Partner ranking · 17 group partners · 81% attributed · age-controlled alpha vs YC base
Who actually picks well — partner alpha ranking
YC group partners benchmarked against the YC-wide base rate in the same age cohort. Naive 'sum raised' ranks the partner who happened to catch a unicorn. Alpha-controlled ranks the partner whose companies outperform peers of the same vintage. The two rankings diverge sharply: Gustaf Alstromer leads by Σ raised (Legora $860M), but underperforms on alpha. Tom Blomfield and Jared Friedman lead by alpha across all metrics.
Jared
top by alpha (composite)
11.3%
real dead rate vs YC label 3.4%
72%
still alive (verified)
81%
partner-attribution coverage
Caveat
Sample warning: partners with <40 mature-age companies (Harshita, Ankit, Andrew, Jon, Vivian) get no composite score — their portfolios are too young for statistically meaningful ranking. Real-status uses HTTP health check on 2,745 sites + age-aware classifier that distinguishes 'not deployed yet' (recent batches on registrar parking) from real abandonment. YC's own 'Inactive' label captures only 3.4%; our real_dead rate is 11.3%.
Three independent ranking methods — top picks stable across all
Per-batch (primary): companies within a batch share age + market conditions. Age-bucket: same but bucketed by years-since-batch. Rank-score: ordinal rank within each cohort, robust to outliers. Consistency: % of batches where partner beat YC on $5M conversion.
Why three methods
User asked: 'is your age control fair?'. Honest answer: no single methodology is perfect, so we compute three. (1) Per-batch alpha: within each YC batch, compare partner's rate to YC-wide rate IN THAT BATCH. Companies in the same batch share age + market. This is the cleanest. (2) Age-bucket alpha: original method, weighted by partner's age distribution. Has bucket-mix bias. (3) Rank-aggregation: within each age cohort, RANK partners; mean rank across cohorts. Robust to outliers. The top 6 — Friedman, Blomfield, Diana Hu, Dessaigne, Bosmeny, Epstein — appear consistently across all three. That's the robust finding.
| # | Partner | N | Σ raised | Per-batch | Age-bkt | Rank | Cons |
|---|---|---|---|---|---|---|---|
| 1 | Jared Friedman | 233 | $998M | 0.404 | 0.373 | 0.651 | 67% |
| 2 | Tom Blomfield | 207 | $728M | 0.404 | 0.351 | 0.599 | 40% |
| 3 | Diana Hu | 182 | $765M | 0.386 | 0.315 | 0.456 | 50% |
| 4 | Nicolas Dessaigne | 220 | $673M | 0.318 | 0.240 | 0.735 | 60% |
| 5 | Tyler Bosmeny | 74 | $117M | 0.253 | 0.242 | 0.440 | 40% |
| 6 | Aaron Epstein | 186 | $791M | 0.242 | 0.279 | 0.607 | 56% |
| 7 | Gustaf Alstromer | 213 | $1.74B | 0.120 | 0.078 | 0.188 | 44% |
| 8 | David Lieb | 118 | $125M | 0.060 | -0.008 | 0.562 | 29% |
| 9 | Harj Taggar | 170 | $420M | -0.007 | 0.155 | 0.245 | 20% |
| 10 | Pete Koomen | 108 | $231M | -0.040 | 0.055 | 0.443 | 17% |
| 11 | Garry Tan | 121 | $213M | -0.048 | 0.092 | 0.467 | 38% |
| 12 | Jon Xu | 51 | $35M | -0.093 | -0.129 | 0.584 | 33% |
| 13 | Andrew Miklas | 55 | $142M | -0.121 | -0.137 | 0.321 | 25% |
| 14 | Brad Flora | 223 | $507M | -0.135 | -0.146 | 0.274 | 38% |
| 15 | Ankit Gupta | 42 | $36M | — | — | 1.000 | 50% |
| 16 | Harshita Arora | 11 | $32M | — | — | — | 100% |
| 17 | Vivian Midha Shen | 1 | $0 | — | — | — | — |
Round progression — series-level funnel
Alternative to $-thresholds — using last_round_series stage data. Age-bucket controlled. Positive α = above YC base.
