POSE: Phase 1: Jaseci: An Open Source Ecosystem for Rapid Artificial Intelligence (AI) at Scale
- Institution
- Regents of the University of Michigan - Ann Arbor, MI
- Principal investigator
- Jason Mars
- Funding
- $300K
- Duration
- 2025–2026
Moonbase score
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Abstract
This Pathways to Enable Open-Source Ecosystems (POSE) project is centered on strengthening the nation’s competitiveness in artificial intelligence (AI) for development and economic growth. This project supports the growth of an open-source ecosystem around a novel AI development stack that dramatically simplifies and accelerates the creation of scalable AI applications, with the potential to speed up AI development by 10 times. By significantly reducing the technical complexity and developer expertise required to launch production-grade AI systems, this project aims to increase participation in AI innovation as well as the pool of developers capable of building next-generation technologies. Early adopters, including major enterprises in finance and technology, have demonstrated the effectiveness of the platform in accelerating product development. By lowering the barriers to AI application development, this project will foster innovation across industries, enable faster commercialization of AI technologies, and strengthen the United States’ leadership in the global AI economy. The impact extends to sectors such as finance, healthcare, education, and public services, where more rapid AI innovation will create economic opportunities and societal benefits. This Pathways to Enable Open-Source Ecosystems (POSE) project establishes a foundation for a sustainable open-source ecosystem around a novel AI development stack designed to address critical barriers in creating scalable, production-ready AI applications. The platform introduces a new programming model that simplifies the design and development of distributed, cloud-based AI software, coupled with a runtime system that automates orchestration, microservice configuration, database management, and deployment optimization. The effort will focus on systematically discovering and growing the open-source ecosystem by identifying user needs, engaging early adopters, expanding dissemination channels, and creating high-quality educational materials. The project also lays the groundwork for transitioning to a sustainable governance model to support long-term community growth. Activities include developer interviews, workshops, courses, tutorials, and pilot events to refine onboarding processes and foster a collaborative contributor base. By building a strong foundation for adoption, governance, and community development, this project ensures the scalability and sustainability of the open-source ecosystem, positioning it as a key enabler for broader AI innovation and national technological leadership. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Jason MarsPI
- Lingjia Tang
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