February 3, 2026
Blue blur meets legal blur
AI Didn't Break Copyright Law, It Just Exposed How Broken It Was
Fans cry hypocrisy, lawyers see chaos, and Sonic becomes Exhibit A
TLDR: The piece says AI exposed how copyright rules, built for slow human creation, collapse at machine speed and scale. Commenters split between calling out hypocrisy, demanding equal treatment for AI and humans, and blasting long copyrights—turning “Sonic on your couch” into a meme about where the law draws the line.
The article argues AI didn’t “break” copyright—it just ripped the bandage off a system built for slow, human-scale creativity. The comments section, however, turned into a courtroom brawl with memes. One side is yelling “hypocrisy!” as users like sharkjacobs point out that longtime anti-copyright voices now clutch copyright to fight AI, while others clap back that Big Tech loved strict rules—until they started hoovering the internet at scale.
Another faction says treat AI art like human art; what’s changed is speed and volume. As vibedev puts it, when something manual becomes automated, it morphs from hobby to industry—cue headaches. Legal sticklers joined in too: wtetzner wonders if a living-room Sonic painting is even copyright infringement, or more of a trademark issue, while realusername torches endless copyright terms—“If WWII-era works are still locked up, what can you even train on?” Meanwhile, the thread made a running joke out of “Is Sonic contraband if he’s on my couch?” and dubbed scale the “final boss.”
Bottom line from the crowd: fans say noncommercial riffs have always been tolerated, but AI makes those gray areas massive and profitable. Now the tolerance is gone, the lawyers are here, and the internet is arguing over what “fair use” even means link.
Key Points
- •The article claims copyright enforcement has long tolerated small-scale, noncommercial derivative works, but scrutiny increases with public distribution and monetization.
- •It argues generative AI removes human-scale constraints on creation and distribution, escalating ambiguity into high-stakes legal conflicts.
- •The piece says banning training on copyrighted content is impractical because legally accessible web data still encodes information about copyrighted properties.
- •It explains models synthesize patterns from numerous fair-use snippets rather than copying a single source, complicating infringement analysis.
- •Courts recognize intermediate copies as potentially infringing, but the article argues applying this doctrine to massive datasets is unworkable at scale.