When hiring senior engineers, the crucial test is whether they can build. This means assessing their ability to take a real-world business problem—like designing a warehouse system—and translate it into a tangible technical solution. This skill separates true builders from theoretical programmers.

A person's past rate of growth is the best predictor of their future potential. When hiring, look for evidence of a steep learning curve and rapid progression—their 'slope.' This is more valuable than their current title or accomplishments, as people tend to maintain this trajectory.

Exceptional individuals often publish their thoughts online. By reading their content, you can assess their thinking, expertise, and confluence of ideas, making a traditional interview redundant. This allows you to move decisively when you find a match, as when the speaker hired his Opendoor cofounder on the spot.

A common hiring mistake is prioritizing a conversational 'vibe check' over assessing actual skills. A much better approach is to give candidates a project that simulates the job's core responsibilities, providing a direct and clean signal of their capabilities.

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.

The most promising junior candidates are those who demonstrate self-learning by creating things they weren't asked to do, like a weekend app project. This signal of intrinsic motivation is more valuable than perfectly completed assignments.

Ditch standard FANG interview questions. Instead, ask candidates to describe a messy but valuable project they shipped. The best candidates will tell an authentic, automatic story with personal anecdotes. Their fluency and detail reveal true experience, whereas hesitation or generic answers expose a lack of depth.

Dropbox's founders built their team using a first-principles approach, prioritizing exceptional talent even when candidates lacked traditional pedigrees or direct experience for a role. This strategy of betting on the person's potential over their polished resume proved highly effective for scaling.

For cutting-edge AI problems, innate curiosity and learning speed ("velocity") are more important than existing domain knowledge. Echoing Karpathy, a candidate with a track record of diving deep into complex topics, regardless of field, will outperform a skilled but less-driven specialist.