Reps. Jay Obernolte, R-Calif., and Lori Trahan, D-Mass., rolled out a draft\u00a0measure Thursday that would set a nearly all-encompassing framework for granting the U.S. government regulatory control over various aspects of artificial intelligence while still prioritizing technological innovation and adoption \u2014 beginning with allowing federal preemption of state regulation for a three-year period.\u00a0<\/p>\n
The discussion draft of the\u00a0Great American Artificial Intelligence Act of 2026<\/a> looks to create\u00a0four pillars for AI advancement: establishing frontier artificial intelligence model governance,\u00a0collecting insight into changes within the U.S. workforce landscape, fortifying cybersecurity postures and spurring new AI research and development.<\/p>\n In governing frontier AI models, the proposal seeks to codify the existence of the Center for AI Standards and Innovation, an office that was initially established under the Biden administration as the AI Safety Institute and was rebranded by the Department of Commerce in 2025<\/a>.<\/p>\n The measure looks to task\u00a0that office with spearheading development of standards and voluntary guidance for AI models, as well as studying and mitigating national security risks, building on Trump administration efforts<\/a> to partner with industry in allowing government evaluations of AI models. A director of CAISI would be appointed by the Commerce secretary.\u00a0<\/p>\n Under the draft legislation, CAISI would also oversee other efforts, such as monitoring foreign competitors and setting up a new licensing regime to designate independent verification organizations, which are defined as entities that conduct audits of frontier model developers’ compliance with transparency.\u00a0<\/p>\n The measure would grant\u00a0CAISI $100 million in annual federal funding for 2027 to 2029.<\/p>\n Frontier model developers would also be tasked with maintaining transparency in how they monitor their AI systems.<\/p>\n The proposal stipulates that large AI model developers craft\u00a0and implement an AI framework that will apply to all their models, showcase standards compliance efforts, identify risk thresholds,\u00a0determine whether a model poses \u201ca catastrophic risk\u201d when\u00a0managing cybersecurity defenses \u2013\u2013 particularly in private model weights \u2013\u2013 and also disclose release dates. This information would have to be publicly available, as would any modifications to companies\u2019 governance frameworks.\u00a0<\/p>\n Large frontier model developers would also be required to retain a qualified independent verification organization to confirm compliance with their individual frameworks. As independent entities, those verification organizations will be scrutinized to ensure that there are no ties to the companies they monitor. State attorneys general\u00a0are also permitted to receive audit reports from those organizations upon request.<\/p>\n Understanding how AI is impacting the workforce is a paramount component of the draft bill. At an educational level, it prioritizes incorporating fundamental AI literacy into various curricula, including at the K-12 level and in institutions of higher education.<\/p>\n The National Science Foundation would lead the charge in supporting more AI-focused education efforts, using vehicles like awards and grants to expand access for students and educators to learn more about AI. Notably, the bill asks NSF to create eight Centers of AI Excellence with help from Commerce\u2019s Regional Technology and Innovation Hub Program.\u00a0<\/p>\n Aside from education, the\u00a0proposal’s authors also seek to understand how AI is impacting the jobs market. It directs the Department of Labor to supply clear statistics on changes in the labor market and AI workforce landscape via a new Artificial Intelligence Workforce Research Hub.\u00a0<\/p>\n The Labor secretary would also be tasked with publishing a request for comment on how to best implement the data collection and forecasting analytics pursuant to the bill\u2019s requirements. An initial expert workshop comprised of economists, AI technical experts, industry participants, labor organizations and government officials would be established to evaluate the Bureau of Labor Statistics’\u00a0understanding of AI\u2019s workforce impact.<\/p>\n As the ramifications of AI-driven cyberthreats have accelerated following the release of Anthropic\u2019s advanced model Mythos<\/a> and other high-performance AI tools, the bill expands existing federal cybersecurity efforts to understand the risk landscape. Prioritizing threat intelligence sharing, it would reauthorize\u00a0and extend\u00a0the Cybersecurity Information Sharing Act of 2015<\/a> until 2035 and direct\u00a0the Homeland Security secretary to develop an outreach plan targeting small or rural owners and operators of critical infrastructure to inform them of both threats and recourse options.\u00a0<\/p>\n The Cybersecurity and Infrastructure Security Agency and CAISI would work together to assist maintainers of open-source software in their defensive efforts. Under the draft\u00a0bill, maintainers \u2014 or the lead developers in charge of open-source software code \u2014 would be eligible for funding to help detect and patch outstanding vulnerabilities through controlled access to select frontier models.\u00a0<\/p>\n The final section of the proposal is focused on research and development efforts to ensure the U.S. continues leading in AI innovation. The draft\u00a0bill seeks to promote\u00a0interagency coordination to support the creation of a new testbed program with participation between the national laboratories, federal laboratories, NIST, the National AI Research Resource pilot program \u2013\u2013 or a successor program \u2013\u2013 and private sector entities.\u00a0<\/p>\n Leaders in charge of testbed coordination include the Energy secretary, the undersecretary of Commerce for Standards and Technology and the NSF director. The testbeds would conduct security risk and vulnerability assessments. Work within the testbeds would prioritize assessments by identifying security vulnerabilities of AI systems with respect to threats like autonomous offensive cyber capabilities, vulnerabilities in the AI software ecosystem, chemical and biological threats, critical infrastructure threats and more.<\/p>\n