Built on Metix AI's global talent database of 860M+ profiles, a full-population profile of 13 frontier AI labs across the US, UK and France: who feeds and loses talent to whom, each lab's staff composition and retention, a named talent-flow network, and an org reconstruction of the engineer and researcher tiers.
The figures below follow Metix AI database methodology (data as of roughly the first half of 2026); the population = talent currently employed at the 13 frontier AI labs and based in the US, UK or France.
Meta (AI research track) at 4,092 and Google DeepMind at 2,837 together account for 58% of the visible 11,914-person technical pool. OpenAI (2,628) and Anthropic (1,251) follow. Profile coverage differs: DeepMind is counted as a standalone entity, so researchers sitting under the Google parent are undercounted; SSI's team is tiny and secretive.
On SignalFire's methodology, Anthropic leads on two-year retention at 80%, with the OpenAI→Anthropic net flow running roughly 8:1; of xAI's 12 co-founders, all 11 besides Musk have departed (including 4 co-founders who left in quick succession in early 2026). Tenure data confirms it: median tenure for current technical talent is just 8 months at xAI and 9 months at Anthropic, both in a phase of rapid expansion and turnover.
OpenAI and Anthropic make heavy use of the flat "Member of Technical Staff" title (the visible research-track share is in single digits), while Meta's research track and DeepMind explicitly label Research Scientists (73.3% / 50.2%). The implication: evaluating OpenAI/Anthropic talent has to come back to the body of work, not the title.
Meta MSL's high-priced 2025 hiring cohort has a median tenure of 23 months, with 40.0% past the 36-month mark; layer on mid-level mobility at xAI and OpenAI's expiring retention grants, anda 1703-person "high-mobility" list makes the second half of 2026 the high-water mark for mobility in this talent market.
Every event below has been verified item by item against public sources (source list in the research backup); we keep only the facts that bear on talent-flow judgments. All dollar figures are as reported; most have not been confirmed by the companies.
In 2025-06 Altman publicly accused Meta of offering "$100M signing bonuses"; within two weeks 8 people jumped from OpenAI to Meta (including the Zurich trio); Meta hired foundation-model lead Ruoming Pang from Apple (a reported $200M+ multi-year package); of the 44-person MSL roster, roughly 75% hold a PhD. Meta officially denied only the "single signing-bonus" wording.
OpenAI Chief Research Officer Mark Chen's internal note — "someone has broken into our home and stolen something" — was followed by recalibrating comp; as reported in 2025-08, the company handed roughly 1,000 research/engineering staff a retention bonus of about $1.5M each, vesting over two years. Failed acquisition turning into hiring campaigns became Meta's standard play (SSI and Thinking Machines were both probed and then hired; the Tulloch case was reported at up to $1.5B/6 years, a figure Meta denied).
On SignalFire's methodology: the OpenAI→Anthropic net flow runs about 8:1 and DeepMind→Anthropic about 11:1; two-year retention is Anthropic 80% > DeepMind 78% > OpenAI 67% > Meta 64%. In 2026-05 Karpathy joined Anthropic's pretraining team (company-confirmed). On the xAI side: of the 12 co-founders, all 11 besides Musk have departed (as of 2026-03), including 4 co-founders who left in quick succession in early 2026.
In 2026-02 OpenAI hired back two researchers from Meta within a single week (Ruoming Pang and Pengchuan Zhang); the talent war widened from researchers to commercialization executives (Salesforce/Snowflake/Datadog leaders, Palantir FDEs) and quant talent (a named Jane Street case moving to Anthropic); and Chinese tech giants' reverse hiring was confirmed (Yonghui Wu joining ByteDance Seed, Shunyu Yao becoming Tencent's chief AI scientist).
Population = 11,914 technical staff (Research Scientists / Research Engineers / MTS / Engineering / Safety & Alignment) across the US, UK and France.
Read: Meta (AI research track) and Google DeepMind together account for 58% of the visible technical pool. Note that profile coverage differs across labs: DeepMind is counted as a standalone entity, so researchers sitting under the Google parent are undercounted; SSI's team is tiny and secretive (publicly reported at about 20 people), so visible profiles are naturally sparse.
