AS
Aya Spencer
Program Manager · Insights Lead
Startup intelligence · 2025 LinkedIn
Startup Ecosystem Analysis · 2020–2025
Color guide: Founder signalFund signalAngel signalSector signal

What signals define
a winning startup

A deep analysis of 1,390 high-growth companies founded between 2020 and 2025 — covering founder backgrounds, fund patterns, angel networks, sector dynamics, and the signals that define winning companies.

About this dataset
SCOPE
Total companies1,390
Founding window2020 – 2025
Peak founding year2021 (358 cos)
GeographiesGlobal, US-dominant
Top citySan Francisco
FUNDING TIERS
All companies1,390
Raised $10M+712 (51%)
Raised $50M+300 (21.6%)
Raised $100M+160 (11.5%)
Raised $500M+38 (2.7%)
Raised $1B+20 (1.4%)
Companies founded per year
2020: 350, 2021: 358, 2022: 264, 2023: 235, 2024: 141, 2025: 42
2020–2021 are the largest cohorts — companies founded then have had 4–5 years to compound, making them the most represented in the $100M+ tier. 2024–2025 companies are early-stage and underrepresented by design.
Companies by sector
AI/ML leads with 516 companies, followed by FinTech/Crypto at 235.
AI/ML is the dominant sector at 37% of the dataset — nearly double FinTech/Crypto in second place.
Key findings — across all areas
Founder profiles → see Founder profiles tab for full detail
The operator premium
Seasoned operators jump from 71% of all companies to 89% of $100M+ funded — the most consistent signal across the entire dataset. Operator experience outranks technical depth as a predictor of scale.
+18pp lift at $100M+
Top company alumni density
The biggest signal gap in the dataset: 39% of all companies have top company alumni, vs 71% of $100M+ funded. Google, Palantir, Stripe, and SpaceX are the highest-signal feeders.
+32pp — largest gap
AI experience: fastest-rising signal
Only 12% of all companies have top AI experience on the team — but 40% of $100M+ funded companies do. A 3.3x concentration that is almost certainly still underpriced at the early stage.
3.3× in winners
Fund analysis → see Fund analysis tab for full detail
a16z leads volume; Lightspeed leads quality
Andreessen Horowitz has the most companies (413) but Lightspeed's portfolio averages $128M raised per company — the highest of any fund. Different theses, both effective.
a16z: 413 cosLightspeed: $128M avg
General Catalyst owns HealthTech
GC leads HealthTech with 56 companies — nearly double second-place a16z at 38. Also leads CleanTech. The only fund with dominant positions in two separate sectors.
HealthTech #1CleanTech #1
Cybersecurity has no clear winner
Sequoia and Lightspeed are tied at the top of Cybersecurity (50 and 42 companies) — the only major sector without a single dominant fund. Specialist fund Cerca Partners is the name to watch.
Sequoia + Lightspeed tiedCybersecurity
Angel investors → see Angel investors tab for full detail
Edward Lando: most prolific by far
127 companies across the dataset — nearly double second-place Nadav Ben-Chanoch at 66. Lando operates a volume strategy concentrated in FinTech and Crypto, through Pareto Holdings.
127 companies
Nat Friedman + Daniel Gross: highest quality signal
12 and 9 companies in the $50M+ cohort respectively — and they co-invest frequently. Via AI Grant, they back a concentrated set of AI companies that disproportionately reach top funding tiers.
AI Grant co-founders
Eric Schmidt: most concentrated in winners
13 companies in the $50M+ cohort — the highest ratio among individuals. Exclusively AI infrastructure and Defense/Space. His presence on a cap table is one of the strongest individual quality signals.
13 in $50M+ tier
Sectors → see Sectors tab for full detail
AI/ML is 37% of the entire dataset
516 companies — by far the largest sector. The Palantir alumni network is its strongest talent source, and top AI experience is 3.3x more concentrated in AI winners than in the overall dataset.
516 companies37% of dataset
Defense/Space: newest and fastest-growing cohort
42 companies, but the newest founding window — mostly 2022–2023. Driven by geopolitical tailwinds and drone/autonomous systems demand. a16z's American Dynamism thesis dominates here with 18 companies.
Newest cohorta16z dominant
CleanTech: the grant-backed outlier
44 companies, with the highest ratio of grant-funded companies of any sector. Sam Altman is the most active individual angel with 4 CleanTech bets. GC leads (14 cos), followed by Khosla and Lightspeed at 7 each.
Most grant-fundedSam Altman top angel
Winners → see Winners tab for the full anatomy
The 3 signals that separate winners from everyone else
Across 160 companies that raised $100M+, three signals show the sharpest separation from the rest of the dataset:

