写明 Meta 超级智能实验室的人中，77% 在实验室成立时已在 Meta 工作

{  "note": "Who is counted. A person counts when one job entry is at Meta and current (experience.company.name match current_employer and experience.is_current eq true, in one has_experience entry) and the profile's headline or current_title names the lab with one of lab_terms. The Platform matches text word by word, in any order, and ignores case, so MSL also matches msl and superintelligence also matches SuperIntelligence. Counts are profiles visible through the Metix AI Platform, never the lab's headcount; stale profiles count as current, so a count is not a guaranteed floor either.",  "current_employer": "Meta",  "current_employer_note": "match Meta on the current entry. The name also matches entries written as Facebook: a current entry named Facebook counts, and a Facebook condition returned all 790 profiles of the narrow definition, so the two names are one employer on the Platform and a Facebook condition is no separate test. company.name eq Meta returned the same 790 profiles as match Meta, and every one of the 63 profiles read in the audit has a current entry at Meta itself.",  "lab_terms": [    "superintelligence",    "MSL"  ],  "lab_fields": [    "headline",    "current_title"  ],  "lab_terms_note": "superintelligence alone (the narrow definition) gives 790 profiles. MSL, the lab's short name, adds 146 current Meta profiles that never write superintelligence; 13 of 14 read in a whole slice use it for the lab, and the rest recruit for it. FAIR, one of the lab's groups since 2025, is not a lab term: people who name only FAIR do not name the lab, and teams.json counts them as context (fair_only).",  "narrow_terms": [    "superintelligence"  ],  "history_names": [    "Meta",    "Facebook",    "Instagram",    "WhatsApp",    "Oculus"  ],  "history_names_note": "Names that count as an earlier job at Meta, matched word by word in one has_experience entry. The name Meta does not match an entry listed under Instagram; adding the other names moves fewer than 10 people.",  "cut": "2025-06-01",  "cut_note": "Meta announced Meta Superintelligence Labs at the end of June 2025. A person with a job entry at a history name that started before the cut had a Meta job before the lab. experience.start_date is a month (YYYY-MM) or a year; the Platform compares it as its first day, so a year alone reads as January. A newcomer whose Meta entry says only 2025 therefore counts as at Meta before the cut, which raises the share at Meta when the lab formed, and an earlier entry whose end date says only 2025 counts as ending before May 2025, which can move someone who was at Meta into returned.",  "stay_end": "2025-05-01",  "cohorts_note": "Four groups that do not overlap and add up to the whole. Stayed: a non-intern Meta entry that started before the cut and is current or ended in May 2025 or later, so the person was at Meta when the lab formed. Returned: a non-intern Meta entry before the cut, but none that lasted into May 2025, so they had left and came back. Former interns: the only Meta entries before the cut are internships. New to Meta: no Meta entry that started before the cut, including fewer than 10 whose current Meta entry has no start date. Returned, former interns, and new to Meta together are the outside hires, everyone who was not at Meta when the lab formed.",  "intern_rule": "An entry is an internship when experience.seniority eq Intern or experience.title matches intern. Both are tested inside the same has_experience entry as the employer and the date.",  "labeling_terms": [    "labeling",    "labelling",    "annotation",    "annotator",    "red teamer",    "red teaming"  ],  "labeling_note": "Titles and headlines that name data labeling, annotation, or red teaming. The audit found such roles among recent hires (fewer than 10 in the slice read); vendors often staff them, but the profile lists Meta.",  "freshness_note": "Profiles are a periodically refreshed snapshot. Across all Meta job entries on the Platform, 3,988 start in 2026, 1,104 of them in March and April and fewer than 10 in May or later, so start dates stop in April 2026 and the most recent months are probably undercounted. April 2026 is the last month shown, and people who joined since are missing.",  "audit": "63 profiles read in whole slices, never from the top of a search, because Search ranks by match quality. The slices covered people with no Meta-named entry before the cut, people with a Meta entry before the cut, people who write MSL but not superintelligence, and the profiles whose Meta entry has no start date (fewer than 10). 62 of 63 describe themselves as working in the lab, and the rest recruit for it. All 63 have a current Meta entry, almost all of them named Meta. All 31 read with a Meta entry before the cut had a real Meta job there, not only an internship. Among people without a Meta-named entry before the cut, an earlier Meta job turned up listed under Instagram, which is why Instagram is a history name, and fewer than 10 of them hold data labeling or red teaming roles. Separate reads of sub-team matches found fewer than 10 profiles that say they have left the lab while the Meta entry is still current, so a few members are out of date. Records read for the audit are kept private.",  "comparison": {    "id": "microsoft",    "label": "Microsoft",    "current_employer": "Microsoft",    "lab_terms": [      "superintelligence"    ],    "history_names": [      "Microsoft"    ],    "cut": "2025-11-01",    "stay_end": "2025-10-01",    "edges": [      "2012-01-01",      "2016-01-01",      "2020-01-01",      "2023-01-01",      "2025-01-01"    ],    "note": "Current Microsoft staff whose headline or current_title names superintelligence. Microsoft AI announced its superintelligence team in November 2025, so the cut for this group is 2025-11-01. Microsoft also matches Microsoft AI and Microsoft Research; subsidiaries with their own names (LinkedIn, GitHub) are not history names. A whole slice of 15 all named the Microsoft AI superintelligence team; fewer than 10 of them are recruiters or sourcers for it."  }}

