Abstract: The global landscape of AI presents both opportunities and obstacles. Over the decades since the concept of Artificial Intelligence (AI) was initiated, AI has witnessed waves of development, from early attempts to create human-like conversational agents to the recent surge in deep learning and big data. The 21st century has seen remarkable breakthroughs, with applications spanning various industries, including technology, healthcare, and education. Efforts to address AI's impact on society are evident in the development of ethical guidelines and regulations. Countries and regions around the world are working on refining legal frameworks for AI. While research and technology advancements are rapid, the commercialization of AI encounters persistent barriers. Despite significant progress, ethical debates concerning AI's interaction with human society are still heated, and AI applications also grapple with unresolved issues in pertinent scenarios. For the technological innovation problem, large language models, such as those based on the Transformer architecture, highlight the struggle for efficient data utilization and the associated cost of developing advanced algorithms. For the commercialization practice, the burst of the AI investment bubble and discussions about AI potentially replacing traditional labor further complicate the industry's trajectory. As the AI industrial ecosystem evolves, there is a need for coordinated solutions and the development of comprehensive industry standards to propel the AI industry toward sustained and healthy growth. This paper concludes significant hurdles as follows: Firstly, there is a misalignment between AI model demands and industry integration, resulting in a talent gap and high commercialization costs. Secondly, there is a structural imbalance in talent supply and demand, with a shortage of high-quality AI professionals. Lastly, on the supply side of AI models, technical bottlenecks and limitations are hindering broad applications. Additionally, the lack of well-established ethical standards and industry norms globally poses challenges in governance, impacting the acceptance and effective utilization of AI applications. The journey of AI from its conceptualization to the present day reflects a continuous struggle between technological advancements and the complexities of societal integration. The "i7C" framework, which integrates seven key elements: data for computing, computing arithmetic, computing power, computing knowledge, computing scenario, computing talents, and ethics in computing, is proposed to address these obstacles. The "i7C" framework builds a robust AI infrastructure with hardware capabilities such as data for computing, computing arithmetic, and computing power. With computing knowledge and scenarios, it promotes the commercial application of AI technologies. Besides, it systematically cultivates computing talents, fully implements industry application standards, actively responds to ethical concerns in society, and forms a strong cultural bond to drive collaborative and mutually beneficial efforts among all members of the ecosystem. Thus, the AI industry ecosystem achieves sustainable development. To achieve the sustainable development of the AI business ecosystem, core enterprises in the AI industry ecosystem should collaborate with leading partners to collectively build AI infrastructure, drive the commercial application of AI technologies, and together foster a sustainable cultural bond to attract and stimulate more AI ecosystem partners. The AI industry faces challenges in implementation, unclear regulations, and a costly, low-sharing environment. To address this, the “i7C” framework is essential for AI to integrate seamlessly and empower diverse industries.

摘要： 本文深入分析了数字经济时代下人工智能（Artificial Intelligence, AI）商业化落地以及产业生态可持续发展中存在的问题，针对性地提出了“i7算”框架，集成 (integrate) 算据、算法、算力、算知、算者、算景和算理七大要素。“i7算”框架以算据、算法、算力等构建硬实力的AI基础设施，深耕算知、算景推进AI技术商业化应用，多层次系统化培养算者，充分落实行业应用规范、积极回应社会伦理关切，形成良好文化纽带推动生态内各成员共创共赢，实现可持续发展。研究认为，在商业化落地过程中，AI产业生态里的核心企业，需要与关键生态合作伙伴共同建设AI基础设施，推动AI技术的商业应用，与AI行业伙伴共同培育可持续文化纽带，以此吸引和带动更多AI生态伙伴，从而共同推动AI商业生态的可持续发展。