Before asking any predictive question, it's useful to see the raw shape of the funnel. This analysis is a descriptive snapshot of how many applicants made it to each stage, both overall and broken down by which track they selected at Stage 1. Conversion rates here also serve as baselines for the rest of Part B (e.g., when we ask later 'do returning applicants have higher ranking rates than first-timers?', the all-applicant rate in this analysis is the comparison).
Track selections at Stage 1 are multi-select, so per-track funnels overlap — a person who selected Empirical AND Theory appears in both.
MATS (Machine Alignment, Transparency & Security) is an AI safety research fellowship that places ~120 fellows with ~100 mentors per cohort. Cohort 10.0 ran in summer 2026 and was the first cohort with a centralized application review instead of decentralized stream-specific review. This analysis is part of a broader effort to evaluate the 10.0 process and inform the design of 11.0 (autumn 2026).
The 10.0 pipeline in brief. ~2,200 people applied. Each applicant went through three stages:
For the empirical track, the composite formula is 0.50·RS + 0.35·TE + 0.15·SS, where TE = 0.50·MLE + 0.30·SWE + 0.20·Math. A "relevance multiplier" (Direct=1.0 / Adjacent=0.85 / Distant=0.60) is applied to Research Skills based on how the applicant's experience matches the streams they applied to.
Outcome definitions used throughout these analyses:
is_ranked (primary outcome) — applicant was ranked by ≥1 stream. This is the cleanest signal of "the selection process picked this person." Not the same as "received an offer" — offer count is bounded by cohort size (~120), but rank count reflects quality independently of capacity.is_invited_to_worktest (secondary outcome) — applicant was engaged by ≥1 stream in any way: invited to a work test, invited to an interview, ranked, or sent the Megastream takehome. Strict superset of is_ranked. One level above is_ranked in the funnel.passed_mentors_bar — applicant was offered or waitlisted. In 10.0, this equals is_ranked exactly (every ranked person got either an offer or a waitlist slot).Out of 2,203 canonical applications, 1,991 passed Stage 1 (90.4%), 804 reached Stage 3 (36.5%), 189 were ranked by ≥1 stream (8.6%), and 126 received offers (5.7%).
The two biggest filtering stages are Stage 2 → Stage 3 (composite-based, drops 1,187 applicants — ~60% of those who passed Stage 1) and Stage 3 → Ranked (stream-side review, drops 615 applicants — ~76% of Stage-3 entrants).
| Stage | n | % of applied | % of previous stage |
|---|---|---|---|
| Applied | 2,203 | 100.0% | 100.0% |
| Passed Stage 1 | 1,991 | 90.4% | 90.4% |
| Reached Stage 3 | 804 | 36.5% | 40.4% |
| Engaged by ≥1 stream | 519 | 23.6% | 64.6% |
| Ranked by ≥1 stream | 189 | 8.6% | 36.4% |
| Offered | 126 | 5.7% | 66.7% |
| Waitlisted | 63 | 2.9% | 50.0% |
| Track | Applied | Passed S1 | Reached S3 | Ranked | Offered |
|---|---|---|---|---|---|
| Empirical | 1,683 | 1,592 | 638 | 147 | 107 |
| Policy & Strategy | 445 | 402 | 201 | 30 | 17 |
| Technical Governance | 373 | 341 | 157 | 25 | 20 |
| Theory | 687 | 600 | 229 | 75 | 36 |
| Compute Infrastructure | 277 | 214 | 69 | 14 | 6 |
Sample. Canonical 10.0 sample (deduped, n=2,203). Per-track masks based on [stage-1-track] Selected tracks.
Outcome variable(s). Stages derived from Furthest stage reached: Stage 1 / Stage 2 / Stage 3 / Offered / Waitlisted. 'Engaged by ≥1 stream' = is_invited_to_worktest (includes Megastream takehome path + ranking).
Predictor fields. N/A — descriptive.
Filters applied. Canonical dedup applied (7 person_ids had 2 rows in apps_10; kept the row with furthest stage).
Missing-data handling. No imputation needed; Furthest stage reached is non-null for all rows.
Key assumptions / caveats.