{  "note": "What is counted, all within the population in queries/population.json unless a block says otherwise. Every count is sent with size 1, so it costs 1 API Credit, or nothing when it finds no one, and reads no record.",  "first_joined": {    "edges": [      "2012-01-01",      "2014-01-01",      "2016-01-01",      "2018-01-01",      "2020-01-01",      "2022-01-01",      "2023-01-01",      "2024-01-01",      "2025-01-01"    ],    "after_cut_edges": [      "2025-07-01",      "2025-08-01",      "2025-09-01",      "2025-10-01",      "2026-01-01",      "2026-04-01"    ],    "note": "A person's first Meta job started before a date exactly when they have an entry at a history name that started before it, so a count at each edge gives how many first joined before it, and the difference between two edges is a band of first-join dates. Bands run from the start of the record through the cut (the dates in edges) and on month by month and quarter by quarter to April 2026 (after_cut_edges). The last band holds April 2026 and anything later, and the fewer than 10 whose Meta entry has no start date. Adjacent bands merge wherever one would hold 1 to 9 people; the cut is never merged away. The second series counts only non-intern entries, so it gives each person's first Meta job that was not an internship; it runs to the cut, and everyone without such a job before the cut is one band."  },  "moves": {    "edges": [      "2025-01-01",      "2025-04-01",      "2025-06-01",      "2025-07-01",      "2025-08-01",      "2025-09-01",      "2025-10-01",      "2026-01-01",      "2026-04-01"    ],    "note": "For the people who stayed: when their latest current Meta entry started. A count of people with a current Meta entry that started on or after an edge gives how many started their newest current role since then, so a person with two current entries is placed by the newer one. Many people change teams inside Meta without adding an entry, so this shows who recorded a new role, a lower bound on who moved."  },  "sources": {    "ended_from": "2025-01-01",    "note": "Where the outside hires (everyone not at Meta when the lab formed) had just been: a job entry that ended in 2025 or later (experience.end_date gte ended_from), which also means it is no longer current. Internships do not count for companies (the intern rule in population.json), so a PhD student's summer at a lab is not where they came from; for academia they do count, because doctoral students' university entries often carry the Intern label. Rows do not overlap: each person counts at the first row in this order whose condition they meet, and the rest are other employers or none. ai_lab marks the five AI labs whose subtotal is published.",    "order": [      {        "id": "openai",        "label": "OpenAI",        "names": [          "OpenAI"        ],        "ai_lab": true      },      {        "id": "google-deepmind",        "label": "Google DeepMind",        "names": [          "DeepMind"        ],        "ai_lab": true      },      {        "id": "anthropic",        "label": "Anthropic",        "names": [          "Anthropic"        ],        "ai_lab": true      },      {        "id": "xai",        "label": "xAI",        "names": [          "xAI"        ],        "ai_lab": true      },      {        "id": "thinking-machines",        "label": "Thinking Machines Lab",        "names": [          "Thinking Machines"        ],        "ai_lab": true      },      {        "id": "apple",        "label": "Apple",        "names": [          "Apple"        ]      },      {        "id": "scale-ai",        "label": "Scale AI",        "names": [          "Scale AI"        ]      },      {        "id": "google",        "label": "Google, other than Google DeepMind",        "names": [          "Google"        ]      },      {        "id": "microsoft",        "label": "Microsoft",        "names": [          "Microsoft"        ]      },      {        "id": "amazon",        "label": "Amazon",        "names": [          "Amazon",          "AWS"        ]      },      {        "id": "nvidia",        "label": "NVIDIA",        "names": [          "NVIDIA"        ]      },      {        "id": "academia",        "label": "A university or other educational institution",        "company_type": "Educational"      }    ],    "context": [      {        "id": "google",        "label": "Google, including Google DeepMind",        "names": [          "Google",          "DeepMind"        ]      },      {        "id": "openai",        "label": "OpenAI",        "names": [          "OpenAI"        ]      },      {        "id": "anthropic",        "label": "Anthropic",        "names": [          "Anthropic"        ]      },      {        "id": "apple",        "label": "Apple",        "names": [          "Apple"        ]      },      {        "id": "microsoft",        "label": "Microsoft",        "names": [          "Microsoft"        ]      },      {        "id": "amazon",        "label": "Amazon",        "names": [          "Amazon",          "AWS"        ]      }    ],    "context_note": "For the whole group: anyone with a past (not current) non-intern job at the employer, at any time. These rows overlap and have no total. Names match word by word, so Google also matches Google DeepMind and Amazon matches Amazon Web Services (AWS). company.type Educational is the employer type the Platform records for universities and schools."  },  "roles": {    "functions": [      "Research",      "Engineering and Technical",      "Product"    ],    "senior": [      "Senior"    ],    "manager_up": [      "Manager",      "Head",      "Director",      "Vice President",      "President/Vice President",      "C-Level",      "Partner",      "Founder",      "Owner"    ],    "note": "current_function and current_seniority describe the current job and come from the Platform's fixed sets; either can be missing. Senior covers senior, staff, and principal individual contributors (the audit saw Staff, Senior Staff, and Principal titles labelled Senior); manager_up is Manager and every level above it; the rest of those with a stated level are Specialist or Intern. A doctorate is an education entry with education.degree eq Doctorate, finished or in progress.",    "baseline_note": "The baseline is Meta's other research staff: a current Meta entry, current_function eq Research, and no lab term in the headline or current title. It includes people who name FAIR only."  },  "teams": [    {      "id": "fair",      "label": "FAIR",      "terms": [        "FAIR"      ]    },    {      "id": "tbd-lab",      "label": "TBD Lab",      "terms": [        "TBD"      ]    },    {      "id": "infra",      "label": "MSL Infra and other infrastructure",      "terms": [        "infra",        "infrastructure"      ]    },    {      "id": "par",      "label": "Products and Applied Research",      "terms": [        "PAR",        "product applied research"      ]    }  ],  "teams_note": "Sub-team names in the headline or current title of people in the population, matched word by word; a person can name more than one. Audited: FAIR, 10 profiles naming it read in whole slices, nearly all naming it as the current team. TBD, all 13 matches read: 10 are in TBD Lab or report to it, and the rest name TBD while recruiting for it, while saying they are not in it, or while saying they have left the lab. Infra, the first 15 of 49 matches for infra or infrastructure (not a whole slice): all 15 work on infrastructure inside the lab, 11 of them naming MSL Infra or Infra at MSL. Products and Applied Research: the words applied research mostly match the job title Applied Research Scientist (most of those read) or an old team name, so the row needs PAR or all of product, applied, and research. fair_only counts current Meta profiles that name FAIR and no lab term; it was not audited outside the population.",  "places": {    "states": [      "California",      "New York",      "Washington"    ],    "bay_area": [      "Menlo Park",      "San Francisco",      "Sunnyvale",      "Palo Alto",      "Mountain View",      "Redwood City",      "Burlingame",      "Fremont",      "San Jose",      "Santa Clara",      "San Mateo",      "Foster City",      "Oakland",      "Berkeley",      "Cupertino",      "Los Altos",      "Belmont",      "San Carlos",      "Millbrae",      "San Bruno",      "Newark",      "Milpitas",      "Los Gatos",      "Campbell",      "Saratoga",      "Daly City",      "South San Francisco",      "Emeryville",      "Alameda",      "Hayward",      "Dublin",      "Pleasanton",      "Walnut Creek",      "Atherton",      "Portola Valley",      "Woodside",      "Half Moon Bay"    ],    "countries": [      "United Kingdom"    ],    "note": "The profile's own location, where people list themselves: location.country and location.state, and location.city for the Bay Area, whose cities are listed one by one and compared exactly."  },  "postings": {    "company": "Meta",    "title_terms": [      "superintelligence",      "MSL"    ],    "description_terms": [      "superintelligence labs"    ],    "research_title": "research",    "cities": [      "Menlo Park",      "San Francisco"    ],    "states": [      "New York"    ],    "experience_max_months": 36,    "posted_from": "2026-09-01",    "note": "Open Meta postings (is_open eq true) whose title names superintelligence or MSL, or whose description contains the words superintelligence and labs. All 30 read on 2026-09-22 are Meta postings whose description names Meta Superintelligence Labs. Every one carries the seniority label Not Applicable, so seniority says nothing here, and all were posted in September 2026, so the index shows the openings of the moment rather than a history. Counts of postings carry no minimum."  }}

{  "note": "Who is counted. A person counts when one job entry is at Meta and current (experience.company.name match current_employer and experience.is_current eq true, in one has_experience entry) and the profile's headline or current_title names the lab with one of lab_terms. The Platform matches text word by word, in any order, and ignores case, so MSL also matches msl and superintelligence also matches SuperIntelligence. Counts are profiles visible through the Metix AI Platform, never the lab's headcount; stale profiles count as current, so a count is not a guaranteed floor either.",  "current_employer": "Meta",  "current_employer_note": "match Meta on the current entry. The name also matches entries written as Facebook: a current entry named Facebook counts, and a Facebook condition returned all 790 profiles of the narrow definition, so the two names are one employer on the Platform and a Facebook condition is no separate test. company.name eq Meta returned the same 790 profiles as match Meta, and every one of the 63 profiles read in the audit has a current entry at Meta itself.",  "lab_terms": [    "superintelligence",    "MSL"  ],  "lab_fields": [    "headline",    "current_title"  ],  "lab_terms_note": "superintelligence alone (the narrow definition) gives 790 profiles. MSL, the lab's short name, adds 146 current Meta profiles that never write superintelligence; 13 of 14 read in a whole slice use it for the lab, and the rest recruit for it. FAIR, one of the lab's groups since 2025, is not a lab term: people who name only FAIR do not name the lab, and teams.json counts them as context (fair_only).",  "narrow_terms": [    "superintelligence"  ],  "history_names": [    "Meta",    "Facebook",    "Instagram",    "WhatsApp",    "Oculus"  ],  "history_names_note": "Names that count as an earlier job at Meta, matched word by word in one has_experience entry. The name Meta does not match an entry listed under Instagram; adding