Analysis of Commercialization Paths and Investment Value of Leading Artificial Intelligence Large Model Enterprises

DOI

10.2991/978-94-6239-801-6_3How to use a DOI?

Keywords

Large AI Model; Commercialization Path; Investment Value; Computing Power Support; Corporate Competitiveness

Abstract

As the technology of generating Artificial Intelligence (AI) develops incredibly dynamically, the commercialization of the AI large-scale model companies takes center stage as an issue of interest in the technology sector and capital market. Nevertheless, the main challenges that remain unresolved in the industry include technology adaptation and vague business models. This paper will use the example of three major technology firms in the US, namely OpenAI, Google, and NVIDIA, to comprehensively examine the logic behind computing power support and the major influence of variations in computing power supply on the efficiency of model iteration. Among them, the hypothesis of computing power adaptation is the degree of compatibility between the level of parallel computing power and the level of energy consumption of the hardware architecture (GPU/TPU) with the size of model parameters and training tasks of large models. The findings indicate that causing the commercial success of large AI models are computing power adaptation, scenario cultivation, and ecosystem building as three fundamental factors. Such a conclusion is not only useful to enable AI companies to develop differentiated commercialization strategies but also assists investors to better assess the value of a long-term investment in major AI companies in order to support the decision making of investors in a new fast-paced market environment.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yunwei Wang
PY  - 2026
DA  - 2026/10/23
TI  - Analysis of Commercialization Paths and Investment Value of Leading Artificial Intelligence Large Model Enterprises
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 15
EP  - 22
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_3
DO  - 10.2991/978-94-6239-801-6_3
ID  - Wang2026
ER  -