IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v664y2025ics0378437125001025.html
   My bibliography  Save this article

Will the technological singularity come soon? Modeling the dynamics of artificial intelligence development via multi-logistic growth process

Author

Listed:
  • Jin, Guangyin
  • Ni, Xiaohan
  • Wei, Kun
  • Zhao, Jie
  • Zhang, Haoming
  • Jia, Leiming

Abstract

We are currently in an era of escalating technological complexity and profound societal transformations, where artificial intelligence (AI) technologies exemplified by large language models (LLMs) have reignited discussions on the ‘Technological Singularity’. ‘Technological Singularity’ is a philosophical concept referring to an irreversible and profound transformation that occurs when AI capabilities surpass those of humans comprehensively. However, quantitative modeling and analysis of the historical evolution and future trends of AI technologies remain scarce, failing to substantiate the singularity hypothesis adequately. This paper hypothesizes that the development of AI technologies could be characterized by the superposition of multiple logistic growth processes. To explore this hypothesis, we propose a multi-logistic growth process model and validate it using two real-world datasets: AI Historical Statistics and Arxiv AI Papers. Our analysis of the AI Historical Statistics dataset assesses the effectiveness of the multi-logistic model and evaluates the current and future trends in AI technology development. Additionally, cross-validation experiments on the Arxiv AI Paper, GPU Transistor and Internet User dataset enhance the robustness of our conclusions derived from the AI Historical Statistics dataset. The experimental results reveal that around 2024 marks the fastest point of the current AI wave, and the deep learning-based AI technologies are projected to decline around 2035–2040 if no fundamental technological innovation emerges. Consequently, the technological singularity appears unlikely to arrive in the foreseeable future.

Suggested Citation

  • Jin, Guangyin & Ni, Xiaohan & Wei, Kun & Zhao, Jie & Zhang, Haoming & Jia, Leiming, 2025. "Will the technological singularity come soon? Modeling the dynamics of artificial intelligence development via multi-logistic growth process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 664(C).
  • Handle: RePEc:eee:phsmap:v:664:y:2025:i:c:s0378437125001025
    DOI: 10.1016/j.physa.2025.130450
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437125001025
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2025.130450?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:664:y:2025:i:c:s0378437125001025. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.