the other names moves fewer than 10 people.",  "cut": "2025-06-01",  "cut_note": "Meta announced Meta Superintelligence Labs at the end of June 2025. A person with a job entry at a history name that started before the cut had a Meta job before the lab. experience.start_date is a month (YYYY-MM) or a year; the Platform compares it as its first day, so a year alone reads as January. A newcomer whose Meta entry says only 2025 therefore counts as at Meta before the cut, which raises the share at Meta when the lab formed, and an earlier entry whose end date says only 2025 counts as ending before May 2025, which can move someone who was at Meta into returned.",  "stay_end": "2025-05-01",  "cohorts_note": "Four groups that do not overlap and add up to the whole. Stayed: a non-intern Meta entry that started before the cut and is current or ended in May 2025 or later, so the person was at Meta when the lab formed. Returned: a non-intern Meta entry before the cut, but none that lasted into May 2025, so they had left and came back. Former interns: the only Meta entries before the cut are internships. New to Meta: no Meta entry that started before the cut, including fewer than 10 whose current Meta entry has no start date. Returned, former interns, and new to Meta together are the outside hires, everyone who was not at Meta when the lab formed.",  "intern_rule": "An entry is an internship when experience.seniority eq Intern or experience.title matches intern. Both are tested inside the same has_experience entry as the employer and the date.",  "labeling_terms": [    "labeling",    "labelling",    "annotation",    "annotator",    "red teamer",    "red teaming"  ],  "labeling_note": "Titles and headlines that name data labeling, annotation, or red teaming. The audit found such roles among recent hires (fewer than 10 in the slice read); vendors often staff them, but the profile lists Meta.",  "freshness_note": "Profiles are a periodically refreshed snapshot. Across all Meta job entries on the Platform, 3,988 start in 2026, 1,104 of them in March and April and fewer than 10 in May or later, so start dates stop in April 2026 and the most recent months are probably undercounted. April 2026 is the last month shown, and people who joined since are missing.",  "audit": "63 profiles read in whole slices, never from the top of a search, because Search ranks by match quality. The slices covered people with no Meta-named entry before the cut, people with a Meta entry before the cut, people who write MSL but not superintelligence, and the profiles whose Meta entry has no start date (fewer than 10). 62 of 63 describe themselves as working in the lab, and the rest recruit for it. All 63 have a current Meta entry, almost all of them named Meta. All 31 read with a Meta entry before the cut had a real Meta job there, not only an internship. Among people without a Meta-named entry before the cut, an earlier Meta job turned up listed under Instagram, which is why Instagram is a history name, and fewer than 10 of them hold data labeling or red teaming roles. Separate reads of sub-team matches found fewer than 10 profiles that say they have left the lab while the Meta entry is still current, so a few members are out of date. Records read for the audit are kept private.",  "comparison": {    "id": "microsoft",    "label": "Microsoft",    "current_employer": "Microsoft",    "lab_terms": [      "superintelligence"    ],    "history_names": [      "Microsoft"    ],    "cut": "2025-11-01",    "stay_end": "2025-10-01",    "edges": [      "2012-01-01",      "2016-01-01",      "2020-01-01",      "2023-01-01",      "2025-01-01"    ],    "note": "Current Microsoft staff whose headline or current_title names superintelligence. Microsoft AI announced its superintelligence team in November 2025, so the cut for this group is 2025-11-01. Microsoft also matches Microsoft AI and Microsoft Research; subsidiaries with their own names (LinkedIn, GitHub) are not history names. A whole slice of 15 all named the Microsoft AI superintelligence team; fewer than 10 of them are recruiters or sourcers for it."  }}

{  "note": "What is counted, all within the population in queries/population.json unless a block says otherwise. Every count is sent with size 1, so it costs 1 API Credit, or nothing when it finds no one, and reads no record.",  "first_joined": {    "edges": [      "2012-01-01",      "2014-01-01",      "2016-01-01",      "2018-01-01",      "2020-01-01",      "2022-01-01",      "2023-01-01",      "2024-01-01",      "2025-01-01"    ],    "after_cut_edges": [      "2025-07-01",      "2025-08-01",      "2025-09-01",      "2025-10-01",      "2026-01-01",      "2026-04-01"    ],    "note": "A person's first Meta job started before a date exactly when they have an entry at a history name that started before it, so a count at each edge gives how many first joined before it, and the difference between two edges is a band of first-join dates. Bands run from the start of the record through the cut (the dates in edges) and on month by month and quarter by quarter to April 2026 (after_cut_edges). The last band holds April 2026 and anything later, and the fewer than 10 whose Meta entry has no start date. Adjacent bands merge wherever one would hold 1 to 9 people; the cut is never merged away. The second series counts only non-intern entries, so it gives each person's first Meta job that was not an internship; it runs to the cut, and everyone without such a job before the cut is one band."  },  "moves": {    "edges": [      "2025-01-01",      "2025-04-01",      "2025-06-01",      "2025-07-01",      "2025-08-01",      "2025-09-01",      "2025-10-01",      "2026-01-01",      "2026-04-01"    ],    "note": "For the people who stayed: when their latest current Meta entry started. A count of people with a current Meta entry that started on or after an edge gives how many started their newest current role since then, so a person with two current entries is placed by the newer one. Many people change teams inside Meta without adding an entry, so this shows who recorded a new role, a lower bound on who moved."  },  "sources": {    "ended_from": "2025-01-01",    "note": "Where the outside hires (everyone not at Meta when the lab formed) had just been: a job entry that ended in 2025 or later (experience.end_date gte ended_from), which also means it is no longer current. Internships do not count for companies (the intern rule in population.json), so a PhD student's summer at a lab is not where they came from; for academia they do count, because doctoral students' university entries often carry the Intern label. Rows do not overlap: each person counts at the first row in this order whose condition they meet, and the rest are other employers or none. ai_lab marks the five AI labs whose subtotal is published.",    "order": [      {        "id": "openai",        "label": "OpenAI",        "names": [          "OpenAI"        ],        "ai_lab": true      },      {        "id": "google-deepmind",        "label": "Google DeepMind",        "names": [          "DeepMind"        ],        "ai_lab": true      },      {        "id": "anthropic",        "label": "Anthropic",        "names": [          "Anthropic"        ],        "ai_lab": true      },      {        "id": "xai",        "label": "xAI",        "names": [          "xAI"        ],        "ai_lab": true      },      {        "id": "thinking-machines",        "label": "Thinking Machines Lab",        "names": [          "Thinking Machines"        ],        "ai_lab": true      },      {        "id": "apple",        "label": "Apple",        "names": [          "Apple"        ]      },      {        "id": "scale-ai",        "label": "Scale AI",        "names": [          "Scale AI"        ]      },      {        "id": "google",        "label": "Google, other than Google DeepMind",        "names": [          "Google"        ]      },      {        "id": "microsoft",        "label": "Microsoft",        "names": [          "Microsoft"        ]      },      {        "id": "amazon",        "label": "Amazon",        "names": [          "Amazon",          "AWS"        ]      },      {        "id": "nvidia",        "label": "NVIDIA",        "names": [          "NVIDIA"        ]      },      {        "id": "academia",        "label": "A university or other educational institution",        "company_type": "Educational"      }    ],    "context": [      {        "id": "google",        "label": "Google, including Google DeepMind",        "names": [          "Google",          "DeepMind"        ]      },      {        "id": "openai",        "label": "OpenAI",        "names": [          "OpenAI"        ]      },      {        "id": "anthropic",        "label": "Anthropic",        "names": [          "Anthropic"        ]      },      {        "id": "apple",        "label": "Apple",        "names": [          "Apple"        ]      },      {        "id": "microsoft",        "label": "Microsoft",        "names": [          "Microsoft"        ]      },      {        "id": "amazon",        "label": "Amazon",        "names": [          "Amazon",          "AWS"        ]      }    ],    "context_note": "For the whole group: anyone with a past (not current) non-intern job at the employer, at any time. These rows overlap and have no total. Names match word by word, so Google also matches Google DeepMind and Amazon matches Amazon Web Services (AWS). company.type Educational is the employer type the Platform records for universities and schools."  },  "roles": {    "functions": [      "Research",      "Engineering and Technical",      "Product"    ],    "senior": [      "Senior"    ],    "manager_up": [      "Manager",      "Head",      "Director",      "Vice President",      "President/Vice President",      "C-Level",      "Partner",      "Founder",      "Owner"    ],    "note": "current_function and current_seniority describe the current job and come from the Platform's fixed sets; either can be missing. Senior covers senior, staff, and principal individual contributors (the audit saw Staff, Senior Staff, and Principal titles labelled Senior); manager_up is Manager and every level above it; the rest of those with a stated level are Specialist or Intern. A doctorate is an education entry with education.degree eq Doctorate, finished or in progress.",    "baseline_note": "The baseline is Meta's other research staff: a current Meta entry, current_function eq Research, and no lab term in the headline or current title. It includes people who name FAIR only."  },  "teams": [    {      "id": "fair",      "label": "FAIR",      "terms": [        "FAIR"      ]    },    {      "id": "tbd-lab",      "label": "TBD Lab",      "terms": [        "TBD"      ]    },    {      "id": "infra",      "label": "MSL Infra and other infrastructure",      "terms": [        "infra",        "infrastructure"      ]    },    {      "id": "par",      "label": "Products and Applied Research",      "terms": [        "PAR",        "product applied research"      ]    }  ],  "teams_note": "Sub-team names in the headline or current title of people in the population, matched word by word; a person can name more than one. Audited: FAIR, 10 profiles naming it read in whole slices, nearly all naming it as the current team. TBD, all 13 matches read: 10 are in TBD Lab or report to it, and the rest name TBD while recruiting for it, while saying they are not in it, or while saying they have left the lab. Infra, the first 15 of 49 matches for infra or infrastructure (not a whole slice): all 15 work on infrastructure inside the lab, 11 of them naming MSL Infra or Infra at MSL. Products and Applied Research: the words applied research mostly match the job title Applied Research Scientist (most of those read) or an old team name, so the row needs PAR or all of product, applied, and research. fair_only