snxraven/ravenscott-blog
1
0
Fork 1
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update

Browse Source
This commit is contained in:
Raven Scott 2025-06-08 02:28:56 -04:00
parent 8ca84c1684
commit 9602893cf9
1 changed files with 3 additions and 28 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

29
markdown/LLMs, LRMs, and Scraping Are Killing the Internet and Must Stay in the Lab.md
View File

@ -14,8 +14,6 @@ The AI landscape in 2025 is a dystopian fever dream, a chaotic gold rush where t
The transformer architecture, a statistical trick for predicting text, has been inflated into a godlike entity, worshipped with fanatical zeal while ignoring the wreckage it leaves behind. This obsession with scale is collective madness. Models are trained on datasets so colossal - trillions of tokens scraped from the internet’s cesspool, books, and corporate sludge - that even their creators can’t untangle the mess.
Every company, every startup, every wannabe AI guru is unleashing armies of scrapers to plunder the web, hammering servers and destabilizing the digital ecosystem. High-profile failures - like Samsung banning ChatGPT after code leaks, Google’s Bard hallucinating, Zillow’s AI pricing flop costing millions, and IBM Watson Health’s erroneous cancer recommendations - underscore the chaos [Lakera, 2024](https://www.lakera.ai/blog/risks-of-ai). We’re not building progress; we’re orchestrating a digital apocalypse.
## Apple’s *Illusion of Thinking*: A Flamethrower to AI’s Lies
Apple’s June 2025 paper, *The Illusion of Thinking*, is a flamethrower torching the AI industry’s lies. Authors Parshin Shojaee, Iman Mirzadeh, and their team devised ingenious puzzle environments to test LRMs’ so-called reasoning, demanding real problem-solving, not regurgitated answers. The results are a flaming middle finger to every AI evangelist.
@ -24,9 +22,7 @@ LRMs breeze through simple tasks but implode spectacularly on complex ones, spew
These models are erratic, nailing one puzzle only to choke on a near-identical one, guessing instead of reasoning, their outputs as reliable as a drunk gambler’s dice roll. Humiliatingly, basic LLMs often outshine LRMs on simple tasks, with the “reasoning” baggage slowing them down or causing errors. Why are we worshipping a downgrade?
The “thinking processes” LRMs boast are a marketing stunt, revealed by Apple as a chaotic mess of incoherent leaps, dead ends, and half-baked ideas - not thought, but algorithmic vomit. LRMs fail to use explicit algorithms, even when essential, faking it with statistical sleight-of-hand that collapses under scrutiny. This brittleness isn’t theoretical: IBM Watson Health’s cancer AI made erroneous treatment recommendations, risking malpractice, and Google’s Bard hallucinated inaccurate information [Lakera, 2024](https://www.lakera.ai/blog/risks-of-ai).
A January 2025 McKinsey report notes that 50% of employees worry about AI inaccuracy, 51% fear cybersecurity risks, and many cite data leaks, aligning with Apple’s findings of unreliable outputs [McKinsey, 2025](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work). This isn’t a warning - it’s a guillotine.
The “thinking processes” LRMs boast are a marketing stunt, revealed by Apple as a chaotic mess of incoherent leaps, dead ends, and half-baked ideas - not thought, but algorithmic vomit. LRMs fail to use explicit algorithms, even when essential, faking it with statistical sleight-of-hand that collapses under scrutiny.
## Amodei’s Confession: We’re Flying Blind
@ -34,8 +30,7 @@ If Apple’s paper lit the fuse, Dario Amodei’s essay is the explosion that le
We’re not tweaking a buggy app; we’re wielding tech that could reshape civilization, navigating it with a Magic 8-Ball. Amodei’s dream of an “MRI on AI” is a desperate cry, not a roadmap. He admits we can’t explain why a model picks one word or makes an error. This isn’t like not knowing a car’s engine - you can still drive. It’s like not knowing why a nuclear reactor keeps melting down, yet firing it up.
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).
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.
## 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.
@ -68,8 +63,6 @@ Amodei’s right: we can’t explain a damn thing these models do. If an AI deni
Benchmarks are a con, rigged because models train on test problems, faking genius. Apple’s puzzles exposed the truth: in the real world, they’re clueless. Deploying based on fake scores is fraud. LLMs, built on stolen data, spew bias, lies, and hate. Scaling that is weaponizing chaos - an AI newsroom churning propaganda, a hiring tool blacklisting groups, a legal bot fabricating evidence. This is how civilizations rot.
