By Staff in the Bureau of Competition & Office of Technology
Generative AI has the potential to rapidly transform the way we live, work, and interact. Within just a few months, generative AI chatbots and applications have launched and scaled across industries and reached hundreds of millions of people. AI is increasingly becoming a basic part of daily life.
Generative AI depends on a set of necessary inputs. If a single company or a handful of firms control one or several of these essential inputs, they may be able to leverage their control to dampen or distort competition in generative AI markets. And if generative AI itself becomes an increasingly critical tool, then those who control its essential inputs could wield outsized influence over a significant swath of economic activity.
The FTC’s Bureau of Competition, working closely with the Office of Technology, is focused on ensuring open and fair competition, including at key inflection points as technologies develop. Generative AI represents one of these paradigm shifts. Accordingly, it is especially important that firms not engage in unfair methods of competition or other antitrust violations to squash competition and undermine the potential far-reaching benefits of this transformative technology. Unfair methods of competition can distort the rate and direction of innovation. By contrast, open and competitive markets can pave the way for emerging technologies, such as generative AI, to yield their maximum potential benefit.
This blog post identifies a few of the essential technical building blocks of generative AI and discusses competition concerns potentially raised by generative AI.
What Is Generative AI?
“Generative AI” is a category of AI that empowers machines to generate new content rather than simply analyze or manipulate existing data. By using models trained on vast amounts of data, generative AI can generate content—such as text, photos, audio, or video—that is sometimes indistinguishable from content crafted directly by humans. Large language models (LLMs), which power chatbots and other text-based AI tools, represent one common type of generative AI.
Many generative AI models are developed using a multi-step process: a pre-training step, a fine-tuning step, and potential customization steps. These steps may all be performed by the same company, or each step may be performed by a different company. The pre-training step creates a base model with broad competency in a specific domain, such as language or images. For example, a pre-trained language model might take a partial sentence such as “the family brought their pet goat to the…” and generate potential “autocomplete” suggestions like “park,” “vet,” or “farm.” After pre-training, the model is fine-tuned for a specific application, such as responding to questions or generating images from prompts. In a chatbot interface, a user may ask: “What are some good places to bring your pet goat?”Finally, some types of generative AI can be further customized via methods specific to certain types of models, such as prompt engineering. Prompt engineering is used by many chatbot developers to add more constraints—directions to not respond to inappropriate or harmful questions—or to imitate behaviors.
