I've been in AI for over a decade.

Every 2-3 years, the dominant technology changes completely. SVMs to deep learning to transformers to LLMs to agents.

The engineers who survive these transitions share common traits. The ones who don't share different ones.

What makes an AI career last:

Strong fundamentals. Math, statistics, algorithms, and system design don't go out of style. Every paradigm shift still uses linear algebra, probability, and good software engineering.

Adaptability without panic. When transformers replaced RNNs, some engineers panicked and jumped ship. Others spent a weekend implementing a transformer and kept building. Guess which group thrived?

T-shaped expertise. Deep in one area, broad awareness of everything else. When your deep area becomes less relevant, your breadth helps you pivot quickly.

Building real systems, not just models. Production engineering skills transfer across any paradigm. Docker doesn't care what model you're deploying.

Continuous learning as a HABIT, not an event. Reading papers, building side projects, engaging with the community — weekly, not when you feel behind.

What doesn't last: skills tied to a single framework or model. Expertise in only one narrow area with no breadth. Resistance to change. Defining yourself by the tools you use rather than the problems you solve.

The AI engineer who identifies as "an LLM engineer" is fragile. The one who identifies as "someone who builds intelligent systems" is antifragile.

Build a career that survives the next paradigm shift. Because it's coming. It always is.

Invest in fundamentals. Stay curious. Keep building. That's the only career strategy that's worked across every era of AI. And it always will.