Hiring managers often create AI-specific roles thinking it attracts experts. Instead, they should frame job descriptions around the complex problems the business needs to solve. This attracts true problem-solvers who can learn any necessary technology, rather than individuals skilled at keyword optimization.

When building for a specific domain like insurance, the best hiring strategy isn't to find unicorn candidates with both AI and deep industry expertise. Instead, hire top-tier AI talent and top-tier domain experts and have them collaborate closely, sitting them "next to each other" alongside customers.

For roles leveraging new technologies like AI, where tools are nascent and constantly changing, competency is a fleeting metric. Instead, hire for curiosity. A curious mind will adapt, learn, and master new tools as they emerge, making them a more valuable long-term asset.

When building a team for a new domain like AI, Shai intentionally seeks people with deep passion and native understanding, even with zero marketing background. He believes his team can teach marketing fundamentals but cannot instill genuine passion or an "AI-first" mindset.

Getting hired at a premier AI lab like Google DeepMind often bypasses traditional applications. Top researchers actively scout and directly contact individuals who produce work that demonstrates excellent "research taste." The key is to independently identify and pursue fruitful research directions, signaling an innate ability to innovate.

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.

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.

Perplexity's talent strategy bypasses the hyper-competitive market for AI researchers who build foundational models. Instead, it focuses on recruiting "AI application engineers" who excel at implementing existing models. This approach allows startups to build valuable products without engaging in the exorbitant salary wars for pre-training specialists.

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.