1. Cap table density. Winners average 20 investors vs 9 for all companies — and 29% have 3 or more top-tier funds simultaneously. This isn't a byproduct of raising more; it reflects deliberate network construction from day one.

2. Top company alumni density. The +32pp lift is the single largest gap in the dataset. Winners don't just have one ex-Google — they average 5.4 top company alumni mentions across their team, vs 1.6 overall.

3. Headcount velocity. Winners grow their teams at 104% year-over-year — 2.5x the rate of smaller companies. LinkedIn follower growth is 91% over 90 days vs 35% overall. These show up early and are measurable before a company reaches Series B.
20 avg investors 5.4× alumni density 104% headcount growth → Winners tab
Founder profiles · 1,390 companies
Color guide: Founder signal

What does a startup
founder actually look like?

Each section below shows the same signal twice — once across the full dataset, and once filtered to winning companies ($100M+ funded). The gap between them is where the real insight lives.

Background signals
Across the full dataset n = 1,390
In winning companies $100M+ funded · n = 160
How founders typically look
Seasoned operator
71%
Seasoned executive
61%
Prior VC-backed founder
49%
Prior exit
47%
Deep technical background
44%
Elite industry (BCG/GS)
36%
Top company alumni
39%
Top AI experience
12%
Deep technical background is the most common signal — but has zero predictive lift
Present in 44% of all companies, it's table stakes. The average dataset founder is an operator with prior VC exposure who may or may not have exited before.
How winning founders differ
Seasoned operator
89%
+18pp
Seasoned executive
81%
+20pp
Prior VC-backed founder
57%
+8pp
Prior exit
54%
+7pp
Deep technical background
44%
0pp ↔
Elite industry (BCG/GS)
42%
+6pp
Top company alumni
71%
+32pp ↑↑
Top AI experience
40%
+28pp ↑↑
Two signals dominate: top company alumni (+32pp) and top AI experience (+28pp)
These are the sharpest gaps. Technical background alone is irrelevant. Operator experience is near-universal. The differentiators in winning teams are where people worked and whether they have hands-on AI expertise.
University backgrounds
Top feeder universities — full dataset
University signal in winning companies
Where founders studied
Stanford and UC Berkeley account for more than 2,400 team member mentions combined — more than the next four schools combined.
Elite university density in winners
Stanford / Harvard / MIT team member86%
vs 58% across all companies — a 28pp gap
At least 2 elite university alumni on team64%
vs 31% overall — density across the team, not just the founder
MBA operator alongside technical founder~41%
HBS + Wharton MBA presence signals operator layer added to technical core
It's about team density, not founder pedigree
The signal isn't where the CEO went to school — it's whether elite-university alumni are distributed across the core team. Winners build orgs where this is the norm, not the exception.
Tech company talent pipelines
Most common prior employers — full dataset
The Palantir effect in winning companies
Where teams came from
Google alumni appear in 606 company teams — more than Microsoft (425) and Amazon (331) combined. Big Tech dominates the talent pipeline overall.
Palantir: the highest-signal company background
78 companies have Palantir alumni — 3× concentrated in winners
5.6% of all companies → 16.9% of $100M+ funded
Palantir's deployment-first, enterprise-obsessed culture produces founders who sell to large organizations, navigate complex integrations, and work with sensitive data at scale. These skills map directly onto AI infrastructure and vertical AI — the hottest categories in this dataset.
ElevenLabsLangChainDecagonSierraTogether AIMistral AITennrSupabaseCyera+69 more
Palantir beats every other company background as a winner predictor
Google alumni are 25× more common in the dataset — but Palantir alumni are 3× more concentrated in winners. Volume isn't the signal. Palantir's specific culture of enterprise deployment and AI-at-scale is.
Fund analysis · 1,390 companies
Color guide: Founder signalFund signalSector signal

