To move beyond general knowledge, AI firms are creating a new role: the "AI Trainer." These are not contractors but full-time employees, typically PhDs with deep domain expertise and a computer science interest, tasked with systematically improving model competence in specific fields like physics or mathematics.

AI tools are so novel they neutralize the advantage of long-term experience. A junior designer who is curious and quick to adopt AI workflows can outperform a veteran who is slower to adapt, creating a major career reset based on agency, not tenure.

Instead of viewing AI with a fear-based scarcity mindset (e.g., "How will this replace me?"), adopt an abundance approach. Ask how AI can augment your skills and make you better at your job. Professionals who master using AI as a tool will become more, not less, valuable in the marketplace.

The most effective career strategy for employees facing automation is not resistance, but mastery. By learning to operate, manage, and improve the very AI systems that threaten their roles, individuals can secure their positions and become indispensable experts who manage the machines.

If AI were perfect, it would simply replace tasks. Because it is imperfect and requires nuanced interaction, it creates demand for skilled professionals who can prompt, verify, and creatively apply it. This turns AI's limitations into a tool that requires and rewards human proficiency.

Instead of fearing job loss, focus on skills in industries with elastic demand. When AI makes workers 10x more productive in these fields (e.g., software), the market will demand 100x more output, increasing the need for skilled humans who can leverage AI.

AI coding assistants won't make fundamental skills obsolete. Instead, they act as a force multiplier that separates engineers. Great engineers use AI to become exceptional by augmenting their deep understanding, while mediocre engineers who rely on it blindly will fall further behind.

Experience alone no longer determines engineering productivity. An engineer's value is now a function of their experience plus their fluency with AI tools. Experienced coders who haven't adapted are now less valuable than AI-native recent graduates, who are in high demand.