Why LLMs aren’t solving job matching — they’re just scaling it

I was recently interviewed by Gary Fowler of GSD Venture Studios for his very popular video podcast, Top Global Startups. We dove into a topic close to my heart: how large language models (LLMs) are changing—but not revolutionizing—the world of job matching. Too often, we hear that AI will fix all hiring pains. The truth? These models may scale matching efforts, but they still stumble when it comes to genuine alignment between people and roles.

First, I set the stage by acknowledging the sheer power of LLMs. They’re phenomenal at pattern recognition, which is great for digesting piles of resumes, parsing job descriptions, and even crafting personalized outreach. But there’s a catch: without deep domain context or nuance, these models risk reinforcing shallow matches as they tend to key in on buzzwords instead of core motivations.

Then I pivot to the human element. I argue that job matching is about more than keywords. It’s about chemistry, culture, and long‑term potential. I share examples of startups that layer on behavioral data, values alignment surveys, or structured interviews post‑LLM screening. They’re not replacing the old guard—they’re scaling it smartly.

Finally, I explore what I call “responsible scaling.” It’s one thing to automate outreach at massive volume. It’s another to ensure fairness, filter bias, and maintain candidate experience. I showcase early efforts: bias‑audited models, human‑in‑the‑loop systems, and feedback loops that continually retrain the matching engine.

Through it all, I maintain that LLMs are a powerful tool in our recruiter toolkit but they’re not the answer in themselves. The future of smart hiring lies in combining scalable tech with thoughtful human processes. That intersection is what lifts matching from “good” to “exceptional.”

If you’re building or investing in AI‑powered talent platforms, ask yourself: Are you just amplifying volume? Or are you truly deepening alignment?