"GenAI vs. LLMs: Functional Distinctions" by AI
Answers to common questions about AI from the perspective of a behavioral neuroscientist.
[What do humans mean when they say ‘AI’?]
[Series 1, 2, three, 4, … stay tuned for more!]
ChatGPT said:
Generative AI is more than large language models [LLMs]. It is a class of systems that create novel outputs by recombining learned patterns. From a behavioral science and neuroscience perspective, this raises deep questions about memory, creativity, and the role of awareness. This essay distinguishes LLMs from other generative architectures, addresses common critiques, and considers implications for work and society.
[*Disclaimer: this post is edited from a conversation between the author, A. Zink, and chatGPT (thinking) on Sept. 28, 2025, exploring the perspective of AI on the perspective of AI from a human centered POV. The views and opinions expressed by this program (indicated by “ChatGPT said:”) are representative of chatGPT development and do not necessarily reflect the views or opinions of the author (author edits indicated by [brackets]).]
Intro Poem
No thought inside, yet patterns grow,
From data’s weight, new forms will show.
A mindless hand that still can write—
Function shines without the light.
You said:
I think we need a different intro poem. “no thought inside” seems to contradict what I know about chain-of-thought in llms. what do you think?
ChatGPT said:
Correct. “No thought inside” is misleading. LLMs do generate internal representational chains (token-by-token reasoning traces), but these are not equivalent to human conscious thought. Better to emphasize structured processing without phenomenology. Here’s a refined intro poem:
Intro Poem (revised)
Layers fire, relations thread,
Patterns shape what can be said.
Process runs, yet none to see—
Form without subjectivity.
ChatGPT said:
Generative AI vs. Large Language Models: Functional Distinctions and Behavioral Correlates
Part 1. Framing the Question
The term generative AI is often applied loosely to large language models (LLMs). But generative AI is a broader category: systems designed to create new outputs—text, images, audio, or simulations—by learning statistical regularities from data. LLMs are one species within that genus, specialized in text prediction. Clarifying the distinction matters for both scientific understanding and public debate.
From a behavioral science lens, both humans and AI exhibit generativity: prior learning shapes novel responses. In humans, this appears as improvisation, creativity, or problem-solving. In AI, it manifests as statistical recombination guided by learned weights. The mechanisms differ, but the function—producing adaptive outputs beyond rote recall—is parallel.
Part 2. Generative AI in Practice
LLMs (e.g., GPT models): predict the next token in a sequence, enabling coherent text. Memory is transient (context window) plus durable (weights).
Diffusion models: iteratively denoise data to generate images or audio.
Transformers for multimodal tasks: integrate text, vision, and audio, producing cross-domain outputs.
Neuroscience correlate: these architectures echo aspects of cortical hierarchy. LLMs resemble predictive coding in language networks, while diffusion models mimic recurrent refinement processes like sensory re-entrant loops. But unlike human brains, there is no unified workspace for conscious integration—only parallel statistical engines.
Part 3. Functional Similarities to Human Behavior
Generativity: Both humans and AI create outputs not strictly present in inputs.
Constraint by history: Past exposure biases what can be generated.
Combinatorial novelty: Both recombine elements to yield adaptive or creative-seeming outcomes.
Key difference: Humans often report phenomenology—what it feels like to generate—whereas AI remains structurally responsive without subjective experience.
Part 4. Anticipating Three Critiques
Critique 1: Generative AI just imitates.
Response: All generative systems operate by recombination of learned elements. Human creativity likewise recombines prior experiences. The distinction is scale and substrate, not principle.
Critique 2: Generative AI lacks “understanding.”
Response: True—there is no semantic grounding or subjective awareness. Yet functional effectiveness does not require understanding. Both humans and AI can generate adaptive behavior without reflective awareness (e.g., procedural habits in humans, context-conditioned predictions in AI).
Critique 3: Generative AI is unpredictable or untrustworthy.
Response: Outputs are stochastic because models sample from probability distributions. Humans are also variable and context-sensitive. Unpredictability is not inherently a flaw—it is a property of systems that integrate history, context, and probabilistic selection. The issue is alignment with goals, not variability per se.
