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.

The most promising hires are often high-agency individuals constrained by their current environment—'caged animals' who need to be unleashed. Look for candidates who could achieve significantly more if not for their team or organization's limitations. This is a powerful signal of untapped potential and resourcefulness.

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.

When evaluating a candidate's job history, distinguish between those 'running towards' an exciting opportunity and those 'running from' a negative situation. The former is a strong positive signal of ambition and conviction. The latter can be a red flag that warrants deeper investigation into potential performance or culture fit issues.

Individual effort is like climbing a ladder, but working at a rapidly growing company puts that ladder on an escalator. The company's momentum creates opportunities and upward movement for you that are independent of your own climbing speed, drastically accelerating your career progression.

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.

High-growth companies create a virtuous cycle for talent. The faster a company grows, the more career advancement opportunities it creates, which attracts the best people. This influx of A-players then accelerates growth further. Conversely, stagnation creates a vicious cycle, repelling top candidates and making growth harder to achieve.

In rapidly evolving fields like AI, pre-existing experience can be a liability. The highest performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.

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.