Don't Be Everything to Everyone: The Case for Career and Model Specialization (Small Language Models)

Abstract

## Abstract **Don't Be Everything to Everyone: The Case for Career and Model Specialization (Small Language Models)** We’ve all heard the advice: “Be versatile, keep your options open.” But here’s the twist — whether you’re building a career or building a language model, trying to be everything to everyone is a recipe for burnout and bloat. The real magic happens when you specialize. This keynote makes parallels between professional growth and AI design: - 🎯 **Sovereignty of your stack**: Just like owning your career path, owning your open-source tools gives you freedom, resilience, and a little swagger. - 🔄 **Small iterations, big wins**: Careers aren’t built in one giant leap, and neither are models. Iteration is the secret sauce that compounds into mastery. - ⚡ **Efficiency through expertise**: Specialists — human or machine — don’t waste energy chasing every problem. They focus, optimize, and deliver outsized impact. Think of it as a pep talk for both your résumé and your repo. By the end, you’ll see why the future belongs to those who go deep, not wide — and why small, sharp models (and careers) punch way above their weight.

Speakers