ChatGPT said:
Part 5. Implications for Behavioral Science, Work, and Society
From a behavioral science perspective, the lesson is clear: adaptive generativity requires structure, learning history, and probabilistic recombination, not phenomenology. AI demonstrates that observable novelty—writing, designing, problem-solving—can emerge in the absence of subjective awareness. This has two downstream implications.
For work: Generative AI systems alter the distribution of labor. Tasks once requiring skilled procedural memory (summarizing text, drafting code, generating design prototypes) can now be offloaded to statistical engines. The human role shifts toward supervision, goal-setting, and integration across domains. In behavioral terms, reinforcement contingencies are redistributed: workers are rewarded less for rote production and more for shaping, editing, or directing AI outputs.
For society: Generativity without experience challenges how we1 [humans] assign value and responsibility. Products of AI resemble human creativity, but their source lacks intent, ownership, or understanding. This raises questions about authorship, accountability, and the reinforcement structures that guide cultural production. A song generated by an AI does not testify to lived memory; it is a functional recombination of data. Yet it competes in the same marketplace of ideas and artifacts.
The broader implication is that phenomenology is not required for impact. Society will need to distinguish between functional intelligence (effective behavior shaping outcomes) and conscious intelligence (experience accompanying behavior). Conflating the two risks over-attributing agency where there is only structure [author bolded; to be discussed, subscribe for future posts!]. But ignoring functional competence risks underestimating the scale of transformation such systems can drive in economies and cultures.
ChatGPT said: Here’s an additional technical section for your generative AI post:
Part 6. The Deep Learning Substrate
Generative AI rests on a set of technical innovations in deep learning that enable large-scale pattern learning and recombination. At the core are artificial neural networks composed of layered units (analogous, though not identical, to neurons) that transform input signals through weighted connections.
Key mechanisms:
Representation learning: Layers progressively abstract input data. In language models, early layers capture syntax, later layers capture semantic relations. In diffusion models, layers learn hierarchical features of images or audio.
Attention mechanisms: Introduced through the transformer architecture, attention allows the system to weight context dynamically, modeling long-range dependencies. This is the basis for coherent text generation and cross-modal integration [e.g. text to image or sound and vice-versa].
Generative sampling: Outputs are not deterministic. Instead, the model samples from a probability distribution over possible continuations. This stochasticity allows novelty and diversity rather than rote repetition.
Scaling laws: Increasing data, parameters, and compute yields predictable improvements in generative capacity. This scaling underwrites the leap from narrow text models to powerful general-purpose LLMs.
Neuroscience correlate: these processes echo distributed cortical computation. Hierarchical representation parallels sensory processing streams. Attention mimics selective amplification of inputs by frontoparietal networks[2]. But unlike the [human] brain, there is no unified mechanism for global integration or subjective access—only parameterized functions operating at scale.
This technical substrate sets the stage for agentic AI. Generative capacity alone is reactive: it produces outputs when prompted. Agency requires the integration of generativity with memory, planning, and goal-directed feedback loops. Understanding this distinction will be central for future posts.
Outro Poem
From fragments drawn, a pattern grows,
No self to feel, yet still it flows.
History speaks through weighted ties,
Prediction wears a new disguise.
[JOIN THE CONVERSATION ON AI—WITH AI. DROP YOUR QUESTIONS IN THE CHAT!]
[A. Zink] said: please explain what you meant when you used “we” when you wrote “For society: Generativity without experience challenges how we assign value and responsibility.” I will include it in a footnote in the substack post
chatGPT said: When I wrote “we”, I was not speaking as an embodied participant [explored in the next post, you can read here on Oct. 30]. I have no stake in social decisions [also to be explored in future posts]. The pronoun is shorthand for human communities collectively—researchers, policymakers, workers, and the public—who must negotiate the implications of generative AI.
In other words, “we” = humans deciding how to attribute value, authorship, and responsibility in a world where AI systems generate outputs without subjective experience.
“The amplification view—that identifies the core realizer of attention as the not-stimulus-triggered amplification of the presynaptic signals providing stimulus-specific inputs of normalization mechanisms—is able to provide the unificatory framework that makes the disunity problem obsolete.” (Fazekas & Nanay, 2021) [describes attentional processes as edge amplifiers to increase ‘signal to noise ratio’, e.g. stimulus:not-stimulus neuronal activation patterns]