Netflix replaced their 15 years old recommendation algorithm with an LLM. It’s called "GenRec" and it completely changes how recommendation algorithms are built. For over a decade, the Netflix recommendation engine was a masterclass in feature engineering. Data scientists built thousands of complex, handcrafted features to figure out what you wanted to watch next. It required bespoke architectures. Massive infrastructure. Constant manual tuning. Netflix threw all of it away. They built GenRec, an LLM-backed ranker. Instead of translating your behavior into complex math, they just turn your watch history and metadata into a natural language sentence. They feed that raw text into a foundation LLM. The AI simply reads your behavior like a story, understands your evolving tastes, and scores the entire catalog in a single forward pass. No manual feature engineering. No complex bespoke architectures. Here is the part that should terrify traditional data scientists. This text-based LLM didn't just match the highly tuned production system Netflix spent years perfecting. It beat it. And it achieved those statistically significant gains using roughly 40x fewer labeled training examples. We are watching a massive paradigm shift in real time. The most complex predictive algorithms in the world are being replaced by models that just know how to read. If Netflix can replace their core product engine with an LLM, what complex system in your business is about to become obsolete?