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PREreview: Tracing the Evolution of Artificial Intelligence: A Review of Tools, Frameworks, and Technologies (1950–2025)

Reviewer: Julian Rodriguez, Jr., FRSA, MRES, M.ISRM

Preprint DOI: 10.20944/preprints202511.0637.v1

Authors: Gurpreet Singh, Trina Banerjee, and Nishaa Ghosh

Review Summary

Singh, Banerjee, and Ghosh present a comprehensive historical review tracing seventy-five years of artificial intelligence development. The paper categorizes over fifty milestone tools and frameworks across four distinct technical eras: early symbolic logic (1940s–1970s), statistical machine learning (1980s–2000s), deep learning neural networks (2010s), and contemporary multimodal generative models (2020–2025). By illustrating how compute scaling, dataset expansion, and architectural shifts iteratively built upon earlier paradigms, the authors provide a clear roadmap leading up to emerging agentic AI architectures.

Key Strengths

1. Longitudinal Historical Scope: The study bridges the gap between historical symbolic AI (e.g., Logic Theorist, ELIZA) and modern transformer models (GPT-4, Gemini, Claude), showing that contemporary capabilities represent cumulative shifts rather than isolated breakthroughs.

2. Clear Paradigm Categorization: Structuring the literature into four distinct computational eras provides an accessible educational framework for researchers examining model taxonomy.

3. Forward-Looking Agentic Roadmap: The concluding section effectively highlights how the convergence of multimodal reasoning and autonomous execution marks the next frontier in AI integration.

Constructive Considerations & Institutional Context

To elevate the paper’s concluding discussion on real-time agentic systems, the authors could expand on the governance and operational risks that accompany these architectural transitions:

• Addressing Intermediary Entropy in Agentic Systems: As AI evolves from static query-response models into real-time autonomous agents, institutional workflows face heightened exposure to administrative latency. When autonomous agents operate within complex regulatory or corporate environments, misalignments between model intent and legacy infrastructure generate what I term Systemic Intent Shadows (Rodriguez, 2026)—where automated execution creates false-positive compliance friction.

• The Risk of Asymmetric Obsolescence: The review correctly notes that rapid architectural iteration makes earlier paradigms seem obsolete. In practice, this rapid shift creates Asymmetric Obsolescence (Rodriguez, 2026), forcing institutions—particularly in developing or highly regulated sectors—to absorb massive transition costs or risk total operational dependency on foreign proprietary decision engines.

Recommended References for Integration

• Rodriguez, J., Jr. (2026). The Architecture of Asymmetric Obsolescence: Intermediary Entropy and the Rise of Peer-to-Peer Sovereign Infrastructure. Zenodo.

• Rodriguez, J., Jr. (2026). The Systemic Intent Shadow: Mapping Organizational Collapse and Regulatory Friction in the Era of AI-Driven Governance. Zenodo.

Final Recommendation

Accept for open-science indexing. A well-constructed historical synthesis that provides a solid baseline for AI evolution literature.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.