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markdown/LLMs, LRMs, and Scraping Are Killing the Internet and Must Stay in the Lab.md
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@ -36,8 +36,6 @@ We’re not tweaking a buggy app; we’re wielding tech that could reshape civil
Anthropic’s red-teaming experiments, breaking models to study flaws, are a Band-Aid on a severed artery. We’re light-years from cracking the black box. A January 2025 McKinsey report calls LLMs “black boxes” lacking transparency, eroding trust in critical tasks [McKinsey, 2025](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work). A March 2025 IBM article stresses that without traceability, risks like data leakage escalate [IBM, 2025](https://www.ibm.com/think/insights/ai-agents-2025-expectations-vs-reality).
A 2024 *ScienceDirect* survey on LLM safety identifies explainability challenges as a core barrier, noting that opaque models are prone to misinformation and inference privacy breaches [ScienceDirect, 2024](https://www.sciencedirect.com/science/article/pii/S2666659024000130). If the industry’s moral compass is this lost, deploying AI in production is a crime against humanity.
## Web Scraping’s Reign of Terror
The AI industry’s data addiction is a digital plague, and web scraping is its weapon. An X post glorifying Scrapy, a Python framework with over 55,000 GitHub stars, exposes the truth: the industry is waging war on the internet [Scrapy Post, 2025](https://x.com/birgenbilge_mk/status/1930558228590428457?s=46). Scrapy’s “event-driven architecture” and “asynchronous engine” hammer servers with hundreds of simultaneous requests, ripping data at breakneck speed. Its CSS/XPath selectors and JSONL exports make it a darling for LLM pipelines.