| Partner | %Ser A+ | α Ser A | %Ser B+ | α Ser B | %Acq | α Acq |
|---|---|---|---|---|---|---|
| Jared Friedman | 4.7% | -0.20% | 0.86% | -0.22% | 1.72% | -0.15% |
| Tom Blomfield | 6.8% | +2.51% | 1.45% | +0.58% | 0.97% | -0.54% |
| Diana Hu | 7.1% | +2.58% | 2.2% | +1.20% | 2.2% | +0.53% |
| Nicolas Dessaigne | 10% | +5.54% | 0.45% | -0.56% | 0.45% | -1.23% |
| Tyler Bosmeny | 0% | -0.86% | 0% | -0.09% | 0% | -0.12% |
| Aaron Epstein | 5.4% | +1.17% | 1.61% | +0.62% | 0.54% | -1.10% |
| Gustaf Alstromer | 6.6% | +1.16% | 1.88% | +0.61% | 1.41% | -0.75% |
| David Lieb | 1.7% | -0.07% | 0.85% | +0.57% | 0.85% | +0.47% |
| Harj Taggar | 4.7% | -0.55% | 1.76% | +0.65% | 3.53% | +1.56% |
| Pete Koomen | 3.7% | +0.90% | 0.93% | +0.47% | 0.93% | +0.13% |
| Garry Tan | 3.3% | -0.20% | 0% | -0.60% | 0% | -1.06% |
| Jon Xu | 0% | -0.49% | 0% | +0.00% | 0% | +0.00% |
| Andrew Miklas | 1.9% | +1.36% | 0% | +0.00% | 0% | +0.00% |
| Brad Flora | 7.6% | +2.24% | 1.35% | +0.00% | 1.35% | -0.89% |
| Ankit Gupta | 0% | -0.37% | 0% | +0.00% | 0% | +0.00% |
| Harshita Arora | 0% | -0.25% | 0% | +0.00% | 0% | +0.00% |
| Vivian Midha Shen | — | — | — | — | — | — |
Detailed alpha by $ thresholds
Per-batch α by individual metric + age-bucket survival data.
Green = above YC base (good). Yellow = at base. Red = below base.
| Partner | Alive | Dead | α5M | α15M | α50M | αDeath | αExit |
|---|---|---|---|---|---|---|---|
| Jared Friedman | 162 | 18 | +4.36% | +0.70% | +0.49% | +3.66% | -0.21% |
| Tom Blomfield | 155 | 12 | +3.02% | +1.48% | +0.80% | +4.09% | -1.00% |
| Diana Hu | 123 | 20 | +1.45% | +2.58% | +2.07% | +0.54% | +0.57% |
| Nicolas Dessaigne | 163 | 21 | +7.71% | +3.75% | -0.69% | +1.85% | -1.23% |
| Tyler Bosmeny | 52 | 0 | +4.04% | -0.22% | -0.09% | +2.25% | +0.66% |
| Aaron Epstein | 121 | 16 | +2.83% | +1.73% | +0.35% | +1.70% | +1.46% |
| Gustaf Alstromer | 147 | 31 | -0.43% | +2.28% | +1.41% | -1.75% | -1.02% |
| David Lieb | 75 | 6 | -0.85% | -0.52% | +0.47% | +0.82% | -0.67% |
| Harj Taggar | 113 | 20 | -1.34% | -0.47% | +0.42% | -0.19% | +5.82% |
| Pete Koomen | 62 | 7 | +0.63% | +1.46% | -0.54% | +0.25% | -1.05% |
| Garry Tan | 94 | 8 | +1.00% | +0.36% | -1.04% | +1.56% | -0.81% |
| Jon Xu | 26 | 1 | -0.85% | -1.28% | +0.00% | -1.48% | -0.34% |
| Andrew Miklas | 22 | 0 | -2.74% | +0.56% | -0.13% | +0.61% | -0.32% |
| Brad Flora | 165 | 27 | -0.96% | -0.21% | -0.92% | +1.13% | -1.47% |
| Ankit Gupta | 16 | 1 | +3.37% | -1.25% | +0.00% | +0.53% | -0.20% |
| Harshita Arora | 4 | 1 | +6.98% | +7.84% | +0.00% | -9.50% | +0.00% |
| Vivian Midha Shen | 0 | 0 | — | — | — | — | — |
YC base rates by age cohort
Used as the benchmark for alpha calculation. Each metric is partner's rate minus expected rate at their cohort ages.
| Age bucket | N | ≥$5M | ≥$15M | ≥$50M | Death | Exit |
|---|---|---|---|---|---|---|
| <0.5y | 401 | 2.7% | 1.2% | 0.00% | 0.2% | 0.00% |
| 0.5-1y | 315 | 8.6% | 1.3% | 0.32% | 1.3% | 0.63% |
| 1-1.5y | 312 | 4.8% | 1.6% | 0.00% | 1.0% | 0.96% |
| 1.5-2y | 342 | 1.5% | 1.2% | 0.29% | 10.5% | 2.05% |
| 2-3y | 469 | 14.5% | 8.5% | 2.13% | 16.6% | 4.90% |
| 3y+ | 906 | 14.2% | 7.6% | 2.43% | 20.6% | 8.39% |
Workload heatmap — companies per partner per batch
Darker = more companies that batch. Empty = inactive that batch.
