Because the performance of deep learning
algorithms depends critically on the training data that they are created from, it may be possible,
in a particular application area, for a specific company (either an incumbent or start-up) gain a
significant, persistent innovation advantage through their control over data that is independent of
traditional economies of scale or demand-side network effects. This “competition for the
market” is likely to have several consequences. First, it creates incentives for duplicative racing
to establish a data advantage in particular application sectors (say, search, autonomous driving,
or cytology) followed by the establishment of durable barriers to entry that may be of significant
concern for competition policy.
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