Competition between ecosystems is a natural part of competition in the digital network industry. Whether this competition is healthy depends on the number of different ecosystem operators and the sizes of their ecosystems. This chapter explains that there are different stages of ecosystem competition that can be identified. In stage 1 ecosystem competition, firms compete to capture a platform market. These firms do not yet have a vast ecosystem of products and must compete intensely to capture an userbase. The winner that captures the platform market can enter into stage 2 ecosystem competition, where it expands across markets to solidify its position. Once an ecosystem is sufficiently large it enters into stage 3 ecosystem competition; maturity. Here, the ecosystem operator can start to extract profits. When ecosystems grow too large, they become stage 4 incumbent ecosystems. They no longer experience competitive pressures and become able to abuse their ecosystem power to extract excessive rents.

Market concentration is a significant and growing problem in precisely the digital markets where the Internet was supposed to herald an era of healthy competition. Algorithmic systems are subject to a significant new force shifting market competition, the “feedback effect.” More data not only produces better results through traditional scale and scope economies, but also by generating better machine learning models. This means that traditional antitrust and regulatory remedies are poorly suited to redress competitive imbalances. Instead, regulators should impose a progressive data-sharing mandate. With this novel mechanism, dominant digital platforms would be required to make data available to competitors, blunting their inherent advantage in algorithm-dominated markets.