Workforce architecture for AI-era teams. Frameworks, not opinions.
The fastest-growing engineering title in startups. The role, how it differs from solutions engineering, how to interview for it, and when to hire.
What AI actually changes about hiring and what it does not. Decision guidance for founders choosing between recruiting automation and people.
The roles AI-native companies are hiring for that did not exist three years ago. Forward deployed engineers, GTM engineers, what they cost and when to hire them.
Foundational frameworks for designing AI-augmented teams. The five-dimension scoring model, role clarity, and leverage-first hiring.
Stage-specific hiring playbooks for seed through Series C. Compensation benchmarks, interview signals, and the equity trap.
How AI-first companies structure teams differently. Org charts, workflow architecture, and the shift from headcount to leverage.
What to do with your hiring plan after raising. Management layers, exec hires, and scaling without losing velocity.
The new org building block: individual contributors who use AI to operate at the output of a 3-5 person team.
The decision framework for every role. When to hire full-time, when to automate, and when a super IC is the answer.
A framework for deciding your highest-leverage hire when AI changes what every role can accomplish.