The AI Bottleneck: It's Not Intelligence, It's Context Engineering

While large language models (LLMs) are achieving remarkable feats in mathematics, even matching International Mathematical Olympiad gold medalists, their performance in everyday enterprise applications lags significantly. The article argues that the bottleneck isn't the models' intelligence, but rather the specification of tasks and context engineering. Mathematical problems have clear specifications, while real-world tasks are fuzzy and full of implicit constraints. Improving AI hinges on building better context engines and task specifications, requiring breakthroughs in data acquisition, model training, and continuous learning. In the short term, AI will yield astounding results in science; long-term, broad corporate automation still faces the challenge of overcoming the specification and context engineering hurdles.