Sam Altman argues that for young professionals, the most crucial hard skill to acquire is fluency with AI tools. He equates this to how learning to program was the key high-leverage skill a generation ago, suggesting it's more valuable than mastering any specific academic domain.

Sending a resume is now an outdated and ineffective way to get noticed by AI startups. The proven strategy is to demonstrate high agency by building a relevant prototype or feature improvement and emailing it directly to the founders. This approach has led to key hires at companies like Suno and Micro One.

Theoretical knowledge is now just a prerequisite, not the key to getting hired in AI. Companies demand candidates who can demonstrate practical, day-one skills in building, deploying, and maintaining real, scalable AI systems. The ability to build is the new currency.

When building core AI technology, prioritize hiring 'AI-native' recent graduates over seasoned veterans. These individuals often possess a fearless execution mindset and a foundational understanding of new paradigms that is critical for building from the ground up, countering the traditional wisdom of hiring for experience.

To build an AI-native team, shift the hiring process from reviewing resumes to evaluating portfolios of work. Ask candidates to demonstrate what they've built with AI, their favorite prompt techniques, and apps they wish they could create. This reveals practical skill over credentialism.

As AI renders cover letters useless for signaling candidate quality, employers are shifting their screening processes. They now rely more on assessments that are harder to cheat on, such as take-home coding challenges and automated AI interviews. This moves the evaluation from subjective text analysis to more objective, skill-based demonstrations early in the hiring funnel.

Companies now expect "entry-level" candidates to have proven capabilities to build and develop complete systems from day one. They've stopped hiring for potential, effectively raising the new entry-level bar to what was previously considered a mid-level standard.

Since AI assistants make it easy for candidates to complete take-home coding exercises, simply evaluating the final product is no longer an effective screening method. The new best practice is to require candidates to build with AI and then explain their thought process, revealing their true engineering and problem-solving skills.

At the start of a tech cycle, the few people with deep, practical experience often don't fit traditional molds (e.g., top CS degrees). Companies must look beyond standard credentials to find this scarce talent, much like early mobile experts who weren't always "cracked" competitive coders.