Asymptotic network effects are network effects with diminishing returns.

Recall the basic definition of network effects: as usage of a product grows, its value to each user also grows. In some cases, however, network effects can start to weaken after certain point in the growth of the network. Growth in an asymptotic network, after a certain size, no longer benefits the existing users.

Uber is one example of this, since after about a 4-minute wait time, Uber passengers no longer benefit much from an increase in the number of drivers. The value of more supply "asymptotes" as the growth in value approaches zero for the demand side.

Many data network effects, for example, are asymptotic. After the dataset reaches a certain size, the algorithm no longer meaningfully improves as the dataset grows. Most data network effects suffer from this property. Businesses like Waze do the best job of avoiding this, because that service requires real-time data that must be continually updated by thousands or even tens of thousands of nodes to be minimally useful.