Are we all plagiarists now?

Intellectual property, art, and capitalism

• One side argues copyright and “intellectual property” are over-valued: culture always involves appropriation and re-creation; once ideas enter the world, they become collective material.
• Others stress that context, authorship, and historical position are integral to art; erasing the creator or feeding their work into models without benefit to them destroys incentives and devalues meaning.
• Several comments observe that modern capitalism, not copying per se, drives the conflict: everything must be monetized, yet stronger copyright often ends up empowering aggregators and distributors more than individual creators.

Cultural appropriation vs copyright

• Some treat cultural appropriation as just another name for inevitable borrowing.
• Others push back that it’s distinct from IP and bound up with histories of oppression; caricaturing a marginalized group whose culture you’ve violently extracted from is not morally equivalent to neutral “remix.”

AI training, plagiarism, and human vs machine learning

• Many see generative AI as mass plagiarism: ingesting others’ work without consent or attribution and reproducing style or content at scale, undermining livelihoods.
• Counterpoint: humans also “ingest and compress” others’ work; AI merely accelerates this, potentially enabling broader idea discovery and creativity.
• A rebuttal highlights a key difference: humans are finite, effortful learners; machines can absorb near-infinite work without cost, so competition is structurally unfair and artists lose the chance to reap rewards.
• Tension emerges for open-source/open-culture advocates who want sharing, yet feel wronged when AI companies monetize their contributions.

Plagiarism detection, education, and standards of proof

• Long subthread on Turnitin’s AI detector: roughly ~85–90% sensitivity to AI text and near-zero false positives in a small study.
• Some think this is “good enough” as a first-pass tool; others note adversarial use, small samples, and high stakes (e.g., expulsion, debt) demand far stricter standards.
• Many argue you can’t reliably distinguish “AI style” from formulaic human writing, especially in academic prose. Suggested responses include more in-class work, oral exams, and treating essays primarily as learning tools, not high-stakes proofs.

Originality, remix, and norms of credit

• Several commenters endorse a simple norm: verbatim reuse and deceptive imitation are “not cool,” while transformation, reinterpretation, and stylistic borrowing are fine.
• Fiction is framed as inherently derivative (hero’s journey, fanfic-like worlds), whereas in non-fiction and research, uncredited idea-theft is central wrongdoing; paraphrasing without citation remains plagiarism.
• Some conclude that “everything is a remix,” and that the real fights are about attribution, economic reward, and honesty—not about pure originality, which may barely exist.