How To Actually
Get In.
What this is
We mapped 420+ people and 1,700+ public ties behind frontier AI. Then we analyzed how they broke in, built trust, raised capital, and became powerful. The patterns are below. The interactive map and book turn those patterns into something you can explore.
Three entry paths from the AI Power Map data. Most people in the dataset used one of these.
10-Year Career Arcs by Starting Point
Based on career arcs in the dataset, here is what a 10-year trajectory looks like from different starting points.
The PhD pipeline is fastest to high valuation. The enterprise pipeline is slowest but most reliable. The open-source pipeline is the most accessible from any starting point. Read more in the book →
From the book: 6 of 30+ patterns
- People who enter AI from enterprise, policy, or domain expertise often have more durable positions than people who enter from hype alone.
- Public teaching (blogs, open-source tools, datasets, tutorials) is the most common "wedge" for people who break in without elite credentials.
- Several founders in the map entered the AI circle after age 40 by owning a narrow enterprise problem that insiders hadn't solved.
- The most connected people in the graph are not always the most famous. Some "bridge nodes" quietly connect clusters that otherwise wouldn't talk to each other.
- Community builders and podcast hosts appear as high-centrality nodes despite having no ML research background. Networks reward connectors.
- The career paths that repeat most often: research lab, publication, co-founder trust, startup. But there are at least 5 other paths that work.
The book covers 30+ patterns including specific entry paths by background.
Explore the full map and book
Search 420+ people. See how they're connected. Read the 30+ patterns behind AI careers, funding, and power.