Founder DNA.
How breakout companies mint the next generation of founders. The startup-mafia lineage map and rankings, then the DNA behind it: where breakout founders come from, what they studied, how they migrate — and the score that predicts the next ones.
How founders spread from one company to the next — and how each mafia ranks.
The lineage map first: click any blue or yellow node to open that mafia's chart. Edge styles separate founder, employee, acquisition/spinout and investor links. Below it, the rankings.
How they rank — unicorns vs revenue scale.
Two ways to score a mafia: by the unicorns its alumni produce, or by the $100M+ revenue companies (a tougher bar, no valuation hype). Toggle outcome, narrow the cohort, change sort.
% of alumni startups (raised ≥$1M) that reached $100M+ revenue — a durability bar harder to fake.
The rankings show which mafias convert best. In absolute terms the biggest feeders are still the giants — the companies below have seeded the most alumni founders overall.
The biggest feeders
Companies whose alumni have founded the most startups — from 44,670 alumni-founder records.
Where breakout founders come from.
Country of origin for the founders behind every unicorn, thoroughbred and colt — resolved from biography and education history, not current address. Absolute counts on the left; who punches above their weight per 1M inhabitants on the right.
Origin of all founders
6,623 of 16,511 tracked founders have a known origin.
Origin of all founders per 1M inhabitants
Global average 0.86 per 1M. Countries with 3+ founders and 1M+ population.
What breakout founders studied.
Degrees, fields of study and the universities that mint breakout founders — 30,288 education records across 16,511 founders.
Highest degree attained
59.7% hold a postgraduate degree — 1 in 7 a PhD. Of 8,948 founders with degree data.
Fields of study
Founders holding degrees in several fields count once per field.
Those degrees cluster in a familiar set of schools — Stanford, MIT and Harvard top the table — and the country mix shows how much of the pipeline still runs through the US.
Top universities
University countries
Education is also where mobility starts: for a third of breakout founders, the first border crossing happens on the way to campus.
The first move happens at university
Where known-origin founders went to study — the on-ramp for the migration flows in section 04.
Breakout founders move — mostly west, mostly to the Bay.
Cross-border moves tracked through two lenses: where a founder comes from vs where their company sits today, and where the company was founded vs where its HQ is now.
Corridor by corridor the pull is one-directional: into the US, and into the Bay Area above all. Switch the lens to company relocations — the HQs follow the founders.
The main migration corridors
Founder lens: a founder's researched country of origin vs their company's current HQ country — every founder-corridor pair counted once (unicorn / thoroughbred / colt cohort). Company lens: companies whose founding country differs from their current HQ country — either the deep-research-verified unicorn & thoroughbred cohort, or every company in the Dealroom database with both countries on record.
Flip from where founders leave to where they land and the pattern repeats: the strongest ecosystems are built by people born somewhere else.
Startup ecosystems run on immigrants
Default view counts immigrant founders as a share of known-origin founder records per startup location. Switch to startup level for the share of startups with at least one immigrant founder.
Unicorns and thoroughbreds only — colts are excluded, as are companies with no researched founder origin. A founder counts as immigrant when their researched origin differs from the startup's current HQ country; Silicon Valley / Bay Area uses the startup's metro within the US. Regional and world bars aggregate all researched companies. Countries shown have 60+ known-origin founder records.
One score to spot the next breakout founders.
Everything on this page — lineage, rankings, origins, education, mobility — feeds a proprietary founder-strength model. It already scores every founder behind the world's breakout companies, and it's learning to flag the next ones before they break out. The predictions live in the platform; this page is just the trailer.
Illustrative distribution — the model scores every founder and hunts in the right tail.
Map talent movement in real time.
The full Dealroom platform unlocks people profiles, founder backgrounds, alumni networks, hiring movement and AI talent signals across the global tech ecosystem.