A research instrument — not a rich list

What actually
transfers?

Fifteen of the world’s largest fortunes, taken apart line by line — every fact cited, every interpretation labelled and graded. Wealth figures refresh daily from the Forbes real-time list; the research underneath is hand-written and does not.

Live ledger2026-08-22
People analysed
15fully cited
Richest in the set
$857BElon Musk
Combined net worth
$3413Bmedian $186B
STEM background
67%10 of 15
Inherited outright
3 of 15no skill reproduces this
Fact · cited

Jeff Bezos graduated from Princeton in 1986 with a BSE in electrical engineering and computer science.

Verifiable, and carries a source naming the exact field it supports.

Interpretation · inferred

His work shows long-term thinking and customer obsession.

Our reading of public evidence, carrying a confidence level and the specific quote behind it. Never presented as a fact about how he thinks.

The shape of the set

Every field below has a source behind it.

Top industries

Number of people in each industry.

Headcount, not wealth. The Patterns page shows the same industries weighted by money, which looks very different.

Most common fields of study

Distinct fields across all recorded degrees, counted once per person.

Someone who studied two fields appears under both, so the bars sum to more than 15.

STEM vs non-STEM

Share of the group with a documented STEM education.

67%
33%
  • STEM10
  • Non-STEM5

67% studied a STEM field, but it tracks which industry someone entered far more closely than it tracks wealth — 9 founders here, and the non-STEM fortunes sit in retail, luxury and investing.

Most common mental models

How often each mental model is attributed across the group.

Interpretations of public evidence, not measured traits. Each attribution on a profile carries the specific evidence behind it.

Unsupervised clustering

Two archetypes, found without names

A k-means model grouped all fifteen by structure alone — industry, how the wealth arose, education, scale. Every claim prints the counts behind it, including what the sample size cannot support.

unsupervised
The other half of the record

Expensive mistakes

6 documented failures by the same people, with what each cost. Buffett's own estimate for buying Berkshire: $200bn in forgone value.

cited
The useful part

What is actually learnable

Eight traits you can practise, six you inherit or luck into, and an explicit refusal to move the second column into the first.

interpretation

The dataset

Search by name or company. The full table has every column and filter.

Full table →
15 of 15 people
  • Industry
    Automotive & Space
    Wealth type
    founder
    Country
    United States
    Age
    55
    Field of study
    Physics and Economics
    Background
    STEM
  • Industry
    Technology
    Wealth type
    founder
    Country
    United States
    Age
    53
    Field of study
    Computer Engineering
    Background
    STEM
  • Industry
    Technology
    Wealth type
    founder
    Country
    United States
    Age
    62
    Field of study
    Electrical Engineering and Computer Science
    Background
    STEM
  • Industry
    Technology
    Wealth type
    founder
    Country
    United States
    Age
    53
    Field of study
    Computer Science and Mathematics
    Background
    STEM
  • Industry
    Technology
    Wealth type
    founder
    Country
    United States
    Age
    61
    Field of study
    Pre-medicine
    Background
    STEM
  • Industry
    Technology
    Wealth type
    founder
    Country
    United States
    Age
    82
    Field of study
    Pre-medicine
    Background
    STEM
  • Industry
    Technology
    Wealth type
    founder
    Country
    United States
    Age
    42
    Field of study
    Psychology and Computer Science
    Background
    STEM
  • Industry
    Semiconductors
    Wealth type
    founder
    Country
    United States
    Age
    63
    Field of study
    Electrical Engineering
    Background
    STEM
  • Industry
    Fashion & Luxury
    Wealth type
    founder
    Country
    Spain
    Age
    90
    Field of study
    Background
    Non-STEM
  • Industry
    Technology
    Wealth type
    operator
    Country
    United States
    Age
    70
    Field of study
    Applied Mathematics and Economics
    Background
    STEM
  • Industry
    Fashion & Luxury
    Wealth type
    mixed
    Country
    France
    Age
    77
    Field of study
    Civil Engineering and Mathematics
    Background
    STEM
  • Industry
    Finance & Investments
    Wealth type
    investor
    Country
    United States
    Age
    95
    Field of study
    Business Administration
    Background
    Non-STEM
  • Industry
    Retail
    Wealth type
    heir
    Country
    United States
    Age
    81
    Field of study
    Business Administration
    Background
    Non-STEM
  • Industry
    Retail
    Wealth type
    heir
    Country
    United States
    Age
    78
    Field of study
    Business Administration (Marketing)
    Background
    Non-STEM
  • Industry
    Retail
    Wealth type
    heir
    Country
    United States
    Age
    76
    Field of study
    Economics
    Background
    Non-STEM

Net worth, rank and age refresh from the live feed (22 August 2026). Everything interpretive is hand-written and reviewed separately.