The most potent productivity gains from AI aren't just for junior staff. Seasoned professionals who combine deep domain expertise with adaptability are using AI to rapidly learn adjacent skills like design or marketing. This allows them to "collapse the skill stack" and single-handedly perform tasks that previously required multiple people.

In a rapidly changing technology landscape, professionals must act as the "dean of their own education." This involves a disciplined, continuous process of learning and skill acquisition, essentially building a new foundation for your career every four to five years.

Ken Griffin advises that graduation marks the beginning, not the end, of education. He argues the most important skill is learning how to learn, as professionals will need to develop entirely new toolkits multiple times over a 40-50 year career to remain relevant amidst technological change and increased longevity.

Focusing exclusively on one industry makes you an expert in a silo but blind to broader market shifts and innovations from other sectors. This intellectual laziness limits your ability to bring fresh perspectives to clients, making you less valuable and more replaceable than a well-rounded expert who can cross-pollinate ideas.

Product management "range" is developed not by learning domain-specific facts, but by recognizing universal human behaviors that transcend industries—the desire for simplicity, convenience, or saving time. Working across different verticals hones this pattern-matching skill, which is more valuable than deep expertise in a world of accessible information.

AI reverses the long-standing trend of professional hyper-specialization. By providing instant access to specialist knowledge (e.g., coding in an unfamiliar language), AI tools empower individuals to operate as effective generalists. This allows small, agile teams to achieve more without hiring a dedicated expert for every function.

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performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.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

The goal for your 20s is a two-step process. First, earn money by trading your time. Then, use that money to go deep on one high-value "meta-skill" (like sales or coding) that makes learning other skills easier. Avoid diversification and focus intensely on mastering that one thing.