MATS sent a Q3 2025 alumni survey with 123 respondents spanning cohorts 1.0–8.1 (heaviest representation in 6.0/7.0/8.0). The survey captures outcomes we can't see elsewhere: where alumni are working now, what mechanism they attribute MATS's impact to, whether they received post-MATS funding, and self-reported publication output.
This is the most downstream outcome data we have, and it comes with major caveats — it's an opt-in survey heavily skewed toward engaged/successful alumni, and self-attribution of MATS's causal contribution should be read as the alum's subjective view, not a controlled estimate.
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).| Latest cohort | n | P(any pub) | P(top-tier conf) | P(funding) | P(AI safety org) | P(frontier lab) |
|---|---|---|---|---|---|---|
| 4.0 | 10 | 70% | 30% | 90% | 90% | 80% |
| 5.0 | 6 | 100% | 67% | 83% | 83% | 50% |
| 5.1 | 4 | 100% | 75% | 100% | 100% | 100% |
| 6.0 | 11 | 64% | 36% | 45% | 100% | 91% |
| 6.1 | 12 | 83% | 50% | 83% | 100% | 67% |
| 7.0 | 10 | 70% | 30% | 30% | 100% | 80% |
| 7.1 | 20 | 75% | 30% | 30% | 90% | 70% |
| 8.0 | 15 | 40% | 13% | 20% | 100% | 87% |
| 8.1 | 18 | 33% | 22% | 22% | 94% | 100% |
| Mechanism | # respondents |
|---|---|
| Increased research/technical skill | 32 |
| Strengthened Resume | 30 |
| Professional connections from MATS | 24 |
| Reference from mentor | 21 |
| 1-1 support from MATS team | 13 |
| Other | 6 |
Sample. Alumni survey respondents with apps-data-cohort latest attribution. n=106.
Outcome variable(s). Self-reported binaries: any post-MATS publication, top-tier conference paper, received post-MATS funding, current employer type (AI safety, frontier, government, academia).
Predictor fields. N/A — descriptive cross-cohort summary.
Filters applied. Latest-cohort attribution restricted to cohorts with apps data (4.0+).
Missing-data handling. NaN treated as negative (e.g., NaN funding = no funding reported).
Key assumptions / caveats.