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<p>Artificial intelligence (AI), especially large language models and generative systems, challenges traditional notions of cognition and meaning production. Despite advanced linguistic fluency, AI fundamentally lacks true semantic understanding due to its disembodied, computational, and non-interactive nature. A triadic framework of matter, energy, and information reveals AI as a materially grounded, energetically constrained, and socially embedded technology, sharply contrasting with inherently embodied and socially interactive human cognition. Beyond technical features, AI’s development and deployment are increasingly shaped by corporate interests, resulting in algorithmic governance and knowledge monopolization that reinforce technocratic ideologies while obscuring material dependencies. Cases, such as biased recruitment algorithms and discriminatory facial recognition, exemplify the socio-political consequences of these dynamics. Dominant narratives, including AI singularity theories, divert attention from these tangible issues. Reframing AI as a socially constructed, non-autonomous socio-technical tool underscores the urgent need for democratic governance and ethical oversight. This shift moves the discourse away from speculative futures toward concrete interventions addressing power imbalances and socio-material conditions in AI development.</p>

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Can machines think beyond words? A critique of AI’s meaning-production process

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Lihui Wang

摘要

Artificial intelligence (AI), especially large language models and generative systems, challenges traditional notions of cognition and meaning production. Despite advanced linguistic fluency, AI fundamentally lacks true semantic understanding due to its disembodied, computational, and non-interactive nature. A triadic framework of matter, energy, and information reveals AI as a materially grounded, energetically constrained, and socially embedded technology, sharply contrasting with inherently embodied and socially interactive human cognition. Beyond technical features, AI’s development and deployment are increasingly shaped by corporate interests, resulting in algorithmic governance and knowledge monopolization that reinforce technocratic ideologies while obscuring material dependencies. Cases, such as biased recruitment algorithms and discriminatory facial recognition, exemplify the socio-political consequences of these dynamics. Dominant narratives, including AI singularity theories, divert attention from these tangible issues. Reframing AI as a socially constructed, non-autonomous socio-technical tool underscores the urgent need for democratic governance and ethical oversight. This shift moves the discourse away from speculative futures toward concrete interventions addressing power imbalances and socio-material conditions in AI development.