The article discusses the challenges of dealing with aggressive data-scraping bots that collect information to train large language models (LLMs). It explores various strategies for mitigating their impact, such as serving them dynamic content or "garbage," which can be more efficient and cheaper than traditional anti-bot measures like blocking IPs or implementing paywalls. Ultimately, the author concludes that feeding these bots nonsensical data is a practical solution to manage server traffic without incurring significant costs.