Read: research density is bimodal. Meta's research track (73.3%), DeepMind (50.2%), Mistral, Kyutai and H explicitly label the research track and clear the 50% mark; OpenAI (9.6%), Anthropic (6.4%) and xAI use the flat MTS title, so their research share is understated.
Read: OpenAI and Anthropic have the thickest MTS column (a flat title that doesn't split research from engineering), while Meta's research track and DeepMind have the thickest Research Scientist column; Safety & Alignment is Anthropic's relative signature column. For buyers: if you want "clearly designated Research Scientists," hire from Meta/DeepMind; if you want "full-stack MTS," hire from OpenAI/Anthropic.
Read: three main intake pipes stand out. ① Internal transfers within the big techs are the thickest pipe (Google→DeepMind is the largest, since it's the same system to begin with); ② direct hiring from academia/research (especially Meta's research track and DeepMind) is the primary entry point for research talent, confirming that top PhDs are spoken for before they graduate; ③ the large "startups/other" volume reflects the high-frequency flow among frontier labs and the broader AI startup ecosystem. Quant funds (Jane Street/Citadel alumni) have emerged as a new pipe, with named cases already appearing at Anthropic/OpenAI.
Read: 4763 of the current technical staff joined in 2025, 5.3x the 2023 trough (895), and the explosive hiring of 2024-2025 is the temporal shape of this talent war. Mind survivorship: earlier cohorts are already diluted by attrition, so the curve understates historical hiring and gets closer to true intensity the more recent the year.
Read: the upper-right "veterans zone" = Meta's research track (median tenure 23 months, 40.0% past 36 months, the sediment of FAIR old-timers); the lower-left "new recruits zone" = xAI (8 months), Anthropic (9 months) and Mistral (8 months), all expanding fast, with onboarding-honeymoon staff relatively stable before entering their first round of mobility at 12-24 months. OpenAI (13 months) and DeepMind (15 months) sit in the middle.
Net flow since 2024 (sources include the four dissolution/diffusion origins Character.AI/Inflection/Stability/Adept). Destination structure and ratios are the more robust read.
Read: the talent-flow network is this report's most directly actionable asset for VCs and recruiters. The off-diagonal cells are the proven flow channels (lowest psychological resistance for candidates): the more people who leave one lab and land at another, the more "open" that hiring path is. The diffusion paths of the three teams dissolved by big-tech acqui-hires — Character.AI, Inflection and Adept — are especially worth watching.
Read: the two methodologies corroborate each other directionally. Metix AI visible retention (current / current + visible leavers) runs high in absolute terms because of the sample cap, but the ranking across labs matches SignalFire's 2-year cohort methodology (Anthropic 80% > DeepMind 78% > OpenAI 67% > Meta 64%): Anthropic holds onto people, Meta doesn't.
Read: inflow and outflow are both samples, so net values are directional. Labs in expansion (Anthropic/OpenAI) show inflow well above visible outflow; against the backdrop of a co-founder exodus, xAI's outflow direction is clear. True outflow exceeds the conservative count, but the ranking is trustworthy.
Read (VC view): the sources with the highest founding rate are the first concentration of spinout deal flow. Leavers from dissolved teams like Character.AI/Inflection/Adept found companies and join new labs at notably higher rates; DeepMind/OpenAI leavers flow more to other frontier labs and internet big techs. "Founded a company" is identified by current titles containing founder/stealth, a lower bound, so the true number of founders is higher.
Paid org charts like The Information's only cover the executive layer. This section uses full-population profiles to push the reconstruction down to the team-lead tier and IC depth. Levels are classified from public job titles — not the official org structure, just an overview of team-tier structure; OpenAI/Anthropic's many MTS titles carry no level information. Names are masked by default in the public version.
Below the dual research leadership (CRO Mark Chen + Chief Scientist Jakub Pachocki), the public org chart stops at the VP layer. After the 2026 departures of Tworek/Weil/Peebles and others, the mid-level is the key to understanding OpenAI.
Doubled in a year to about 5,200 people (publicly reported). Pretraining-team head Nick Joseph; in 2026-05 Karpathy joined that team (public source).
