LLMs are A Dead End in Search for General Machine Intelligence: A Review
Noor Chauhan1, Diveyam Mishra2, Mustafa Akolawala3, M.P.S. Chawla4, Khushboo Nagar5
1Noor Chauhan, Department of Artificial Intelligence and Data Science, University of Mumbai, Mumbai (Maharashtra), India.
2Diveyam Mishra, Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology and Science, Indore (Madhya Pradesh), India.
3Mustafa Akolawala, Department of Computer Science, University of Mumbai, Mumbai (Maharashtra), India.
4Prof. M.P.S. Chawla, Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology and Science, Indore (Madhya Pradesh), India.
5Asst. Prof. Khushboo Nagar, Assistant Professor, Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology and Science, Indore (Madhya Pradesh), India.
Manuscript received on 18 December 2025 | First Revised Manuscript received on 08 January 2026 | Second Revised Manuscript received on 16 February 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026 | PP: 1-9 | Volume-16 Issue-1, March 2026 | Retrieval Number: 100.1/ijsce.F370715060126 | DOI: 10.35940/ijsce.F3707.16010326
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This extensive review of large language models (LLMs) aims to highlight the importance of scaling the current generation of large language models toward artificial general intelligence, which is a dead end, while also considering the risks of unregulated use of such models. Through this, it is aimed to explicitly highlight the intelligence factor of current large language models and their malicious manipulative ability. While many large language model organisations compete to achieve better results by scaling up their models, this ultimately leads to the models’ collapse. It is too early to understand the development and benefits of large language models; many have cited LLMs as the primary means of achieving general intelligence agents. To counter this, this paper gathers and evaluates resources from multiple research articles and tests several frequently used LLMs, highlighting their importance in different scenarios. As these models are trained on a wide variety of data, they exhibit domain-independent intelligent behaviour but fail to exhibit causal intelligent behaviour.
Keywords: Large Language Model, General Machine Intelligence, Generative Pretrained Transformer, Machine Common Sense, Artificial Intelligence.
Scope of the Article: Artificial Intelligence