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Begin New Search Topic Governance Author Bertin Martens

1. Why artificial intelligence is creating fundamental challenges for competition policy

Author:
Bertin Martens
Publication Date:
07-2024
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
The rapidly evolving market for artificial-intelligence services is apparently thriving and very competitive, with a growing number of AI start-ups and ever larger and more capable AI models. Investors are pouring large amounts of money into start-ups and into a few big tech firms that have the large financial resources and computing capacity that constitute key inputs to the production and running of AI-driven services. However, exponentially growing AI model training costs create a market entry barrier for AI start-ups, forcing them to cooperate with big tech firms to get access to computing infrastructure and end users. At the same time, their AI models compete with those of the big tech firms. Competition authorities investigate these co-opetition agreements, looking for market-distorting clauses. Beyond computing costs, other competition choke points in the AI supply chain include access to dedicated AI processor chips and training data. Strict enforcement of copyright on data may further tighten an already scarce supply of affordable training data. Policymakers have few, if any, effective tools to deal with these potential competition bottlenecks in AI industries. Co-opetition agreements are necessary to enable AI start-ups to access hyperscale and costly computing infrastructure. At the same time, the agreements give big tech privileged access to the latest AI models. Competition authorities can try to minimise market-distorting provisions in these agreements but there are technical and economic limits to the extent of fragmentation in the AI value chain that can be meaningfully imposed on big tech firms. Licensing fees for copyright-protected data further tighten the already scarce supply of affordable data. It is hard to implement a fair market-clearing mechanism for dedicated AI processors. If model training costs continue to grow exponentially, as appears to be the case for the foreseeable future, the entire competition policy setting for AI industries may need a revision, with collaboration, including between big tech firms, dominating competition to keep the pace of AI innovation going.
Topic:
Science and Technology, Governance, European Union, Microeconomics, and Artificial Intelligence
Political Geography:
Europe

2. What should be done about Google’s quasi-monopoly in search? Mandatory data sharing versus AI-driven technological competition

Author:
Bertin Martens
Publication Date:
07-2023
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
The first part of this paper focuses on competition between search engines that match user queries with webpages. User welfare, as measured by click-through rates on top-ranked pages, increases when network effects attract more users and generate economies of scale in data aggregation. However, network effects trigger welfare concerns when a search engine reaches a dominant market position. The EU Digital Markets Act (DMA) imposes asymmetric data sharing obligations on very large search engines to facilitate competition from smaller competitors. We conclude from the available empirical literature on search-engine efficiency that asymmetric data sharing may increase competition but may also reduce scale and user welfare, depending on the slope of the search-data learning curve. We propose policy recommendations to reduce tension between competition and welfare, including (a) symmetric data sharing between all search engines irrespective of size, and (b) facilitate user real-time search history and profile-data portability to competing search engines. The second part of the paper focuses on the impact of recent generative AI models, such as Large Language Models (LLMs), chatbots and answer engines, on competition in search markets. LLMs are pre-trained on very large text datasets, prior to usage. They do not depend on user-driven network effects. That avoids winner-takes-all markets. However, high fixed algorithmic learning costs and input markets bottlenecks (webpage indexes, copyright-protected data and hyperscale cloud infrastructure) make entry more difficult. LLMs produce semantic responses (rather than web pages) in response to a query. That reduces cognitive processing costs for users but may also increase ex-post uncertainty about the quality of the output. User responses to this trade-off will determine the degree of substitution or complementarity between search and chatbots. We conclude that, under certain conditions, a competitive chatbot markets could crowd out a monopolistic search engine market and may make DMA-style regulatory intervention in search engines redundant.
Topic:
Science and Technology, Governance, European Union, Digital Economy, Innovation, Artificial Intelligence, and Competition
Political Geography:
Europe

3. Pro- and anti-competitive provisions in the proposed European Union Data Act

Author:
Bertin Martens
Publication Date:
01-2023
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
The proposed European Union Data Act gives users access and portability rights to the data generated by their use of tangible digital products and devices. This pro-competitive measure weakens the de-facto monopolistic control product manufacturers have over product data. However, the Data Act would also grant manufacturers a de-jure right to monopolistic pricing of data transfers to third-party service providers, restoring their control over data markets. Other anti-competitive provisions include restrictions on the use of data for competition purposes and a prohibition on transfering data to platforms identified as gatekeepers under the EU Digital Markets Act. Unnecessarily fuzzy definitions of products and data that fall under the Data Act would create uncertainty in implementation and incoherence with other EU data regulations. The Data Act should be simplified by dropping anti-competitive provisions and granting users and third-parties selected by users free access to all data generated by the use of a product or a service. This would increase competition in data-driven services and prevent users paying twice for their data. It would not disincentivise producers from investing in data-driven products and services. A further step could be the creation of a level playing field between producers and users in data-access rights. This can be achieved by introducing the principle of mutual exhaustion of data rights at point of sale. This would put all data co-generating parties in a position to generate economies of scale and scope in data aggregation, or data-driven externalities, to maximise innovation.
Topic:
Markets, Governance, European Union, and Digital Economy
Political Geography:
Europe