Legora intentionally hires people with high learning velocity ("high Y slopes") over deep experience ("high Y intercepts"). In a rapidly evolving AI landscape, this ensures the team can scale their capabilities as exponentially as the company grows.

Early-stage startups can't win on salary. The ideal hire is a veteran from a top tech company who has already achieved financial security. They are motivated by passion for the mission, not compensation, and are more likely to accept an equity-heavy package.

In contrast to widespread tech layoffs, ServiceNow is prioritizing hiring early-career professionals with 0-2 years of experience. The strategy is to tap into a generation of "AI natives" who intuitively leverage new AI tools, viewing this as a key advantage over experienced but less-adapted talent.

When building his internal developer tools team at Meta, Adrian's hiring strategy was simple: find talented engineers who were already building similar tools on the side out of passion or necessity. He then offered them the chance to turn that side-hustle into their full-time, high-impact job.

Eleven Labs bypasses traditional hiring signals by looking for talent based on demonstrated skill. They hired one of their most brilliant researchers, who was working in a call center, after discovering his incredible open-source text-to-speech model. This underscores the value of looking beyond resumes.

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.

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.

Ramp's hiring philosophy prioritizes a candidate's trajectory and learning velocity ("slope") over their current experience level ("intercept"). They find young, driven individuals with high potential and give them significant responsibility. This approach cultivates a highly talented and loyal team that outperforms what they could afford to hire on the open market.

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.