Which funds back
the most companies — and the most capital?

Each section compares fund behavior across the full dataset vs. their presence specifically in $50M+ funded companies. Volume and quality tell different stories.

Fund reach and portfolio quality
Across the full dataset n = 1,390
In high-funded companies $50M+ funded · n = 300
Most active funds by company count
a16z backs 413 companies — 30% more than second-place General Catalyst (294). Y Combinator's 162 represents early-stage pipeline that feeds into later institutional rounds.
Funds in $50M+ companies (quality signal)
Lightspeed jumps to #2 by quality despite being #4 by volume — its portfolio averages $128M raised per company, the highest of any fund. Selectivity, not volume, is the Lightspeed model.
The Lightspeed anomaly: fewer bets, bigger outcomes
232 companies total (4th by volume) → 73 in the $50M+ cohort (2nd by quality). A 31.5% "hit rate" vs a16z's 26.4%. When Lightspeed leads, the average raise is $128M vs the dataset's $86M median for top funds.
Entry stage strategy
When funds enter — full portfolio
What this means for $50M+ high-funded companies
Seed vs Series A vs Series B+ across top 5 funds
Seed Series A Series B+
Khosla is the most seed-heavy at 41% of its portfolio at Seed stage — the highest ratio. a16z and GC both run broad stage coverage, entering at all three levels.
What entry stage means for high-funded companies
Khosla's seed thesis produces outsized hits
Entering earliest = most upside exposure. Khosla's 41% seed ratio, combined with 5 grant-backed bets, reflects a willingness to fund unproven science that larger funds won't touch. When it works, the returns are asymmetric.
Lightspeed's Series B+ concentration explains its quality lead
More Series B+ entries means Lightspeed is writing larger checks into already-proven companies. Higher average funding per company is partly a cause and partly an effect of this later-stage skew.
a16z's broad-stage presence creates the most diverse high-funded portfolio
By entering at all stages, a16z maximizes surface area. Its 109 companies in the $50M+ tier span every sector — a portfolio breadth no other fund matches.
Sector specialization
Fund sector coverage — full dataset
Sector leadership in high-funded companies
Who dominates each sector
AI/ML
a16z
140 companies — largest AI portfolio in dataset
HealthTech
General Catalyst
56 companies — nearly 2× second-place a16z (38)
Cybersecurity
Sequoia + Lightspeed (tied)
50 and 42 companies — no single leader
Defense / Space
a16z
18 companies via American Dynamism thesis
CleanTech
General Catalyst
14 companies; Khosla #2 with grant-backed bets
FinTech / Crypto
a16z / a16z crypto
77 + 65 via dedicated crypto vehicle
Specialist funds punching above their weight
Cerca Partners — Cybersecurity specialist
Top-5 presence in the Cybersecurity $50M+ cohort despite being a small fund. Deep sector expertise over broad coverage. The fund to watch if you're tracking high-funded security companies early.
CybersecuritySpecialist fund
Lux Capital — Defense / Space
#2 in Defense/Space behind a16z with 7 companies. Deep science mandate. Consistent in sectors most funds avoid — and consistently right about them.
Defense / SpaceSpecialist fund
MCJ — CleanTech
Community-driven climate fund with a differentiated LP base and strong operator network in clean energy. Increasingly appearing in $50M+ CleanTech companies alongside GC and Khosla.
CleanTechSpecialist fund
Angel investors · 1,390 companies
Color guide: Founder signalFund signalAngel signal

Who are the most
connected individuals?

