A data flywheel is the compounding loop in which product usage generates data that improves the product, which attracts more usage: user corrections refine mode
The compounding loop in which product usage generates data that improves the product, which attracts more usage: user corrections refine models, behavior signals tune retrieval, outcomes train better automation. It is the most cited moat in AI strategy.
Because a real flywheel requires instrumented feedback capture, the rights to use the data, and engineering that actually closes the loop. Most claimed flywheels are just logs: data collected but never fed back into measurable improvement.