counts current Meta profiles that name FAIR and no lab term; it was not audited outside the population.",  "places": {    "states": [      "California",      "New York",      "Washington"    ],    "bay_area": [      "Menlo Park",      "San Francisco",      "Sunnyvale",      "Palo Alto",      "Mountain View",      "Redwood City",      "Burlingame",      "Fremont",      "San Jose",      "Santa Clara",      "San Mateo",      "Foster City",      "Oakland",      "Berkeley",      "Cupertino",      "Los Altos",      "Belmont",      "San Carlos",      "Millbrae",      "San Bruno",      "Newark",      "Milpitas",      "Los Gatos",      "Campbell",      "Saratoga",      "Daly City",      "South San Francisco",      "Emeryville",      "Alameda",      "Hayward",      "Dublin",      "Pleasanton",      "Walnut Creek",      "Atherton",      "Portola Valley",      "Woodside",      "Half Moon Bay"    ],    "countries": [      "United Kingdom"    ],    "note": "The profile's own location, where people list themselves: location.country and location.state, and location.city for the Bay Area, whose cities are listed one by one and compared exactly."  },  "postings": {    "company": "Meta",    "title_terms": [      "superintelligence",      "MSL"    ],    "description_terms": [      "superintelligence labs"    ],    "research_title": "research",    "cities": [      "Menlo Park",      "San Francisco"    ],    "states": [      "New York"    ],    "experience_max_months": 36,    "posted_from": "2026-09-01",    "note": "Open Meta postings (is_open eq true) whose title names superintelligence or MSL, or whose description contains the words superintelligence and labs. All 30 read on 2026-09-22 are Meta postings whose description names Meta Superintelligence Labs. Every one carries the seniority label Not Applicable, so seniority says nothing here, and all were posted in September 2026, so the index shows the openings of the moment rather than a history. Counts of postings carry no minimum."  }}

{  "note": "Who is counted. A person counts when one job entry is at Meta and current (experience.company.name match current_employer and experience.is_current eq true, in one has_experience entry) and the profile's headline or current_title names the lab with one of lab_terms. The Platform matches text word by word, in any order, and ignores case, so MSL also matches msl and superintelligence also matches SuperIntelligence. Counts are profiles visible through the Metix AI Platform, never the lab's headcount; stale profiles count as current, so a count is not a guaranteed floor either.",  "current_employer": "Meta",  "current_employer_note": "match Meta on the current entry. The name also matches entries written as Facebook: a current entry named Facebook counts, and a Facebook condition returned all 790 profiles of the narrow definition, so the two names are one employer on the Platform and a Facebook condition is no separate test. company.name eq Meta returned the same 790 profiles as match Meta, and every one of the 63 profiles read in the audit has a current entry at Meta itself.",  "lab_terms": [    "superintelligence",    "MSL"  ],  "lab_fields": [    "headline",    "current_title"  ],  "lab_terms_note": "superintelligence alone (the narrow definition) gives 790 profiles. MSL, the lab's short name, adds 146 current Meta profiles that never write superintelligence; 13 of 14 read in a whole slice use it for the lab, and the rest recruit for it. FAIR, one of the lab's groups since 2025, is not a lab term: people who name only FAIR do not name the lab, and teams.json counts them as context (fair_only).",  "narrow_terms": [    "superintelligence"  ],  "history_names": [    "Meta",    "Facebook",    "Instagram",    "WhatsApp",    "Oculus"  ],  "history_names_note": "Names that count as an earlier job at Meta, matched word by word in one has_experience entry. The name Meta does not match an entry listed under Instagram; adding the other names moves fewer than 10 people.",  "cut": "2025-06-01",  "cut_note": "Meta announced Meta Superintelligence Labs at the end of June 2025. A person with a job entry at a history name that started before the cut had a Meta job before the lab. experience.start_date is a month (YYYY-MM) or a year; the Platform compares it as its first day, so a year alone reads as January. A newcomer whose Meta entry says only 2025 therefore counts as at Meta before the cut, which raises the share at Meta when the lab formed, and an earlier entry whose end date says only 2025 counts as ending before May 2025, which can move someone who was at Meta into returned.",  "stay_end": "2025-05-01",  "cohorts_note": "Four groups that do not overlap and add up to the whole. Stayed: a non-intern Meta entry that started before the cut and is current or ended in May 2025 or later, so the person was at Meta when the lab formed. Returned: a non-intern Meta entry before the cut, but none that lasted into May 2025, so they had left and came back. Former interns: the only Meta entries before the cut are internships. New to Meta: no Meta entry that started before the cut, including fewer than 10 whose current Meta entry has no start date. Returned, former interns, and new to Meta together are the outside hires, everyone who was not at Meta when the lab formed.",  "intern_rule": "An entry is an internship when experience.seniority eq Intern or experience.title matches intern. Both are tested inside the same has_experience entry as the employer and the date.",  "labeling_terms": [    "labeling",    "labelling",    "annotation",    "annotator",    "red teamer",    "red teaming"  ],  "labeling_note": "Titles and headlines that name data labeling, annotation, or red teaming. The audit found such roles among recent hires (fewer than 10 in the slice read); vendors often staff them, but the profile lists Meta.",  "freshness_note": "Profiles are a periodically refreshed snapshot. Across all Meta job entries on the Platform, 3,988 start in 2026, 1,104 of them in March and April and fewer than 10 in May or later, so start dates stop in April 2026 and the most recent months are probably undercounted. April 2026 is the last month shown, and people who joined since are missing.",  "audit": "63 profiles read in whole slices, never from the top of a search, because Search ranks by match quality. The slices covered people with no Meta-named entry before the cut, people with a Meta entry before the cut, people who write MSL but not superintelligence, and the profiles whose Meta entry has no start date (fewer than 10). 