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.

Senior leaders now value candidates who ask excellent questions and are eager to solve problems over those who act like they know everything. This represents a significant shift from valuing 'knowers' to valuing 'learners' in the workplace.

Don't hire based on today's job description. Proactively run AI impact assessments to project how a role will evolve over the next 12-18 months. This allows you to hire for durable, human-centric skills and plan how to reallocate the 30%+ of their future capacity that will be freed up by AI agents.

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.

In the fast-evolving world of AI, the most valuable trait in a designer is a deep-seated curiosity and the self-direction to learn and build independently. A designer who has explored, built, and formed opinions on new AI products is more valuable than one with only a perfect aesthetic.

Dr. Fei-Fei Li states she won't hire any software engineer who doesn't embrace AI collaborative tools. This isn't about the tools' perfection, but what their adoption signals: a candidate's open-mindedness, ability to grow with new toolkits, and potential to "superpower" their own work.

Forcing an 'AI culture' is short-sighted. The real goal is to foster a culture that prioritizes continuous growth and learning. This creates an organization that can adapt to any major technological shift, whether the internet, mobile, cloud, or AI. The specific technology is temporary; the capacity to learn is permanent.

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performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.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