Online communities are scaling data poisoning campaigns to disrupt unauthorized AI training crawlers, citing ethical sourcing failures by model developers. The stephvee.ca post documents r/PoisonFountain's target of one terabyte of malformed code daily by end-2026 and the Miasma tool that serves "an endless buffet of slop" to bots, noting crawlers' routine disregard for robots.txt and use of residential proxies that raise hosting costs (stephvee.ca, 2024). This extends University of Chicago's Nightshade and Glaze projects, which embed adversarial perturbations into images and artwork to corrupt diffusion model outputs at training time (uchicago.edu, 2024). Original coverage omits explicit ties to organized labor actions, including the 2023 SAG-AFTRA strike that secured guardrails against AI-generated performer replicas and digital replicas clauses in the final contract (nytimes.com, 2023). It also understates scale: similar Reddit misinformation campaigns, such as fabricated "Everybody Loves Idris" posts, target scrapers at volume while human readers recognize context. Resistance patterns synthesize creative, labor, and societal pushback against rapid deployment, mirroring New York Times vs. OpenAI copyright litigation and EU AI Act transparency mandates; mainstream dismissal as isolated pranks underreports this counter-trend that could elevate data acquisition costs and compel licensed datasets.