62 of 63 describe themselves as working in the lab, and the rest recruit for it. All 63 have a current Meta entry, almost all of them named Meta. All 31 read with a Meta entry before the cut had a real Meta job there, not only an internship. Among people without a Meta-named entry before the cut, an earlier Meta job turned up listed under Instagram, which is why Instagram is a history name, and fewer than 10 of them hold data labeling or red teaming roles. Separate reads of sub-team matches found fewer than 10 profiles that say they have left the lab while the Meta entry is still current, so a few members are out of date. Records read for the audit are kept private.",  "comparison": {    "id": "microsoft",    "label": "Microsoft",    "current_employer": "Microsoft",    "lab_terms": [      "superintelligence"    ],    "history_names": [      "Microsoft"    ],    "cut": "2025-11-01",    "stay_end": "2025-10-01",    "edges": [      "2012-01-01",      "2016-01-01",      "2020-01-01",      "2023-01-01",      "2025-01-01"    ],    "note": "Current Microsoft staff whose headline or current_title names superintelligence. Microsoft AI announced its superintelligence team in November 2025, so the cut for this group is 2025-11-01. Microsoft also matches Microsoft AI and Microsoft Research; subsidiaries with their own names (LinkedIn, GitHub) are not history names. A whole slice of 15 all named the Microsoft AI superintelligence team; fewer than 10 of them are recruiters or sourcers for it."  }}

{  "note": "What is counted, all within the population in queries/population.json unless a block says otherwise. Every count is sent with size 1, so it costs 1 API Credit, or nothing when it finds no one, and reads no record.",  "first_joined": {    "edges": [      "2012-01-01",      "2014-01-01",      "2016-01-01",      "2018-01-01",      "2020-01-01",      "2022-01-01",      "2023-01-01",      "2024-01-01",      "2025-01-01"    ],    "after_cut_edges": [      "2025-07-01",      "2025-08-01",      "2025-09-01",      "2025-10-01",      "2026-01-01",      "2026-04-01"    ],    "note": "A person's first Meta job started before a date exactly when they have an entry at a history name that started before it, so a count at each edge gives how many first joined before it, and the difference between two edges is a band of first-join dates. Bands run from the start of the record through the cut (the dates in edges) and on month by month and quarter by quarter to April 2026 (after_cut_edges). The last band holds April 2026 and anything later, and the fewer than 10 whose Meta entry has no start date. Adjacent bands merge wherever one would hold 1 to 9 people; the cut is never merged away. The second series counts only non-intern entries, so it gives each person's first Meta job that was not an internship; it runs to the cut, and everyone without such a job before the cut is one band."  },  "moves": {    "edges": [      "2025-01-01",      "2025-04-01",      "2025-06-01",      "2025-07-01",      "2025-08-01",      "2025-09-01",      "2025-10-01",      "2026-01-01",      "2026-04-01"    ],    "note": "For the people who stayed: when their latest current Meta entry started. A count of people with a current Meta entry that started on or after an edge gives how many started their newest current role since then, so a person with two current entries is placed by the newer one. Many people change teams inside Meta without adding an entry, so this shows who recorded a new role, a lower bound on who moved."  },  "sources": {    "ended_from": "2025-01-01",    "note": "Where the outside hires (everyone not at Meta when the lab formed) had just been: a job entry that ended in 2025 or later (experience.end_date gte ended_from), which also means it is no longer current. Internships do not count for companies (the intern rule in population.json), so a PhD student's summer at a lab is not where they came from; for academia they do count, because doctoral students' university entries often carry the Intern label. Rows do not overlap: each person counts at the first row in this order whose condition they meet, and the rest are other employers or none. ai_lab marks the five AI labs whose subtotal is published.",    "order": [      {        "id": "openai",        "label": "OpenAI",        "names": [          "OpenAI"        ],        "ai_lab": true      },      {        "id": "google-deepmind",        "label": "Google DeepMind",        "names": [          "DeepMind"        ],        "ai_lab": true      },      {        "id": "anthropic",        "label": "Anthropic",        "names": [          "Anthropic"        ],        "ai_lab": true      },      {        "id": "xai",        "label": "xAI",        "names": [          "xAI"        ],        "ai_lab": true      },      {        "id": "thinking-machines",        "label": "Thinking Machines Lab",        "names": [          "Thinking Machines"        ],        "ai_lab": true      },      {        "id": "apple",        "label": "Apple",        "names": [          "Apple"        ]      },      {        "id": "scale-ai",        "label": "Scale AI",        "names": [          "Scale AI"        ]      },      {        "id": "google",        "label": "Google, other than Google DeepMind",        "names": [          "Google"        ]      },      {        "id": "microsoft",        "label": "Microsoft",        "names": [          "Microsoft"        ]      },      {        "id": "amazon",        "label": "Amazon",        "names": [          "Amazon",          "AWS"        ]      },      {        "id": "nvidia",        "label": "NVIDIA",        "names": [          "NVIDIA"        ]      },      {        "id": "academia",        "label": "A university or other educational institution",        "company_type": "Educational"      }    ],    "context": [      {        "id": "google",        "label": "Google, including Google DeepMind",        "names": [          "Google",          "DeepMind"        ]      },      {        "id": "openai",        "label": "OpenAI",        "names": [          "OpenAI"        ]      },      {        "id": "anthropic",        "label": "Anthropic",        "names": [          "Anthropic"        ]      },      {        "id": "apple",        "label": "Apple",        "names": [          "Apple"        ]      },      {        "id": "microsoft",        "label": "Microsoft",        "names": [          "Microsoft"        ]      },      {        "id": "amazon",        "label": "Amazon",        "names": [          "Amazon",          "AWS"        ]      }    ],    "context_note": "For the whole group: anyone with a past (not current) non-intern job at the employer, at any time. These rows overlap and have no total. Names match word by word, so Google also matches Google DeepMind and Amazon matches Amazon Web Services (AWS). company.type Educational is the employer type the Platform records for universities and schools."  },  "roles": {    "functions": [      "Research",      "Engineering and Technical",      "Product"    ],    "senior": [      "Senior"    ],    "manager_up": [      "Manager",      "Head",      "Director",      "Vice President",      "President/Vice President",      "C-Level",      "Partner",      "Founder",      "Owner"    ],    "note": "current_function and current_seniority describe the current job and come from the Platform's fixed sets; either can be missing. Senior covers senior, staff, and principal individual contributors (the audit saw Staff, Senior Staff, and Principal titles labelled Senior); manager_up is Manager and every level above it; the rest of those with a stated level are Specialist or Intern. A doctorate is an education entry with education.degree eq Doctorate, finished or in progress.",    "baseline_note": "The baseline is Meta's other research staff: a current Meta entry, current_function eq Research, and no lab term in the headline or current title. It includes people who name FAIR only."  },  "teams": [    {      "id": "fair",      "label": "FAIR",      "terms": [        "FAIR"      ]    },    {      "id": "tbd-lab",      "label": "TBD Lab",      "terms": [        "TBD"      ]    },    {      "id": "infra",      "label": "MSL Infra and other infrastructure",      "terms": [        "infra",        "infrastructure"      ]    },    {      "id": "par",      "label": "Products and Applied Research",      "terms": [        "PAR",        "product applied research"      ]    }  ],  "teams_note": "Sub-team names in the headline or current title of people in the population, matched word by word; a person can name more than one. Audited: FAIR, 10 profiles naming it read in whole slices, nearly all naming it as the current team. TBD, all 13 matches read: 10 are in TBD Lab or report to it, and the rest name TBD while recruiting for it, while saying they are not in it, or while saying they have left the lab. Infra, the first 15 of 49 matches for infra or infrastructure (not a whole slice): all 15 work on infrastructure inside the lab, 11 of them naming MSL Infra or Infra at MSL. Products and Applied Research: the words applied research mostly match the job title Applied Research Scientist (most of those read) or an old team name, so the row needs PAR or all of product, applied, and research. fair_only counts current Meta profiles that name FAIR and no lab term; it was not audited outside the population.",  "places": {    "states": [      "California",      "New York",      "Washington"    ],    "bay_area": [      "Menlo Park",      "San Francisco",      "Sunnyvale",      "Palo Alto",      "Mountain View",      "Redwood City",      "Burlingame",      "Fremont",      "San Jose",      "Santa Clara",      "San Mateo",      "Foster City",      "Oakland",      "Berkeley",      "Cupertino",      "Los Altos",      "Belmont",      "San Carlos",      "Millbrae",      "San Bruno",      "Newark",      "Milpitas",      "Los Gatos",      "Campbell",      "Saratoga",      "Daly City",      "South San Francisco",      "Emeryville",      "Alameda",      "Hayward",      "Dublin",      "Pleasanton",      "Walnut Creek",      "Atherton",      "Portola Valley",      "Woodside",      "Half Moon Bay"    ],    "countries": [      "United Kingdom"    ],    "note": "The profile's own location, where people list themselves: location.country and location.state, and location.city for the Bay Area, whose cities are listed one by one and compared exactly."  },  "postings": {    "company": "Meta",    "title_terms": [      "superintelligence",      "MSL"    ],    "description_terms": [      "superintelligence labs"    ],    "research_title": "research",    "cities": [      "Menlo Park",      "San Francisco"    ],    "states": [      "New York"    ],    "experience_max_months": 36,    "posted_from": "2026-09-01",    "note": "Open Meta postings (is_open eq true) whose title names superintelligence or MSL, or whose description contains the words superintelligence and labs. All 30 read on 2026-09-22 are Meta postings whose description names Meta Superintelligence Labs. Every one carries the seniority label Not Applicable, so seniority says nothing here, and all were posted in September 2026, so the index shows the openings of the moment rather than a history. Counts of postings carry no minimum."  }}