In DoorDash's dynamic environment, any given job will materially change every 18 months. Consequently, their hiring philosophy prioritizes identifying candidates with a high trajectory for learning, adapting to complexity, and dealing with ambiguity, rather than just current qualifications.

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.

Instead of focusing solely on a candidate's current skills, Figma's CEO looks for their 'slope,' or their trajectory of rapid learning and improvement. This is assessed by analyzing their history of decision-making and growth mindset, betting on their future potential rather than just their present abilities.

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.

In highly dynamic and unstructured startup environments, hiring for high potential ("slope") is more effective than hiring for deep experience ("intercept"). Experienced hires from structured companies often perceive the environment as chaotic and fail to adapt, whereas high-slope individuals see it as normal and thrive.

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.

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.

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.