设立的校区算大陆院校；清单不含香港、澳门、台湾的院校。

  4. 04建院校列表

    院校所在国家在读取详情时会返回，但不能用来查询，而且很多大陆院校这一项是空的。搜索 250 个当前 AI 岗位在中国的档案（size 250），用 POST /entity/v1/profiles/detail-by-id 读取，_source 只取教育字段，按院校名称判断每所本科院校的所在地。平台对院校名称按词匹配，match 词和 in 里的名称都一样，所以含有清单名称全部用词的更长名称也会被匹配。match 词只用大陆院校名称才会出现的词（城市、省份，以及 Tsinghua、Fudan 这类独有的名字），其他名称写全称，再在同一个 has_education 里用 not 加上排除词，去掉含有清单名称但属于其他地方的院校（台湾的 National Sun Yat-sen University 含有 Sun Yat-sen University 的全部用词，Southeast Missouri State 含有 Southeast University）。每个排除词都用计数核对。总条件数控制在 64 以内。

  5. 05核对列表

    从每家机构读 25 个 AI 岗位档案（共 250 个），看列表能抓到多少条大陆本科经历，有没有误匹配到大陆以外的院校。修改列表再核对。报告样本量和结果，并说明不常见的院校仍可能漏掉。

  6. 06地点

    比较工作层面的 experience.location.country 和档案层面的 location.country 在这些人里各有多少人填写。"在美国"用覆盖更好的那个字段，全球数字作为背景，并说明理由。

  7. 07计数

    每家机构按第 2 步互不重叠地分别算，都限在美国：AI 岗位人数；其中有任意 Bachelor 经历的；其中本科在中国大陆院校的；后两项在当前工作开始于最近 24 个月的人里的数字（同一份工作里加 experience.start_date gte "now-24m"）；后两项在职位名称同时匹配 "scientist" 或 "researcher" 的人里的数字。十家合计用各行之和，但只要某一列有被隐藏的格子，这一列就不给十家合计。再用一个列出所有公司名的查询算十家在全球的数字作为背景。然后在十家机构的美国 AI 员工里逐个数 12 所大学；名称还会匹配到其他大学的，加上排除词，并标为严格计数。最后看本科在中国大陆院校的 AI 从业者的档案写在哪个国家或地区。每个关于人的计数都要过小格子规则（1 到 9 写成 "<10"）；写出文件之前，检查读者能用已公开的格子相减得到的每一个数，只要有一个在 1 到 9 之间，就多隐藏一些再检查。

  8. 08输出

    写出 labs.json、institutions.json、countries.json、context.json，每个都带 "unit": "profiles"、快照日期和来源查询文件；院校列表连同构建和核对过程一起提交。原始记录放在 data/raw/，永远不公开。

  9. 09图表

    各机构在美国的比例，排序，并标出十家合计的比例；每家机构当前工作开始得更早的人和开始于最近 24 个月的人对比；职位名称含 scientist 或 researcher 的和同一机构其他 AI 职位对比；12 所大学的人数，严格计数用斜线条；中国大陆以外的各国家和地区按占总数的比例画。每张图的标题用中性、客观的措辞写结论。上下限取整时方向不变：下限向下取，上限向上取。

  10. 10局限

    人数是可见的下限，不是在职人数；比例是可见且写了本科经历的档案内部的比例，可能高于也可能低于全部员工中的实际比例。没有 Bachelor 经历的人不进任何比例的分子或分母。档案里的国家是本人填写的所在地，不一定是工作地点。几乎没有档案把国家写成中国，当前工作记录在中国的人大多写的是别的国家，所以这些数据说不出有多少人在中国工作；要明确写出这一点，给出总部在中国的雇主的可见人数，也不对有多少人在中国工作做任何判断。更早的一组只包括仍在这份工作上的人，所以两组之间的差距反映的也可能是谁留下，而不只是招了谁。这是一天的截面，不是趋势。不用安全、忠诚或国籍的叙事框架。

查看 PROMPT.md →

你会得到

聚合文件和图表，一段说明抽检发现了什么、改了什么，以及这次运行花了多少 API Credits（取自运行前后的余额）。

如果调用返回 402 insufficient_quota，说明 key 有效，只是余额用完了。

复现数字

104 API Credits3.47 美元超过新账户赠送的 100 API Credits

一个只用 Python 标准库的小脚本，把已提交的查询按计数发出去，写出这个页面所用的聚合文件。需要 Python，并在终端里设置好 METIX_KEY（见 agent 路径的第 1 步），也可以让你的 agent 替你运行这几行。

