Continual Learning: Why Your Deployed LLM Still Can't Learn
Continual learning is the AI research goal of models that keep absorbing new knowledge after deployment without forgetting old skills — but no production LLM actually does this today. Frontier models like Claude Opus 4.8 and GPT-5.5 ship with fixed knowledge cutoffs and stay frozen until the next full retrain, months later; what looks like "learning" in products is really context windows, retrieval (RAG), and memory features re-injecting facts, not weight updates. The gap between the research narrative and shipped reality is widening as vendors add memory features that feel like learning but aren't. For PMs, this matters because any roadmap that assumes the model will organically get smarter about your users is building on a substitute, not the real thing.