OpenAI fails to deliver opt-out system for photographers

Opt-out system & consent

• Many see OpenAI’s undelivered “Media Manager” / opt-out as evidence they don’t genuinely want data excluded, especially since photographers must submit each work with detailed descriptions.
• Commenters argue the burden is absurd at scale: creators would have to track and use opt-out mechanisms for many AI firms.
• Several say consent should be opt‑in, not opt‑out: OpenAI should ask before using works, as most others must.
• Tech companies’ approach to consent is criticized as showing disregard or even contempt, with analogies to invasive or predatory behavior.
• Some note precedents like Google’s _NOMAP Wi‑Fi suffix as similarly lopsided “opt-out” schemes.

Copyright, fair use, and training data

• One side claims training on scraped content is clearly fair use: models create non-expressive abstractions, are transformative, and don’t “copy” in the copyright sense.
• Others argue it should be infringement, especially as models begin to substitute for the market of original works and occasionally regurgitate them.
• Multiple people stress the law is unsettled, with many lawsuits pending; any “it’s clearly X” position is disputed.
• There is debate over analogies to humans learning from books or art:
  • Pro-AI side: learning isn’t infringement; output is only a problem if it reproduces protected expression.
  • Critical side: scale, automation, and corporate profit make this fundamentally different.

Artists’ livelihoods, styles, and compensation

• Some argue artists should be able to exclude their work and even force retraining of models that used it without consent.
• Others note that style is generally not protected, and that artists have always learned by copying others.
• Counterpoint: machines can replicate a style in days and produce near‑infinite derivatives, creating an uneven playing field and disincentivizing innovation.
• Suggested remedies include mandatory compensation schemes for training use, akin to music royalties, and updated licenses for code and writing.

Legal / policy expectations

• Several expect courts or legislatures to eventually clamp down, especially under pressure from large rights‑holders (e.g., media companies).
• Others think powerful AI firms will win favorable rules (e.g., training classified as fair use), especially if framed as essential for innovation or AGI.

Double standards & platform behavior

• Commenters highlight a perceived two‑tier system: everything online is fair game for training, but model weights and AI outputs are aggressively protected.
• Policies forbidding training on AI outputs are seen as hypocritical when those models were trained on uncredited human work.

OpenAI, AGI, and trust

• Strong distrust toward OpenAI is common: accusations of broken promises, bait‑and‑switch from “open” non‑profit roots, and prioritizing profit over creators.
• Some frame the work as so important (potential AGI, “benefit of humanity”) that copyright concerns are treated as secondary.
• Several express skepticism that current LLMs can reach AGI, noting hallucinations, lack of true understanding, and mostly incremental scaling rather than paradigm shifts.