Field notes on hiring for leverage, building AI-first teams and scaling operations that hold.
A practical playbook for landing an AI engineering job — built on Andrew Ng's four-skill map, with the portfolio, interview answers and signals hiring teams actually reward.
AI-first, AI-native and digital-first are often used interchangeably — they are not. Clear definitions of each term, and why the difference matters for hiring.
AI-first talent is a subject matter expert who multiplies their domain expertise with AI. The complete definition, what it is not, and how to hire for it.
Andrew Ng's skills map puts fundamentals second for a reason. Here are the four interview prompts, a scoring rubric, and the answers that separate judgment from vibe-coding.
Andrew Ng's deep dive into the first AI engineering skill — what it really takes to build and deploy AI applications that work in production, from LLM foundations to evaluation-driven development.
A complete guide to Andrew Ng's AI Engineering Skills Map — all four skills explained, how they map to the AI-First Talent framework, and how to screen for them.
Performance calibration breaks when half the output is AI-assisted. Here is the rubric, the evidence standard, the bias controls and the agenda we use to keep ratings fair when leverage — not hours — is the unit of work.
Candidates now arrive with AI-assisted answers. Here is how to tell rehearsed output from real thinking — the signals, the follow-up questions, and the live-work alternative that makes scripting pointless.
Most companies add headcount to add hours. AI-first companies add headcount to add impact. Here is how to tell the difference before you make the offer.
Architect, Entrepreneur, Operator, Auditor, Explorer, Multiplier — the six competencies we screen for when hiring AI-first talent, and the questions that reveal each one.