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Jeff Dean says context matters more than which AI model you pick
Publication date: 08.08.2026
"Trillions of tokens stirred together into a soup" — that's how Jeff Dean describes a model's training data, contrasting it with the context for a specific task, which is far clearer and sharper. Google's former Chief Scientist, who helped build its AI from the ground up, made the core point at a Y Combinator interview with Diana Hu: which model you pick is secondary; what matters more is how you build the system around it.
Dean calls this context engineering. The model, he says, is "really only one piece" of a larger system. What shapes the result far more is tooling, access to information retrieval, multi-agent orchestration, and clear context relevant to your specific problem. His full argument is laid out by Search Engine Journal.
What Dean advises:
- learn from failure — watch where the model stumbles on real tasks;
- adjust guidelines and skills, not parameters: improve the context instead of retraining;
- iterate — every failure shows what information the model was missing.
The key upside is accessibility. Training a model takes massive GPU resources, while context engineering is within reach of anyone with an API and their own retrieval-and-tools setup. For SEO and content teams that's a shift in focus: what counts is clear facts, clean sources, and thoughtful presentation — the kind of material a model can actually find and use correctly. Which neural network ends up answering the user is turning into an increasingly beside-the-point question.
