AI is transforming the lives of Americans, regardless of geography, occupation, or political affiliation. A January 2025 Gallup survey noted that 99% of American adults reported using at least one AI product in the past week, and 83% reported using at least four AI products in the past week. These products range from virtual assistants (e.g., Siri and Alexa) to smartphone weather forecasting apps. As such, it is important for policymakers and stakeholders to be updated on relevant AI concepts and legislative developments.

Section 2.a: Definitions and Key Concepts

Artificial intelligence refers to “the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making.” In short, machines that think like humans. AI usage is on the rise, ranging from Microsoft Word’s grammar autocorrect function to complex image and video generation by Google’s Veo 3 and OpenAI’s Sora 2 software. Thus, it is important for policymakers to understand some of the terminology that is used to discuss AI-related topics (Congressional Research Service).

• Machine Learning: “uses algorithms to enable systems to identify and learn from patterns or relationships in data without being explicitly programmed. The performance of these systems can improve as they learn from more data to make predictions or decisions on new, unseen data.”
• Deep Learning: “uses neural network (NN) techniques to process and analyze large-scale, complex data, including text, images, and audio. NNs were originally inspired by how layers of neurons in the human brain signal each other, with artificial neurons grouped into layers of interconnected nodes.”
• Generative Al (GenAl): “Al systems that can generate content-such as written material, audio, images, or computer code-from prompts using advanced techniques such as NNs that help the underlying models better understand how data elements influence and depend on one another.”
• Foundation Models (FMs): “GenAl models trained on large amounts of diverse types of data that can be fine-tuned for a wide range of downstream tasks. Large language models are one category of FMs designed to learn from and generate text for applications such as language translation and question answering.”
• Data Centers: These large facilities house, manage, and run IT infrastructure, including servers, storage devices, and network equipment. Data centers play a critical role in processing, storing, and distributing large amounts of data, making them essential to gen AI and the larger digital economy.

Furthermore, it is important to differentiate between generative AI (GenAI) and analytical AI throughout this report. GenAI processes use pre-existing media (e.g., images, audio, video, code, and text) to produce similar content. Google’s Veo 3 software falls into this category. Analytical AI, on the other hand, involves using machine learning, natural language processing and data mining techniques to interpret data, and make predictions. For example, analytical AI models are currently being used to examine human medical images (MRIs) to detect signs of cancer. By understanding distinct AI capabilities, state leaders can tailor legislation to embrace the benefits of AI implementation while protecting their constituents.

Section 2.b: Why Does AI Matter? And Public Perceptions of AI

Constituents’ perceptions of AI are heavily divided, as indicated by Pew findings from Apr. 3 revealing that AI experts are more positive about the impact of AI than the public. Similarly, more recent Pew findings from Sep. 17 show that the public is overall more concerned rather than excited about the increased usage of AI in everyday life, yet at the same time recognizes the benefits of AI in areas such as heavy data analysis for weather forecasting. Analysis from Gallup surfaces concerns from the public about AI negatively reinforcing trends ranging from reduced employment opportunities to the spread of false information. Also, recent YouGov data reveals similar findings:

On the one hand, these polls reveal stark contrasts in public perceptions of AI. Yet, these polls show areas of common ground such as a desire to regulate AI and the recognition that AI has strong benefits regarding medical diagnoses and treatments. These overlapping areas might yield opportunities for lawmakers and state residents to come together to advance AI legislation.

Section 2.c: Recent AI Legislation at the Federal and State Level

Since the start of 2025, states and territories have introduced 252 bills that support AI innovation and development. This points to AI being a primary concern for state and territory legislators regardless of political affiliation or geography. There are different trends and themes that emerge from AI legislation, including AI usage in healthcare, higher education research, judicial legal review, data sharing, manufacturing, and cybersecurity.

In addition, 19 state legislative bills related to AI innovation and development have been enacted since 2022, when ChatGPT and other GenAI models became popularized. Notable examples include Texas S.B. 1964 (2025), which creates a regulatory structure for transparent AI use by state government entities; Louisiana H.R. 320 (2025), which promotes AI professional development in state education agencies and institutions; and Georgia S.R. 391 (2025), which establishes a Senate Study Committee on AI and Digital Currency.