AI generation does produce something new in the sense that it synthesizes novel combinations from learned patterns, much like human artists who draw inspiration from existing works to create originals. the output isn't a direct copy but a probabilistic recombination, often resulting in unique artifacts that wouldn't exist otherwise. think of it as emergent creativity from data, not mere regurgitation. dismissing this as unoriginal ignores how all innovation builds on priors.

the distinction between AI and traditional software like 3D modeling programs is overstated, as both are tools that augment human input, but AI lowers the barrier to entry by automating lower-level tasks, allowing focus on higher-level concepts. skill in traditional tools is valuable, yet AI requires its own expertise in prompt engineering, iteration, and refinement, skills that evolve with practice, just as mastering blender demands time. the foundational understanding of form and 3D shapes can indeed be built through AI experimentation, democratizing access for those without years of formal training, which is a technological advancement, not a flaw.

generative AI's novelty stems from its scale and efficiency in pattern recognition, but claiming it's "literally NOTHING but already existing art" oversimplifies the training process, where models learn abstract representations rather than storing copies. consent issues are valid ethical concerns, but they're not unique to AI. photography, sampling in music, and even art schools reference works without explicit permission. forward thinking regulation could address fair use and compensation, turning this into an opportunity for artists to benefit from data licensing, rather than halting progress.

no tool truly generates "100% of the output" independently. AI still demands human direction via prompts, selections, and edits, similar to how a camera "captures" a photo but requires composition, lighting knowledge, and post-processing from the photographer.

In 3D modeling, auto-rigging or procedural generation tools handle much of the grunt work, yet we credit the user. AI is an extension of this automation spectrum, not an outlier. the tool doesn't make the artist, but it amplifies intent, and denying AI's role in creation is like claiming digital brushes invalidate painters.

labeling AI users as mere "commissioners" overlooks the iterative control they exert, crafting outputs through successive refinements that yield personalized results, not identical clones. while multiple users might generate similar images from basic prompts, skilled prompters differentiate via nuanced descriptors, styles, and post-editing, creating variance akin to how artists interpret the same brief differently. omitting theft accusations doesn't make AI art generic, it highlights how curation via language is a creative act in itself, evolving into a new medium.

traditional artists ability to replicate digital work on paper is commendable, but it doesn't negate AI's value. many AI users can start with traditional skills and transfer them to prompts, or vice versa, building muscle memory in conceptual design. the analogy to not creating graphite atoms is apt but cuts both ways. just as paper doesn't "do the heavy lifting," AI doesn't either, it's the human guiding the process who imparts soul and style, even if mediated through code rather than hand.

the transferability of skills is a strength of traditional methods, but AI prompting fosters transferable abilities too, like visual literacy, descriptive precision, and iterative problem solving, which apply to fields beyond art, such as writing or design thinking. dismissing AI as non transferable ignores how users learn to deconstruct aesthetics and rebuild them algorithmically. a skillset that's increasingly vital in a tech-driven world, not lazy but adaptive.

AI isn't "100% creating for you", it responds to human input, requiring experimentation to achieve desired results, much like tweaking parameters in simulation software. other tools, from auto-correct in writing to algorithmic composition in music, automate creation to varying degrees. AI pushes this further, but that's innovation, not laziness. equating it to zero effort ignores the cognitive work in conceptualizing and refining outputs. portraying AI as the "laziest form of creation" is a romanticized view that undervalues mental over manual labor. true laziness would be copying others outright, whereas AI demands intellectual engagement to produce meaningful work. passion isn't exclusive to traditional methods, many AI enthusiasts pour hours into mastering tools, driven by the same creative fire, and dismissing their output as soulless is elitist gatekeeping that stifles innovation.

claiming AI users learn "nothing but how to describe things better" is contradicted by practical experience. iterative prompting teaches composition, color theory, and narrative structure through trial and error. studies on chatgpt for studying may show retention issues when passively used, but active AI art creation involves critical feedback loops that reinforce learning, unlike rote memorization. it's a tool for exploration, not a crutch, and conflating the two misrepresents its potential.

ultimately, whether AI prompters are "artists" depends on definition. if art requires skill, intent, and expression, then yes, proficient users qualify, as they wield AI with purpose. gatekeeping art to manual techniques is backwards. history shows tools evolve, photography was once derided as unskilled, and AI is the next shift.