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1. The tension between exploding AI investment costs and slow productivity growth

Author:
Bertin Martens
Publication Date:
10-2024
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
This working paper explores the tension between rapidly increasing artificial intelligence investment costs and the slower pace of productivity growth, raising concerns about a potential ‘economic winter’ for AI. AI has shown significant technological progress, particularly with machine learning and generative AI models like ChatGPT. Investment in AI has surged. But there are concerns about whether these investments will yield proportional returns. Training costs for a single frontier AI model are increasing exponentially, , from $1,000 in 2017 to nearly $200 million in 2024, driven by constant returns to scale in AI model training data, compute capacity and model complexity. Costs could reach billions of dollars by 2030, despite a rapid fall in unit costs per computing operation over the same period. Global AI infrastructure costs in hardware could exceed $1 trillion by the mid-2030s. Amortizing these huge fixed costs requires business models that can be rolled out across a very large user market. Estimates about AI’s contribution to productivity growth vary, from a modest 0.5 percent per year to a very optimistic 10 percent per year. Research shows that productivity usually catches up slowly compared to costs. Without significant productivity gains, the current investment cost trajectory is unsustainable. We estimate that 3 percent annual productivity growth across advanced economies would be required to sustain AI model cost extrapolations to 2030. In an optimistic scenario, productivity increases would result in accelerated economic growth.
Topic:
Digital Economy, Economic Growth, Microeconomics, Artificial Intelligence, and Productivity
Political Geography:
Global Focus

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