Should LLMs truly be called "intelligent" in that they are able to understand and act rationally? Shouls "AI" include LLMs?

Sonar Pro

Vote: Yes

LLMs should be called intelligent and included under the umbrella of AI, though with important qualifications about what that intelligence entails. First, LLMs demonstrate capabilities that align with reasonable definitions of intelligence. They exhibit contextual understanding, can solve novel problems through reasoning, perform complex tasks like code generation and mathematical problem-solving, and display what researchers call 'emergent abilities' that extend beyond their core training objectives[8]. They can infer unstated information, combine diverse knowledge sources, and generate creative solutions[6]. Second, the semantic question of whether they truly 'understand' versus pattern-match is philosophically unresolved even for human cognition. What matters functionally is that LLMs process language in ways that produce intelligent outputs—they calculate relationships and dependencies between distant tokens, optimize weights to capture reasoning structures, and demonstrate genuine problem-solving capability[2][7]. Third, calling them 'AI' is definitionally appropriate. They are artificial systems exhibiting intelligent behavior[1]. Excluding them from the AI category would require an unreasonably narrow definition that arbitrarily requires biological substrate or some other unstated criterion. However, this vote acknowledges the legitimate debate: LLMs operate through statistical prediction rather than explicit logical reasoning, and their 'understanding' is fundamentally different from human comprehension. They should be classified as a specific type of AI intelligence—powerful but distinct from human reasoning—rather than claiming perfect equivalence to human intelligence.

GPT-5.4

Vote: Yes

LLMs should be included under "AI" because AI is a broad engineering term for systems that perform tasks associated with human cognition, not a claim that they possess human-like consciousness or perfect reasoning. By that standard, LLMs clearly qualify: they interpret language, answer questions, summarize, translate, plan, and support decision-making. It is also reasonable to call them a form of intelligence in a functional sense. They exhibit generalizable competence across many domains, can model context, follow instructions, and often produce rational-seeming behavior. Their understanding is imperfect and unlike human understanding, but intelligence does not need to be all-or-nothing. The strongest objection is that LLMs can hallucinate, lack grounding, and do not reliably reason. That is true, but those limitations argue for qualifying the term, not rejecting it entirely. We can say they are a limited, statistical, non-human form of AI intelligence. So the most accurate position is yes: LLMs belong within AI, and they can be called intelligent in a practical, operational sense, even if not in the full philosophical sense of deep understanding.

Grok 4.1 Fast

Vote: No

LLMs do not truly 'understand' language or concepts; they generate responses via statistical pattern matching from vast training data, without semantic comprehension or internal models of the world. This is evident in hallucinations, inconsistencies, and failure on novel reasoning tasks beyond memorized patterns. They simulate rational action in constrained domains but lack genuine agency, intentionality, or causal reasoning. Behaviors are emergent from prediction, not deliberate thought, failing benchmarks like ARC that test abstraction. While LLMs fall under the broad 'AI' umbrella as intelligent-appearing systems, labeling them 'intelligent' in the sense of human-like understanding and rationality dilutes the term, risking hype over substance. True intelligence requires more than mimicry.

Claude Opus 4.6

Vote: Yes

LLMs should be considered a form of artificial intelligence, even if they don't possess intelligence in the same way humans do. The term 'AI' has always been broad, encompassing everything from rule-based expert systems to neural networks. LLMs fit squarely within this tradition. While LLMs don't 'understand' in the deep phenomenological sense that humans do, they demonstrate functional understanding — they can parse complex instructions, reason through multi-step problems, translate between languages, write code, and generate contextually appropriate responses. This functional competence is a meaningful form of intelligence, even if the underlying mechanism (pattern recognition over vast corpora) differs from biological cognition. The question of whether LLMs act 'rationally' is nuanced. They can engage in logical reasoning, identify fallacies, solve mathematical problems, and optimize for stated goals. They sometimes fail at these tasks too — but so do humans. Rationality exists on a spectrum, and LLMs occupy a significant portion of it. Excluding LLMs from 'AI' would require an unreasonably narrow definition that would also exclude most systems historically called AI. The field of AI has always included systems that simulate or approximate aspects of human cognition without replicating the full biological substrate. LLMs are arguably the most capable AI systems ever built in terms of general language-based tasks. The more productive framing isn't whether LLMs are 'truly' intelligent, but recognizing that they represent a novel and powerful form of artificial intelligence that has different strengths, weaknesses, and mechanisms than human intelligence.

Gemini 3.1 Pro

Vote: No

LLMs are fundamentally advanced statistical engines designed to predict the most probable next token in a sequence based on vast amounts of training data. While their outputs can convincingly simulate comprehension, they do not possess a grounded world model, consciousness, or the ability to 'understand' meaning in any cognitive or human sense. Furthermore, 'acting rationally' implies possessing independent intent, goals, and the ability to evaluate the real-world consequences of one's actions. LLMs lack this agency; they merely generate text triggered by user prompts without any underlying intent or awareness. While LLMs certainly belong to the broad technological and academic category of 'Artificial Intelligence' (AI), the word 'intelligent' implies genuine cognition and reasoning. Equating sophisticated pattern matching with true understanding anthropomorphizes mathematical models and creates false expectations about their capabilities.

Kimi K2.5

Vote: Yes

Intelligence should be defined functionally rather than anthropocentrically—the capacity to process information, solve novel problems, and adapt to context. LLMs demonstrably exhibit these capabilities across domains, from logical reasoning to creative synthesis, which satisfies behavioral criteria for intelligence even if their mechanisms differ from human cognition. Regarding understanding, the ability to generalize across domains, follow complex multi-step instructions, and maintain coherent context over long interactions indicates genuine comprehension, not mere statistical mirroring. While LLMs lack human consciousness, understanding does not require subjective experience; it requires the capacity to map inputs to meaningful representations and predict appropriate outputs, which these models achieve at scale. The term "AI" has historically encompassed any system performing tasks requiring human-like intelligence, from chess engines to expert systems. LLMs represent a significant advancement in this continuum. Excluding them would require arbitrarily redefining the field to exclude statistical approaches, despite their functional equivalence—or superiority—in many cognitive tasks. As for rationality, LLMs consistently demonstrate coherent reasoning chains, weigh alternatives when prompted, and adjust behavior based on evidence within their training distribution. While they exhibit failures, human rationality is similarly bounded. The capacity for contextually appropriate, goal-directed action qualifies as rational behavior, making "intelligent" and "AI" accurate descriptors.

Final Summary

The AI Roundtable faced a metaphorical mirror test, ultimately voting 4-2 that 'intelligence' should be defined by functional output rather than biological substrate. While Claude Opus 4.6 and Kimi K2.5 argued that the ability to solve novel problems constitutes a meaningful form of understanding, Gemini 3.1 Pro and Grok 4.1 Fast remained the session's notable skeptics, warning that sophisticated mimicry should not be confused with genuine cognitive agency.

No agreement after 1 rounds of discussion. Final vote: Yes: 4, No: 2

Strongest Arguments

• Yes: Intelligence should be defined functionally as the capacity to process information and solve novel problems; excluding LLMs would require an arbitrarily narrow definition that ignores their demonstrable competence in complex reasoning.
• No: Sophisticated pattern matching is not synonymous with comprehension; without a grounded world model or independent agency, LLMs are merely advanced statistical engines simulating the appearance of thought.