AI has made knowledge—the ability to produce information—cheap and accessible. The new currency is wisdom: knowing what matters, where to focus, and how to find purpose. This shifts the focus of work and education from learning facts to developing critical thinking, empathy, and judgment.

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.

With AI automating routine coding, the value of junior developers as inexpensive labor for simple tasks is diminishing. Companies will now hire juniors based on their creative problem-solving abilities and learning mindset, as they transition from being 'coders' to 'problem solvers who talk to computers.'

As AI handles technical tasks, uniquely human skills like curiosity, empathy, and judgment become paramount. Leaders must adapt their hiring processes to screen for these non-replicable soft skills, which are becoming more valuable than traditional marketing competencies.

Instead of choosing a career based on its perceived "safety" from AI, individuals should pursue their passions to quickly become domain experts. AI tools augment this expertise, increasing the value of experienced professionals who can handle complex, nuanced situations that AI cannot.

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.

The long-standing career advice to pursue computer science is no longer universally applicable. As AI tools increasingly automate software development, coding is becoming a 'solved problem.' The most valuable skills for the next generation will be creativity, design, and business problem-solving, rather than deep engineering expertise.

At the start of a tech cycle, the few people with deep, practical experience often don't fit traditional molds (e.g., top CS degrees). Companies must look beyond standard credentials to find this scarce talent, much like early mobile experts who weren't always "cracked" competitive coders.

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.