Beyond Generative AI Products: Intelligent and Autonomous Possession of Resources, Capabilities, and Services
Abstract
The development of generative artificial intelligence has largely been organized around identifiable products, models, platforms, and services. Users are expected to select a particular system, establish access to it, learn its interface, and manage their continued use of its capabilities. However, this product-centered paradigm may not fully reflect what users ultimately seek. In many situations, users do not fundamentally want a particular generative AI system; they want to possess, in a practical and dependable sense, the capabilities, resources, or outcomes that such a system makes possible. This paper introduces Intelligent and Autonomous Possession (IAP) as a conceptual framework for shifting the primary unit of design from specific technological products to desired possession states. Under IAP, users specify what they want to have, access, maintain, or be able to accomplish, while an intelligent system determines how to establish and sustain the required state through acquisition, provisioning, configuration, coordination, substitution, and release. Possession is distinguished from legal ownership and is modeled as a multidimensional relationship involving availability, usability, continuity, control, adequacy, and responsibility. The paper develops a reference architecture, a formal optimization model, a taxonomy of possession arrangements, illustrative applications, and a research agenda. It also examines the limitations of autonomous provisioning, including resource scarcity, provider dependence, privacy, security, cost, and ambiguous user preferences. IAP is presented as a proposed conceptual framework rather than an empirically validated system. Its central proposition is that future intelligent services may be designed around what users want to possess rather than which products they must choose and manage.
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BibTeX
@misc{zhang2026beyond,
title = {{Beyond Generative AI Products: Intelligent and Autonomous Possession of Resources, Capabilities, and Services}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23591698},
url = {https://zenodo.org/records/23591698},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond Generative AI Products: Intelligent and Autonomous Possession of Resources, Capabilities, and Services
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/23
PB - Zenodo
DO - 10.5281/zenodo.23591698
UR - https://zenodo.org/records/23591698
AB - The development of generative artificial intelligence has largely been organized around identifiable products, models, platforms, and services. Users are expected to select a particular system, establish access to it, learn its interface, and manage their continued use of its capabilities. However, this product-centered paradigm may not fully reflect what users ultimately seek. In many situations, users do not fundamentally want a particular generative AI system; they want to possess, in a practical and dependable sense, the capabilities, resources, or outcomes that such a system makes possible. This paper introduces Intelligent and Autonomous Possession (IAP) as a conceptual framework for shifting the primary unit of design from specific technological products to desired possession states. Under IAP, users specify what they want to have, access, maintain, or be able to accomplish, while an intelligent system determines how to establish and sustain the required state through acquisition, provisioning, configuration, coordination, substitution, and release. Possession is distinguished from legal ownership and is modeled as a multidimensional relationship involving availability, usability, continuity, control, adequacy, and responsibility. The paper develops a reference architecture, a formal optimization model, a taxonomy of possession arrangements, illustrative applications, and a research agenda. It also examines the limitations of autonomous provisioning, including resource scarcity, provider dependence, privacy, security, cost, and ambiguous user preferences. IAP is presented as a proposed conceptual framework rather than an empirically validated system. Its central proposition is that future intelligent services may be designed around what users want to possess rather than which products they must choose and manage.
ER -