Competition and Antitrust Concerns Related to Generative AI

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US Congress·congress.gov/crs-product/IF12968

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A CRS report providing U.S. Congressional staff with background on antitrust and competition policy issues in generative AI; useful for understanding the regulatory landscape and legislative debates around AI market concentration and compute access.

Metadata

Importance: 52/100policy briefanalysis

Summary

This Congressional Research Service report analyzes how the high computational costs of developing large language models may create barriers to entry that favor well-resourced tech giants, raising antitrust concerns. It examines the dual roles of major companies like Amazon, Google, Meta, and Microsoft as both AI infrastructure owners and model developers, and presents legislative considerations for Congress on promoting competition in the generative AI market.

Key Points

•High computational costs for training large language models create significant barriers to entry, potentially entrenching dominant tech companies.
•Major U.S. tech firms controlling IT infrastructure also develop competing GenAI models, raising potential anticompetitive conduct concerns.
•Congress faces questions about whether new legislation is needed to promote competition or whether existing antitrust frameworks are sufficient.
•Some stakeholders argue the GenAI market remains competitive across its development stack, complicating calls for regulatory intervention.
•Report frames AI competition policy within the broader goal of maintaining American leadership in generative AI development.

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# Competition and Antitrust Concerns Related to Generative AI

## CRS PRODUCT (LIBRARY OF CONGRESS)

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| CRS Product Type: | In Focus |
| CRS Product Number: | IF12968 |
| Referenced Legislation: | [H.R. 2385](https://www.congress.gov/bill/119th-congress/house-bill/2385) |
| Topics: | Commerce & Small Business; Science & Technology |
| Publication Date: | 04/16/2025 |
| Authors: | Cho, Clare Y.; Harris, Laurie; Zhu, Ling |

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Competition and Antitrust Concerns Related to Generative AI

April 16, 2025
(IF12968)


Artificial intelligence (AI) technologies used to generate synthetic content, such as text, images, audio, video, and computer code, are broadly referred to as _generative_ _AI_ ( [GenAI](https://csrc.nist.gov/glossary/term/generative_artificial_intelligence)). One type of GenAI is a [large language model](https://www.sap.com/resources/what-is-large-language-model) (LLM), which can generate responses to prompts in natural language format once it has been trained on a massive amount of text (e.g., text from millions of web pages) and defined by billions of model parameters.

Developing and operating a large-scale GenAI model may require significant [computing resources](https://ainowinstitute.org/publication/policy/compute-and-ai#h-what-is-compute-and-why-does-it-matter), such as hardware, software, and other [information technology (IT) infrastructure](https://www.congress.gov/crs-product/IF12899), which can be costly. This has raised concerns about who may be able to develop such models, whether companies with substantial resources may have a competitive advantage over smaller competitors and start-ups, and whether some companies that own IT infrastructure might be engaging in anticompetitive conduct. Some stakeholders have [argued that competition](https://docs.house.gov/Committee/Calendar/ByEvent.aspx?EventID=118081) is thriving in each component of GenAI development.

Some U.S. companies—including Amazon, Google, Meta, and Microsoft—own IT infrastructure used to train and deploy GenAI models and also invest billions of dollars to develop their own models. Other companies are exploring methods to [reduce computing power](https://ainowinstitute.org/publication/policy/compute-and-ai#h-how-can-demand-for-compute-be-addressed) needed to develop AI models. For 

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