Volume tells you who writes the most checks. Quality — concentration in high-funded companies — tells you whose judgment predicts outcomes. The two lists are very different.

Angel reach and signal quality
Most prolific angels — full dataset n = 1,390
Highest quality signal — $50M+ funded companies
Most companies backed (volume)
#InvestorCosFocus
Most companies in $50M+ cohort (quality)
Eric Schmidt has the highest concentration in high-funded companies despite lower volume
13 companies in the $50M+ cohort from a smaller total portfolio — a higher hit rate than any volume player. His signal is quality, not quantity. When Schmidt backs a company, it's meaningful.
Angel investor profiles
What each angel typically backs
What makes them a signal in high-funded companies
Angel strategies across the dataset
Edward Lando — Volume leader
127 companies · FinTech/Crypto focus · Pareto Holdings
The most prolific individual investor by a wide margin. Invests early and broadly. Volume strategy — 16 of 127 reach $50M+ (~12.6% hit rate, in line with the overall dataset).
Gokul Rajaram — Operator-angel
44 companies · B2B SaaS and marketplaces
Ex-Facebook, Square. Known for providing deep GTM help alongside capital. ~20% hit rate in the $50M+ tier — above average for his volume level.
Sam Altman — Thematic bets
28 companies · AI/ML + CleanTech
Most active individual angel in CleanTech (4 companies) — concentrated for someone with 28 total. Alignment with nuclear and long-horizon science bets alongside AI/ML.
Why these angels appear in high-funded companies
Nat Friedman + Daniel Gross — Highest quality AI signal
12 and 9 companies in $50M+ cohort · often co-invest via AI Grant
Their co-investment pattern in AI companies is one of the strongest individual quality signals in the dataset. When both are on the cap table, the company is almost always in the top funding tier. They don't back broadly — they pick specifically.
AI backgroundCo-invest pair
Eric Schmidt — Strategic credibility
13 in $50M+ cohort · AI infrastructure + Defense
Exclusively AI infrastructure and Defense/Space. His presence unlocks government relationships, enterprise introductions, and credibility that no check size alone can buy. Highest concentration ratio among individuals.
Highest concentration ratio
Jeff Dean — Technical legitimacy
8 in $50M+ cohort · 100% AI/ML
Head of Google AI. Every known investment is in AI/ML. When Dean backs a company, it signals deep technical legitimacy — the kind that attracts top AI researchers and enterprise customers who care about credibility.
AI-exclusiveTechnical credibility
Sector breakdown · 1,390 companies
Color guide: Founder signalFund signalAngel signalSector signal

Where are startups
being built — and winning?

Company volume by sector tells you where founders are concentrating. Winner concentration tells you where the returns are actually materializing.

