In the mid-2010s, as computing and other digital technologies matured (Brynjolfsson and McAffee 2014), researchers began to speculate about a new era of innovation—with artificial intelligence (AI) as the standard-bearer of a “Fourth Industrial Revolution” (Schwab 2015). The release of generative AI (Gen-AI) technologies (e.g., ChatGPT) in late 2022 reignited the discussion, prompting us to wonder: what are the barriers, risks, and potential rewards to using gen-AI for design and manufacturing? As Gen-AI has entered the mainstream, geopolitics and business practices have shifted. Covid-19 disrupted global supply chains, tensions with import partners have risen, and military conflicts introduce new uncertainties. As companies consider propositions like ‘reshoring’ or ‘nearshoring/friendshoring’ production (Yellen 2023), we recognize other hindrances: suboptimal resource allocation, labor market volatility and trends toward an older and geographically mismatched workforce, and highly concentrated tech markets that foster anticompetitive business practices. As the United States expands domestic production capacity (e.g., semiconductors and electric vehicles), Gen-AI could help us overcome those challenges. To investigate the current and potential usefulness of Gen-AI in design and manufacturing, we interviewed industry experts—including engineers, manufacturers, tech executives, and entrepreneurs. They have identified many opportunities for the deployment of Gen-AI: (1) reducing the incidence of costly late-stage design changes when scaling production; (2) providing information to designers and engineers, including identifying suitable design spaces and material formulations and incorporating consumer preferences; (3) improving test data interpretation to enable rapid validation and qualification; (4) democratizing workers’ access and usage of data to enable real-time insights and process adjustment; and (5) empowering less-skilled workers to be more productive and do more-expert work. Current Gen-AI solutions (e.g., ChatGPT, Claude) cannot accomplish these goals due to several key deficiencies, including the inability to provide robust, reliable, and replicable output; lack of relevant domain knowledge; unawareness of industry-standards requirements for product quality; failure to integrate seamlessly with existing workflow; and inability to simultaneously interpret data from different sources and formats. We propose a development framework for the next generation of Gen-AI tools for design and manufacturing (“NextGen-AI”): (1) provide better information about engineering tools, repositories, search methods, and other resources to augment the creative process of design; (2) integrate adherence to first principles when solving engineering problems; (3) leverage employees’ experiential knowledge to improve training and performance; (4) empower workers to perform new and more-expert productive tasks rather than pursue static automation of workers’ current functions; (5) create a collaborative and secure data ecosystem to train foundation models; and (6) ensure that new tools are safe and effective. These goals are extensive and will require broad-based buy-in from business leaders, operators, researchers, engineers, and policymakers. We recommend the following priorities to enable useful AI for design and manufacturing: (1) improve systems integration to ethically collect real-time data, (2) regulate data governance to ensure equal opportunity in development and ownership, (3) expand the collection of worker-safety data to assess industry-wide AI usage, (4) include engineers and operators in the development and uptake of new tools, and (5) focus on skills-complementary deployments to maximize productivity upside.