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.

In a field as complex as AI for science, even top experts know only a fraction of what's needed. Periodic Labs prioritizes intense curiosity and mission alignment over advanced degrees, recognizing that everyone, regardless of background, faces a steep learning curve to grasp the full picture.

OpenGov's CEO advises against the conventional wisdom of hiring salespeople with deep government experience. Instead, his company seeks hungry, courageous, and disciplined individuals and trains them internally on domain specifics, finding this approach more effective.

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 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.

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.

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performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.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.

In a paradigm shift like AI, an experienced hire's knowledge can become obsolete. It's often better to hire a hungry junior employee. Their lack of preconceived notions, combined with a high learning velocity powered by AI tools, allows them to surpass seasoned professionals who must unlearn outdated workflows.