Dual hubs in London + the Bay Area (CTO Kavukcuoglu relocated to Mountain View in 2025, doubling as Google's chief AI architect). Team-lead attrition is its main risk (Microsoft AI hired 20+).
Three groups of representative profiles, selected from the list by level, direction and strength of track record. Profile facts come from the Metix AI database; those marked "publicly verified" have had their current role confirmed against 2025-2026 public sources (frontier-lab profiles update with a lag, so public sources take priority). All profiles in this section come from public professional records. Group A is industry public figures, Group B is senior technical backbone, Group C is crossover and high-potential profiles. Names are masked by default in the public version.
Frontier-lab compensation has split into two markets: the "level market" you can look up on levels.fyi, and the "list market" that bypasses the leveling system ($10M to $1.5B, as reported). Figures are the median of self-reported samples retrieved in 2026-06, not official company numbers.
| Lab | Regular-level median TC | Senior reference | Equity mechanism | Notes |
|---|---|---|---|---|
| OpenAI | L5 $819K / L6 $1.23M | MTS sample: $300K base + ~$500K/yr PPU | PPU profit-participation units, 4-year linear vesting, liquidity via tender | As reported in 2025-08: ~1,000 people × $1.5M retention grant (2-year vest) |
| Anthropic | SWE median $665K | Lead median $785K | Standard private-company equity + tender | Median cash below OpenAI, yet first on retention (SignalFire) |
| Google DeepMind | L6 RS $750K-1M | L7 $950K-1.4M | GSU public stock, best liquidity | RS equity 5-15% higher than SWE at the same level |
| Meta (MSL) | E7 median $1.30M | List market $10M-100M+/yr | RSU + list-based special package | Ruoming Pang $200M+ and Tulloch up to $1.5B/6 years are both as reported |
| xAI | SWE $205-640K | Small sample | Private-company options (swapped after the SpaceX acquisition) | Folded into the SpaceX system from 2026-02 |
| Mistral (Paris) | Paris median €89.5K | Researchers $490-950K (estimated) | BSPCE options | European engineering roles trail the US by an order of magnitude; research roles carry a 3-5x premium |
The split between the research and engineering tracks has hardened: at DeepMind, same-level research roles carry 5-15% more equity; at Mistral, research total comp is roughly 3-5x engineering; and Meta's list-based packages occur only at the researcher / research-leadership level. Quant funds (Jane Street/Citadel alumni) and the labs bid each other up, and Anthropic/OpenAI proactively host mixers to hire entry-level quants, with named cases already appearing (two Jane Street people moved to Anthropic in 2025).
① The level market can be negotiated against the table; the list market can only be competed for with mission, equity upside and compute freedom; ② the liquidity gap between OpenAI's PPU and DeepMind's GSU is a practical lever in recruiting pitches (PPU depends on the company organizing a tender, GSU can be sold anytime); ③ Europe (Mistral/Kyutai/H) is a compensation depression at equivalent talent density, well suited for budget-constrained buyers building an R&D site.
Turn the map into action: VCs watch spinout signals, recruiters watch windows and channels, HR watches defense.
① The founding-rate ranking (Section 4.4) points to the high-frequency sources of spinouts; ② several of xAI's co-founders departed in early 2026 with destinations undisclosed — a team-forming signal worth watching; ③ LeCun (AMI Labs $1.03B seed) and Tworek (Core Automation) confirm the "roughly 6 months from executive departure to a closed round" cadence; ④ tenure structure (3.7) can be used to anticipate: if Meta MSL's high-priced 2025 cohort loosens in H2 2026, a new wave of team-forming windows opens with it.
① The mutual-flow matrix (4.1) shows which flow channels have been historically proven; ② tenure windows layered with organizational reshuffles help locate the more mobile groups; ③ the MTS title carries no level, so capability assessment has to come back to public work (model-contribution rosters/papers/systems); ④ the technical pool has a 36.3% PhD rate, and alumni chains + public work are the highest-hit outreach surface.
