Employees at large companies who independently work nights and weekends on projects outside the roadmap, driven by customer obsession, are exhibiting the key traits of a founder. This behavior, while potentially disruptive to their team, signals a strong, innate entrepreneurial drive ready to be unleashed.

Prioritize hiring generalist "athletes"—people who are intelligent, driven, and coachable—over candidates with deep domain expertise. Core traits like Persistence, Heart, and Desire (a "PhD") cannot be taught, but a smart athlete can always learn the product.

In the fast-evolving world of AI, the most valuable trait in a designer is a deep-seated curiosity and the self-direction to learn and build independently. A designer who has explored, built, and formed opinions on new AI products is more valuable than one with only a perfect aesthetic.

The ideal early startup employee has an extreme bias for action and high agency. They identify problems and execute solutions without needing approvals, and they aren't afraid to fail. This contrasts sharply with candidates from structured environments like consulting, who are often more calculated and risk-averse.

Avoid hiring a growth leader with a big-name pedigree for your early team, as they are often unsuited for the necessary hands-on experimentation. Instead, seek young, hungry builders who are motivated by chaos and comfortable rebuilding their own work as the company's needs evolve.

Lovable prioritizes hiring individuals with extreme passion, high agency, and autonomy—people for whom the work is a core part of their identity. This focus on intrinsic motivation, verified through paid work trials, allows them to build a team that can thrive in chaos and drive initiatives from start to finish without supervision.

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.

The very best engineers optimize for their most precious asset: their time. They are less motivated by competing salary offers and more by the quality of the team, the problem they're solving, and the agency to build something meaningful without becoming a "cog" in a machine.

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.