终端
curl -fsSL https://platform.metix.ai/casebook/source/china-educated-ai-talent-2026.tar.gz | tar xz
cd china-educated-ai-talent-2026
: "${METIX_KEY:?set METIX_KEY first}" && python3 cases/china-educated-ai-talent-2026/fetch.py

你会得到

data/*.json 和 data/receipt.json。运行 git diff cases/china-educated-ai-talent-2026/data 看哪些数字变了：除去快照之后数据本身的变化，数字应该一致。

改成你的问题

花费取决于你的版本读取多少。在提示词第 1 步里写上你自己的上限。

提示词就是这个案例本身。改掉下表里的部分，你的 agent 就会回答你的问题，用同样的检查和同样的花费记录方式。

想改的 改哪里 例子
机构 第 2 步，每个公司名都先用计数核对 前沿创业公司：Mistral AI、Cohere、Perplexity
岗位 第 2 步的职位关键词 数据工程或芯片设计职位
学历口径 第 3 到 5 步 印度的院校，用同样的方法建列表和核对

运行前先问清楚

  1. 看哪些机构？有没有公司名会同时匹配到母公司或别的公司？
  2. 哪些岗位？用职位名称里的哪些词？
  3. 看哪个国家的院校？境外校区算不算？
  4. 只看美国、看全球，还是都看？
  5. 建列表和核对读档案最多能花多少 API Credits？
查看 PROMPT.md →

方法与局限

统计范围怎么定义、怎么计数和抽检，以及这些数字不能说明什么。

人群

2026 年 9 月 22 日 Metix AI Platform 上的档案：当前职位（experience.is_current 为 true）在十家机构之一，职位名称匹配 12 个 AI 词之一，机构、职位和时间条件放在同一个 has_experience 里，描述的是同一份工作。Google DeepMind 包括 "Google DeepMind" 和 "DeepMind" 两种写法；Amazon 包括 AWS。各行不重叠：一个人如果同时在两家机构有当前职位，只算在清单中靠前的那一家。平台按词匹配公司名称，"Google" 也会匹配 Google DeepMind，因为 Google DeepMind 排在前面，那里的员工只算在 Google DeepMind。

院校清单

先读了 250 份当前在中国工作的 AI 岗位档案，收集本科院校，逐个按院校本身判断是否位于中国大陆（记录里的院校国家字段常常缺失，也不能用来查询）。清单由 37 个匹配词和 145 个院校名称组成。平台对两者都按词匹配：名称的每个词都出现在记录里就算，所以更长的名称也会被匹配到；4 个排除词去掉其中属于其他地方的院校，比如台湾的国立中山大学含有"中山大学"的全部用词。做了两次独立核对：十家机构共 250 份档案，28 条中国大陆本科记录全部命中；Meta、Amazon、NVIDIA、Microsoft 在美国的 200 份档案，63 条中命中 60 条，另误配了一所台湾院校。漏掉的三所院校已补进清单，误配的已排除。不常见院校仍可能漏掉，所以比例可能略微偏低。清单在 queries/institutions.json。

在美国

按档案本身的 location.country 判断：这一人群的每份档案都写了所在国家，而工作经历里的地点常常缺失。

最近开始的工作与职位类型

"当前工作开始于最近 24 个月"是当前这份工作的开始时间不早于快照前 24 个月。scientist 或 researcher 指职位名称含这两个词之一，Applied Scientist 也算在内。差距是否超出偶然用双比例 z 检验，把可见档案当作样本，这是一个近似；图 03 比较了 7 家，所以 |z| 在 1.96 到 2.69 之间只算勉强超过。

小数字

少于 10 人的数字显示为 "<10"。因为各行不重叠、十家合计是各行之和，职位类型这一列有几家不能公开，所以不公开这一列的十家合计，否则用减法就能算回来。复现脚本在写出文件之前会检查每一个能用减法算出的数字，只要有一个在 1 到 9 之间就停下，不写文件。

局限

所有人数都是下限；比例是可见且写了本科经历的档案内部的比例，可能高于也可能低于全部员工中的实际比例。按院校所在地定义人群，不推断国籍。中国本土的档案几乎不可见，所以不能统计有多少人在中国工作。快照当天的截面，不是趋势。

最近一次复现

上一次复现花了多少，取自运行前后 Metix AI Platform 记录的余额。

运行日期
2026-09-22
调用次数
105
搜索结果
104
读取记录
0
API Credits
104

搜索结果是搜索返回的 ID 数，每次计数查询算一个，完整搜索按命中数算。记录是完整读取的岗位或档案：这个案例一条都没读。

做这个案例另外花了大约 203 API Credits：agent 做的抽检、试探性查询，以及被公开复现取代的早先运行。你不需要再花这部分。

案例文件：提示词、查询、数据API Credits 与价格