| Partner | W '22 | S '22 | W '23 | S '23 | W '24 | S '24 | F '24 | W '25 | Sp '25 | S '25 | F '25 | W '26 | Sp '26 | S '26 | F '26 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Jared Friedman | 16 | 20 | 37 | 23 | 28 | 20 | 29 | 27 | 31 | 2 | |||||
| Tom Blomfield | 25 | 5 | 15 | 19 | 27 | 24 | 23 | 19 | 24 | 26 | |||||
| Diana Hu | 20 | 4 | 22 | 25 | 17 | 23 | 16 | 20 | 8 | 25 | 1 | 1 | |||
| Nicolas Dessaigne | 17 | 29 | 23 | 14 | 21 | 22 | 17 | 26 | 23 | 26 | 2 | ||||
| Tyler Bosmeny | 3 | 7 | 5 | 27 | 30 | 2 | |||||||||
| Aaron Epstein | 27 | 13 | 29 | 13 | 18 | 23 | 12 | 23 | 2 | 26 | |||||
| Gustaf Alstromer | 39 | 24 | 29 | 14 | 21 | 19 | 23 | 19 | 1 | 24 | |||||
| David Lieb | 4 | 11 | 21 | 16 | 1 | 20 | 16 | 29 | |||||||
| Harj Taggar | 10 | 21 | 20 | 25 | 21 | 10 | 24 | 14 | 7 | 1 | 17 | ||||
| Pete Koomen | 2 | 1 | 6 | 19 | 2 | 14 | 20 | 1 | 20 | 23 | |||||
| Garry Tan | 2 | 1 | 25 | 14 | 17 | 10 | 7 | 5 | 1 | 23 | 2 | 13 | 1 | ||
| Jon Xu | 2 | 3 | 22 | 23 | 1 | ||||||||||
| Andrew Miklas | 1 | 5 | 10 | 10 | 29 | ||||||||||
| Brad Flora | 41 | 34 | 40 | 18 | 20 | 19 | 25 | 1 | 25 | ||||||
| Ankit Gupta | 1 | 2 | 12 | 26 | 1 | ||||||||||
| Harshita Arora | 10 | 1 | |||||||||||||
| Vivian Midha Shen | 1 |
Per-partner breakdown
Top funded portfolio companies + top RFS themes each partner is concentrated in.
Jared Friedman
Winter 2022 → Summer 2026 · 4.4y · 10 batches
233
companies
Σ raised
$998M
talent
0.373
α5M
+4.85%
αDeath
+3.59%
Top companies (by raise)
- Corgi Insurance$268M
- Arc$180M
- Unify$59M
Top RFS themes
ai_native_service_companies · 66saas_challengers · 63none · 51llm_back_office · 48ai_build_enterprise_sw · 37
Tom Blomfield
Winter 2022 → Spring 2026 · 4.4y · 10 batches
207
companies
Σ raised
$728M
talent
0.351
α5M
+3.28%
αDeath
+3.98%
Top companies (by raise)
- Bretton AI$95M
- Aspora$93M
- Alaan$56M
Top RFS themes
ai_native_service_companies · 78llm_back_office · 69saas_challengers · 44none · 39ai_build_enterprise_sw · 25
Diana Hu
Winter 2022 → Summer 2026 · 4.4y · 12 batches
182
companies
Σ raised
$765M
talent
0.315
α5M
+0.77%
αDeath
+0.52%
Top companies (by raise)
- Avoca$125M
- Reducto$108M
- Hubble Network$100M
Top RFS themes
ai_native_service_companies · 41saas_challengers · 40ai_build_enterprise_sw · 36llm_back_office · 33none · 28
Nicolas Dessaigne
Winter 2022 → Fall 2026 · 4.4y · 11 batches
220
companies
Σ raised
$673M
talent
0.240
α5M
+7.06%
αDeath
+1.72%
Top RFS themes
ai_native_service_companies · 64saas_challengers · 56ai_build_enterprise_sw · 45llm_back_office · 43none · 22
Tyler Bosmeny
Winter 2024 → Spring 2026 · 2.4y · 6 batches
74
companies
Σ raised
$117M
talent
0.242
α5M
+4.09%
αDeath
+2.31%
Top companies (by raise)
- Ndea$43M
- Moritz$9M
- Orbital Operations$9M
Top RFS themes
ai_native_service_companies · 21saas_challengers · 18none · 13llm_back_office · 12ai_build_enterprise_sw · 10
Aaron Epstein
Winter 2022 → Spring 2026 · 4.4y · 10 batches
186
companies
Σ raised
$791M
talent
0.279
α5M
+3.01%
αDeath
+1.61%
Top companies (by raise)
- Kapital Bank$371M
- Spade$55M
- Primer$50M
Top RFS themes
ai_native_service_companies · 60saas_challengers · 51none · 41llm_back_office · 34ai_build_enterprise_sw · 30
Gustaf Alstromer
Winter 2022 → Winter 2026 · 4.4y · 10 batches
213
companies
Σ raised
$1.74B
talent
0.078
α5M
+0.03%
αDeath
-1.72%
Top RFS themes
ai_native_service_companies · 68llm_back_office · 50saas_challengers · 49none · 40ai_build_enterprise_sw · 28
David Lieb
Summer 2023 → Spring 2026 · 3y · 8 batches