Sector volume vs winner concentration
All companies by sector n = 1,390
Winners by sector $100M+ funded · n = 160
Where founders are building
AI/ML dominates at 37% of all companies. FinTech/Crypto (17%) is a distant second; HealthTech (11%) and Cybersecurity (10%) are closely matched for fourth and fifth.
Where winners are clustering
AI/ML holds roughly steady — 37% of all companies vs 34% of winners. HealthTech overtakes Cybersecurity in the winners cohort, moving from #5 by volume to #3 by winner count — the clearest sector rank reversal in the dataset.
HealthTech has the best volume-to-winner conversion in this dataset
157 companies → 21 in the $100M+ tier. That's a 13.4% hit rate vs the dataset average of 11.5%. Smaller funnel, higher quality output — driven partly by longer regulatory cycles that naturally filter for more committed founders.
Sector deep dives
Sector dynamics — full dataset
What high-funded companies look like in each sector
How each sector behaves
AI/ML · 516 companies
Largest sector at 37% of dataset. Peak founding 2020–2021, second wave 2023–2024 driven by LLMs. a16z (140) and Sequoia (112) dominate. YC has outsized early-stage presence.
FinTech / Crypto · 235 companies
a16z crypto functions as a separate vehicle (65 cos). Coinbase is a major strategic investor (43 cos). Edward Lando is the #1 individual angel with 33 FinTech companies.
Cybersecurity · 199 companies
No single fund dominates — Sequoia and Lightspeed are tied. Founding peak 2021–2022. High average funding relative to dataset. Enterprise security rounds are large.
HealthTech · 157 companies
General Catalyst leads at 56 companies — nearly double a16z (38). GV at #3 reflects Google's healthcare push. YC unusually prominent, indicating strong early-stage pipeline.
Defense / Space · 45 companies
Newest cohort — mostly 2022–2023 founded. a16z dominates with 18 companies via American Dynamism. Lux Capital is the specialist fund. Geopolitical tailwinds driving rapid growth.
CleanTech · 44 companies
Highest ratio of grant-funded companies of any sector. GC leads (14), Khosla and Lightspeed at 7 each. Sam Altman is the most active individual angel with 4 CleanTech bets.
What separates high-funded companies in each sector
AI/ML high-funded companies
Top AI experience on the team is 3.3× more common in AI high-funded companies than the full AI sector. Palantir alumni appear at 3× the base rate. The talent signal matters more here than in any other sector.
Palantir 3× signalAI experience 3.3×
FinTech / Crypto high-funded companies
Best predictor: prior VC-backed founder on the team. FinTech regulations mean experienced operators matter more than in other sectors. High-funded crypto companies skew toward infrastructure plays, not consumer apps.
Prior founder signalFinTech / Crypto
Cybersecurity high-funded companies
Average funding in the $50M+ Cybersecurity cohort is higher than any other sector. Enterprise security commands large rounds. Cerca Partners as a specialist backer is a quality signal at early stages.
CybersecurityHighest avg round
HealthTech high-funded companies
Elite industry experience (ex-McKinsey, BCG, Oliver Wyman) is more common in HealthTech high-funded teams than any other sector. Regulatory navigation requires a different kind of operator.
Consulting backgroundsHealthTech
Defense / Space high-funded companies
Both a16z and Lux Capital on the cap table together is the strongest co-investment signal in this sector. Government contract experience on the team is a near-universal trait among high-funded companies.
a16z + Lux co-investDefense / Space
CleanTech high-funded companies
Grant funding early is a feature, not a bug — it signals scientific credibility and extends runway while the business model matures. Sam Altman backing is the highest-signal individual indicator in this sector.
CleanTechSam Altman signal
★ Winners — the signal summary
Color guide: Founder signalFund signalAngel signalSector signalWinner signal

What does a winning
startup actually look like?

This tab synthesizes signals across founders, funds, angels, and sectors — filtered exclusively to the top-funded companies. It is designed to surface underrated, early-detectable patterns that predict startup success before the market does.

