The rapid convergence of digital platforms and generative artificial intelligence is transforming competitive dynamics and exposing the limitations of traditional competition law. This thesis analyzes how market power is established and sustained in data-driven ecosystems – through network effects, scale, ecosystem leverage, and data feedback loops – and examines whether the enforcement tools of the EU and US can preserve contestability in this dynamic environment. Through doctrinal analysis, economic theory, and comparative case studies of the United States v. Google (Search) case and the European Commission’s Ad Tech investigation, the study contrasts the EU’s regulatory frameworks (including the Digital Markets Act) with the case-by-case litigation approach in the US.
This thesis concludes that behavioral remedies and monetary penalties are often insufficient where platform architecture embeds conflicts of interest or external foreclosure, and that price-centric models risk enabling dominant companies to consolidate their advantages, especially during the critical AI transition. Therefore, the thesis advocates a recalibrated toolkit: presume structural remedies when business models create systemic conflicts of interest; emphasize remedies that target core infrastructure and the data feedback loop; mandate verifiable, real-time technical access where data sharing is ordered; and set sanctions that go beyond the economic gains from anticompetitive conduct. These measures aim to prevent algorithmic foreclosure and protect innovation and market diversity in digital markets.