① Locate yourself against the retention benchmarks in 4.2; ② the industry's established retention tools — equity refreshes, retention grants and mission narrative — with OpenAI's case giving a price reference ($1.5M × 1,000 people); ③ watch the group with "36+ months of tenure and slowing promotion" (the mirror image of the 3.7 mobility window); ④ non-competes and garden leave are largely ineffective in the US market, so you keep people by giving them direction, not by locking them in.
The retrieval, profiling and flow analysis in this report were all done by Metix AI. We can generate a custom map for any company on the same methodology: full long-list export, org reconstruction, talent-flow monitoring, email unlock and multi-channel outreach — billed on a "pay only for qualified interviews" basis. No interview, no charge.
860M+ global talent profiles11,914-person lab technical-pool long listQuarterly monitoring of the talent-flow networkPay only for qualified interviewsOpenAI, Anthropic, Google DeepMind, Meta (AI research track), xAI, SSI, Thinking Machines, Mistral AI, Reflection AI, World Labs, Physical Intelligence, H Company, Kyutai. Geographic scope = profiles based in the US/UK/France. Google DeepMind is counted as a standalone entity, so a small number of people holding a DeepMind title under the Google parent are undercounted.
Meta is enormous and MSL is not a standalone entity, so this report's "Meta (AI research track)" = people currently at Meta whose title/headline is identifiable as AI research/engineering related (including profiles explicitly tagged Superintelligence Labs/FAIR/GenAI). It is an identifiable subset rather than all of Meta AI, so the absolute numbers run conservative.
Technical talent pool = Research Scientists + Research Engineers + MTS (unspecified) + Engineering + Safety & Alignment. OpenAI/Anthropic/SSI make heavy use of the Member of Technical Staff title without splitting research from engineering, broken out separately as MTS, so the research-track share is a lower-bound methodology. Solutions/product/GTM are not counted in the technical pool.
The talent database is as of roughly the first half of 2026; frontier-lab profiles update with a lag, so the key profiles have been re-checked one by one against public information, and the latest 2025-2026 role changes are annotated according to public sources.
| Lab | current-employee profiles | Technical pool | Research-track share | PhD rate |
|---|---|---|---|---|
| Meta (AI research track) | 4,228 | 4,092 | 73.3% | 59.8% |
| Google DeepMind | 4,123 | 2,837 | 50.2% | 36.9% |
| OpenAI | 5,495 | 2,628 | 9.6% | 16.0% |
| Anthropic | 2,740 | 1,251 | 6.4% | 16.0% |
| xAI | 1,717 | 710 | 5.1% | 16.1% |
| Mistral AI | 528 | 263 | 51.7% | 17.5% |
| Thinking Machines | 88 | 64 | 1.6% | 43.8% |
| Reflection AI | 41 | 21 | 0.0% | 42.9% |
| H Company | 32 | 19 | 63.2% | 10.5% |
| World Labs | 24 | 12 | 8.3% | 33.3% |
| Physical Intelligence | 25 | 9 | 11.1% | 33.3% |
| Kyutai | 13 | 8 | 75.0% | 37.5% |
| SSI | 3 | 0 | 0% | 0% |
① Data recency: the talent database is a static snapshot (roughly the first half of 2026); frontier labs see extremely rapid staff turnover and a pronounced profile-update lag (high-frequency job-hoppers often don't update their profiles for months), so all figures are directional rather than real-time; the key profiles have been re-checked and annotated one by one against public sources.
② Uneven coverage: this report aggregates public professional records, so coverage is limited for secretive teams like SSI, for DeepMind (counted as a standalone entity) and for Meta (taken as the identifiable AI-research-track subset). Comparisons across labs lean on shares and structure, with absolute numbers secondary.
③ Function and level inference: based on title/headline keywords; flat titles like MTS make both the research-track share and the team-lead-tier headcount lower bounds.
④ Outflow and retention: leaver retrieval has a sample cap, so outflow runs conservative; this report's "visible retention rate" differs from SignalFire's 2-year cohort methodology, so read them side by side rather than as substitutes for each other.
⑤ Dollar-figure methodology: compensation packages are all as reported by the media and mostly unconfirmed by the companies; please keep the "as reported" qualifier when citing them.