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the claim

Computer programs and AI are incapable of true semantic understanding

the verdict
SUPPORTED
the evidence backs this
Recorded sources
6 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that computer programs and AI are incapable of true semantic understanding?

Yes. The retrieved literature consistently supports the position that computer programs and AI systems perform syntactic manipulation without possessing true semantic understanding, as they lack sensorimotor grounding, intentionality, and subjective conscious experience.

Verdict: SUPPORTED · 6 sources for, 0 against · Last checked

The analysis

Papers 2, 3, 5, 9, 10, and 11 all explicitly argue or review the philosophical and cognitive science consensus that current AI and computational models process syntax without achieving genuine semantic understanding, intentionality, or phenomenal consciousness. No provided papers successfully refute this claim; papers discussing high statistical performance or category theory (like 0, 1, and 8) describe functional behavior or syntactic-semantic mapping rather than demonstrating true intrinsic semantic comprehension.

Evidence for · 6
Which symbol grounding problem should we try to solve?
2015
Argues that computing is fundamentally syntactic and computational agents only reproduce the functional behavior of meaning rather than true semantic grounding.
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Müller, V. C. (2015). Which symbol grounding problem should we try to solve?. doi.org. https://doi.org/10.1080/0952813X.2014.940143

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More for · 5
Language writ large: LLMs, ChatGPT, meaning, and understanding.
2024
Concludes that LLMs lack direct sensorimotor grounding to connect words to their real-world referents and propositions to true meanings.
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S, H. (2024). Language writ large: LLMs, ChatGPT, meaning, and understanding.. doi.org. https://doi.org/10.3389/frai.2024.1490698

Formulation of the problem and definition of approaches to building semantic knowledge models for artificial intelligence.
2025
Notes that artificial cognitive systems lack subjectivity and value prioritization, which are necessary limitations when attempting to understand meanings.
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Gribkov, A. & Zelenskii, A. A. (2025). Formulation of the problem and definition of approaches to building semantic knowledge models for artificial intelligence.. doi.org. https://doi.org/10.25136/2409-8728.2025.5.74407

Symbol Grounding Problem
2009
Reviews Searle's Chinese Room and Harnad's symbol grounding problem, emphasizing that formal symbol manipulation lacks intrinsic intentionality and aboutness.
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Loula, A. & Queiroz, J. (2009). Symbol Grounding Problem. doi.org. https://doi.org/10.4018/978-1-59904-849-9.ch226

No Consciousness? No Meaning (and no AGI!)
2026
Argues that true semantic understanding requires a connection to subjective conscious experience and qualia, which current AI systems lack.
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M, M. (2026). No Consciousness? No Meaning (and no AGI!). doi.org. https://doi.org/10.32388/dn232y.6

Aidification of the self: a phenomenological approach to machine consciousness through human-robot 'between-ness'.
2026
Maintains that conversational AI systems remain ungrounded discursive constructs whose linguistic patterns are entirely detached from lived human experience.
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S, T. (2026). Aidification of the self: a phenomenological approach to machine consciousness through human-robot 'between-ness'.. doi.org. https://doi.org/10.1093/nc/niag032

Receipt History
first checked04 Aug 2026
judged → SUPPORTED · 8104 Aug 2026
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