Continual Learning: The Last Unsolved Problem in AI

AI agents obey text they read because instructions and data share one channel. Why every filter gets beaten, and the designs that contain the damage.

Pretrained models now predict on your spreadsheet with no training. Where they beat tuned XGBoost, where they still lose, and what the licenses let you ship.

AI systems can reason through anything but cannot remember the solutions. Why catastrophic forgetting is now a production problem, not just a research one.

LLM inference costs dropped 600x since 2023. So why are AI companies losing money faster than ever? The economics nobody wants to talk about.

RAG pipelines fail in production for structural reasons. We break down the five failure modes and what Agentic RAG, Self-RAG, and GraphRAG actually fix.

Training a frontier model costs millions. Distilling a capable student from it costs thousands. Knowledge distillation is quietly reorganizing the economics of the entire AI industry.

A language model can describe a falling glass. It cannot predict where the water goes. World models close the gap between AI that talks about reality and AI that can act in it.

MMLU is saturated. Chatbot Arena is gameable. Public benchmarks leak into training data. The only eval that matters is the one you build yourself, on your data, for your task.

A 30B parameter model runs on a MacBook because only 3B parameters fire per token. Mixture of Experts splits memory cost from compute cost, and that changes everything about where AI can run.

90% of enterprises deploy AI agents. Only 23% scale them. The gap is interoperability. Three protocols, MCP, A2A, and ACP, are racing to build the connective tissue before the ecosystem fragments.