How we define a winner
A winner in this dataset is any company that has raised $100M or more in total funding — representing the top 11.5% of all 1,390 companies analyzed. This threshold was chosen because it signals institutional validation across multiple fund cycles, meaningful revenue or traction, and the ability to attract top talent at scale. It is not a guarantee of success, but it is the strongest proxy for momentum and market conviction available in this dataset. 160 companies meet this threshold.
160
Winner companies
$100M+
Funding threshold
11.5%
Of full dataset
$128M
Median raise (winners)
A note on "high-funded" vs "winner"
Elsewhere on this site you'll see the term "high-funded companies" — this refers to the broader $50M+ funded cohort (300 companies, 21.6% of the dataset), a separate and larger group than the $100M+ winner tier defined above. The two terms are intentionally not interchangeable: "winner" is reserved exclusively for the $100M+ threshold, while "high-funded" describes the wider $50M+ group used in the Fund analysis and Angel investors tabs. Watch for this distinction as you move across tabs — a chart or insight labeled "high-funded" is working with a larger, less selective sample than one labeled "winners."
Unique and underrated winner signals
Patterns that appear in winners but not in the broader dataset
🎯
Palantir alumni: the #1 founder signal
3×
Palantir experience appears in 16.9% of winners vs 5.6% overall — the largest company-specific concentration in the dataset. Google is 25× more common in the dataset but is not overrepresented in winners. Palantir's enterprise-deployment culture is what makes founders who scale.
🤖
Top AI experience: fastest-rising, still underpriced
3.3×
Present in only 12% of all companies, but 40% of winners. This 28pp gap is the second-largest signal gap in the dataset. At the early stage, a team with hands-on AI experience at OpenAI, DeepMind, or Google Brain is still being underweighted relative to its predictive value.
🏛️
Team university density — not just founder pedigree
86%
86% of winners have Stanford, Harvard, or MIT somewhere on the team — vs 58% overall. But the real insight is that 64% have two or more elite alumni vs 31% overall. It's about density across the org, not a single founder credential on a pitch deck.
💼
The operator-executive stack
89%
Seasoned operator (89%) combined with a seasoned executive (81%) appears together in nearly every winning company. This co-pattern — not either signal alone — is what defines the team structure. A technical founder alone is insufficient. The wrapper matters as much as the core.
📊
Cap table density predicts outcome
20 avg
Winners average 20 investors on the cap table vs 9 overall — and 29% have 3+ top-tier funds simultaneously. This isn't just a byproduct of raising more. Winners build their cap tables deliberately as distribution, hiring, and customer networks. Cap table density is measurable from day one.
📈
Headcount velocity: the earliest measurable signal
104%
Winners grow headcount at 104% year-over-year — 2.5× faster than smaller companies. LinkedIn follower growth is 91% over 90 days vs 35% overall. These signals are measurable before a company reaches Series B. Hiring velocity is one of the earliest publicly observable indicators of institutional momentum.
🤝
The a16z + Lightspeed co-investment pair: strongest fund signal
13 cos
When both Andreessen Horowitz and Lightspeed back the same company, it appears in 13 winner companies — the most common tier-1 co-investment pair. These two funds have genuinely different investment philosophies. When they agree on a bet, it signals conviction that transcends any single fund's thesis. Sequoia + Lightspeed (12) and GC + Khosla (9) round out the top combos.
Winner profile — across all four dimensions
Founder profile
Seasoned operator89%
Top company alumni on team71%
Top AI experience40%
Stanford / Harvard / MIT86%
Palantir alumni17%
Prior exit on team54%
Operator > technical depthPalantir overindex
Fund backing
Avg investors on cap table20
Have 3+ top-tier funds29%
a16z + Lightspeed co-invest13 cos
Lightspeed avg funding$128M
GC dominates HealthTech56 cos
Specialist fund in sectorKey signal
Cap table densityFund co-invest pairs
Angel backing
Eric Schmidt (highest winner ratio)13 cos
Nat Friedman (AI Grant)12 cos
Daniel Gross (AI Grant)9 cos
Jeff Dean (AI legitimacy)8 cos
Sam Altman8 cos
Gokul Rajaram (~20% hit rate)9 cos
Schmidt = quality signalAI Grant pair
Sector & context
Founded2020–2021
CitySan Francisco / NYC
Top sectorsAI/ML, FinTech, Health
HealthTech hit rate13.4% (best)
Headcount growth (1yr)104%
LinkedIn growth (90d)91%
HealthTech best conversionSF/NYC dominant
The leaderboard — top 20 companies by total funding
The companies that define what winning looks like in 2020–2025
# Company City Raised Headcount HC growth 1yr