Since modern AI is so new, no one has more than a few years of relevant experience. This levels the playing field. The best hiring strategy is to prioritize young, AI-native talent with a steep learning curve over senior engineers whose experience may be less relevant. Dynamism and adaptability trump tenure.

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.

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.

When hiring, prioritize a candidate's speed of learning over their initial experience. An inexperienced but rapidly improving employee will quickly surpass a more experienced but stagnant one. The key predictor of long-term value is not experience, but intelligence, defined as the rate of learning.

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 hiring, especially for early-career talent, prioritize a candidate's rate of learning and growth potential (their 'slope') over their existing knowledge and experience (their 'y-intercept'). This framework allows you to identify individuals who can scale into massive roles, even if they lack direct experience today.

Since AI tools are new and their use is often restricted at legacy companies, prior experience is a poor predictor of success. Artemis prioritizes a candidate's eagerness to learn and operate at the cutting edge, teaching them their intensive, multi-instance workflows upon joining.

The "attitude vs. aptitude" debate is misleading. Hire the person with the smallest skill gap for the role. For complex roles, hire for intelligence (defined as rate of learning), as smart people can bridge any skill or attitude gap faster.

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.