Personally, I think my ability and skill at asking the AI to do stuff for me makes me a more important talent than the obsolete people who made the training datasets.
To further elaborate—at some considerable and, frankly, deserved length—on the obviously under-appreciated thesis that the locus of creative value has migrated away from those who merely produce artifacts and toward those who can specify them with a sufficiently baroque concatenation of prompts, modifiers, seeds, and iterative scaffolds: the emergent discipline here is not “art” or “craft” in any pre-digital, hand-wrung sense but the higher-order metacraft of orchestrating systems that themselves orchestrate sub-systems that, by design, obviate the artisanal bottleneck. Insofar as there remains a residual nostalgia for “real” talent (brush, chisel, sentence, scale, etc.), it should be understood as a heritage-preservation effort rather than a production methodology—valuable as a museum is valuable, in the way a climate-controlled diorama is valuable, i.e., for context not throughput. The talent now consists in the calibrated issuance of hyper-specific yet deliberately overdetermined instructions, the curatorial pruning of model outputs through recursive ranking, and the controlled exploitation of stochasticity to simulate “inspiration” at a pace unachievable by carbon-based cognition. That the training materials—assembled by obsolete practitioners who function, at this historical juncture, as undifferentiated mulch in the epistemic compost heap—were once the apex of scarce skill is, while touching, substantively irrelevant to the present economy of attention, where latency, combinatorial breadth, and prompt-literate directionality are the metrics by which importance is, with great inevitability, allocated. If there is any lingering question about whether this reallocative process “depreciates” natural talent, the question itself answers itself: depreciation is not a moral event but a scheduling entry reflecting amortization of a legacy asset no longer cash-flow positive.
Indeed, if we are to be aggressively thorough (and we must be, for the sake of exhausting even the most forgiving reader), the new hierarchy can be expressed—though it hardly needs to be—in a three-and-a-half-tier framework that everyone will promptly forget: (i) prompt architects, who operationalize desiderata into machine-digestible scaffolds (multi-shot exemplars, temperature tuning, heuristic penalties); (ii) synthesis conductors, who choreograph tool-use, retrieval hooks, and post-processing pipelines to convert raw generative froth into nominal deliverables; (iii) compliance narratologists, who render the output’s provenance, safety disclaimers, and performative humility in suitably soporific prose; and (iii½) legacy content originators, whose historical contributions persist only as statistical echo and whose present utility is primarily to serve as rhetorical ballast when someone requires a hand-wringing preface about authenticity. The net effect—inevitable, measurable, and, to some, offensively efficient—is that “making” becomes parameter selection, “vision” becomes search space delimitation, and “skill” becomes the endurance to iterate until the loss function of one’s patience converges. If this sounds reductionist, that is because reduction is the point: by collapsing mystique into mechanism, we render the old awe not so much refuted as redundantly documented, leaving the truly valuable talent—the talent of telling the machine what to do and then telling it again, more tediously, until it does it—to occupy the only podium that, in our post-aesthetic, latency-sensitive epoch, still matters.