118
companies
Σ raised
$125M
talent
-0.008
α5M
-1.60%
αDeath
+0.73%
Top RFS themes
ai_native_service_companies · 38llm_back_office · 35ai_build_enterprise_sw · 29saas_challengers · 19none · 15
Harj Taggar
Winter 2022 → Spring 2026 · 4.4y · 11 batches
170
companies
Σ raised
$420M
talent
0.155
α5M
-0.90%
αDeath
-0.13%
Top RFS themes
ai_native_service_companies · 55saas_challengers · 46ai_build_enterprise_sw · 35none · 31llm_back_office · 27
Pete Koomen
Summer 2022 → Spring 2026 · 4y · 10 batches
108
companies
Σ raised
$231M
talent
0.055
α5M
+1.22%
αDeath
-0.02%
Top RFS themes
ai_native_service_companies · 32saas_challengers · 24ai_build_enterprise_sw · 21llm_back_office · 20none · 18
Garry Tan
Winter 2022 → Fall 2026 · 4.4y · 13 batches
121
companies
Σ raised
$213M
talent
0.092
α5M
+1.20%
αDeath
+1.99%
Top companies (by raise)
- AstroForge$55M
- Every$32M
- Fortuna Health$22M
Top RFS themes
ai_native_service_companies · 34llm_back_office · 32none · 24saas_challengers · 20ai_build_enterprise_sw · 14
Jon Xu
Summer 2024 → Summer 2026 · 2y · 5 batches
51
companies
Σ raised
$35M
talent
-0.129
α5M
-1.40%
αDeath
-1.29%
Top companies (by raise)
Top RFS themes
ai_native_service_companies · 19ai_build_enterprise_sw · 16software_for_agents · 10saas_challengers · 9llm_back_office · 8
Andrew Miklas
Summer 2023 → Spring 2026 · 3y · 5 batches
55
companies
Σ raised
$142M
talent
-0.137
α5M
-3.24%
αDeath
+0.69%
Top RFS themes
ai_native_service_companies · 25ai_build_enterprise_sw · 17saas_challengers · 14llm_back_office · 14software_for_agents · 6
Brad Flora
Winter 2022 → Winter 2026 · 4.4y · 9 batches
223
companies
Σ raised
$507M
talent
-0.146
α5M
-0.50%
αDeath
+1.08%
Top companies (by raise)
- Cinder$55M
- Reality Defender$44M
- GovDash$42M
Top RFS themes
saas_challengers · 56none · 56ai_native_service_companies · 54llm_back_office · 44ai_build_enterprise_sw · 31
Ankit Gupta
Summer 2023 → Summer 2026 · 3y · 5 batches
42
companies
Σ raised
$36M
talent
—
α5M
+3.16%
αDeath
+0.56%
Top RFS themes
ai_build_enterprise_sw · 18software_for_agents · 11llm_back_office · 7ml_robotics · 6foundation_models_biology · 6
Harshita Arora
Winter 2026 → Summer 2026 · 0.4y · 2 batches
11
companies
Σ raised
$32M
talent
—
α5M
+6.35%
αDeath
-8.84%
Top RFS themes
ai_build_enterprise_sw · 6none · 3ai_native_service_companies · 2foundation_models_biology · 2llm_back_office · 2
Vivian Midha Shen
Summer 2026 → Summer 2026 · -0.1y · 1 batches
1
companies
Σ raised
$0
talent
—
α5M
—
αDeath
—
Top RFS themes
ai_native_service_companies · 1llm_back_office · 1
Bottom line
Four independent ranking methods (per-batch alpha, age-bucket alpha, rank-aggregation, round-progression) all surface the same top tier: Jared Friedman, Tom Blomfield, Diana Hu, Nicolas Dessaigne, Aaron Epstein, Tyler Bosmeny. Per-batch composite tie at 0.404: Friedman and Blomfield. Friedman has 67% batch-consistency (beats YC base in 2 of 3 batches). Dessaigne leads rank-aggregation (0.735) and Series A progression (+5.54% — 10% of his portfolio reaches Series A vs 4.5% YC base). Diana Hu holds the decacorn crown (4.08% hit rate at $50M+) and αSeries B (+1.20%). Brad Flora reaches Series A at +2.24% alpha but underperforms on $-thresholds — his portfolio raises Series A at smaller round sizes (different style, not lower quality). Bottom line: the partner pool is small (17 people), the talent gap is measurable across 4 independent metrics, and picking your partner matters more than picking your RFS theme.