LRMs and scraping guzzle resources. Apple proved extra “thinking” is hot air; Scrapy’s server attacks burn more. Training one model or running scraping pipelines emits CO2 like a coal plant, strangling the planet for garbage tech. A January 2025 McKinsey report notes 15% of employees worry about AI’s environmental impact [McKinsey, 2025](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work).
The AI hype train is a cult, brainwashing us into worshipping statistical parrots as gods, screaming “reasoning!” and “intelligence!” when it’s all lies, driving us off a cliff. Real-world nightmares abound: an LRM in a self-driving car misreads an intersection, causing a pileup killing dozens; an AI teacher misgrades exams, ruining futures; a power grid AI miscalculates, triggering blackouts for millions. These are headlines waiting to happen.
Outsourcing decisions to AI strips human agency, turning us into drones who can’t question or innovate - not augmenting humanity but lobotomizing it. Production use breeds dependence. When LLMs fail, systems collapse - hospitals halt, markets freeze, supply chains implode, leaving us one glitch from anarchy. LLMs craft lies that fool experts. In production, they could sway elections, manipulate juries, or radicalize masses - we’re not ready for AI-powered propaganda.
@ -78,9 +71,6 @@ Governments are asleep, with no framework to govern AI or scraping’s risks, no
Autonomous AI in critical systems, powered by flawed LRMs, is a death sentence - Apple’s research shows failures go unchecked without human oversight, amplifying harm exponentially. Scraping’s data theft, glorified by the X post, steals from creators, undermining the web’s creative ecosystem. Deploying LLMs built on this is endorsing piracy at scale. Scraping’s server attacks are killing the open web, forcing websites behind paywalls or offline, shrinking the internet’s diversity. LLMs are complicit in this murder.
Scraped data fuels LLMs that churn soulless text, drowning human creativity and turning culture into algorithmic sludge, disconnecting us from authenticity A 2020 Harvard Gazette report notes that AI’s lack of oversight risks societal harm, with regulators ill-equipped to keep pace [Harvard Gazette, 2020](https://news.harvard.edu/gazette/story/2020/10/ethical-concerns-mount-as-ai-takes-bigger-decision-making-role/).
## Toxic Incentives: Profit Over Existence
This insanity is driven by perverse incentives. Venture capitalists demand unicorn returns, so companies rush half-baked models and scraping pipelines to market. OpenAI’s profit-chasing pivot, as Amodei criticized, is the blueprint for this rot. Safety, ethics, and infrastructure are roadkill under “move fast and break things.”
@ -112,19 +102,4 @@ These steps aren’t optional - they’re the only way to save ourselves from th
The AI industry’s peddling a fairy tale, and we’re the suckers buying it. LLMs and LRMs aren’t saviors - they’re ticking bombs wrapped in buzzwords, built on a dying internet’s ashes. Apple’s *The Illusion of Thinking* and Amodei’s confession are klaxons blaring in our faces. Scrapy’s server-killing rampage, glorified on X, is the final straw - we’re not just risking failure; we’re murdering the digital world that sustains us.
From high-profile deployment failures - Samsung, Google, Zillow, IBM - to the ethical quagmire of web scraping, from AI’s environmental toll to its persistent opacity, the evidence is overwhelming. IBM warns of escalating risks like data leakage [IBM, 2025](https://www.ibm.com/think/insights/ai-agents-2025-expectations-vs-reality). Lakera documents privacy violations from scraping, amplifying harm [Lakera, 2024](https://www.lakera.ai/blog/risks-of-ai). This isn’t a mistake - it’s a betrayal of humanity’s trust.
Deploying LLMs and LRMs, fueled by scraping’s destruction, isn’t just dumb - it’s a crime against our survival. Lock them in the lab, crack the code, and stop the internet’s slaughter, or brace for the apocalypse. The clock’s ticking, and we’re out of excuses.
## Sources
- Shojaee, Parshin, et al. “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity.” Apple Machine Learning Research, June 2025, https://machinelearning.apple.com/research/illusion-of-thinking.
- Amodei, Dario. “Essay on AI Interpretability.” Personal website, 2025, quoted in Futurism, https://futurism.com/anthropic-ceo-admits-ai-ignorance.
- Anonymous. “The web scraping tool Scrapy.” X post, 2025, https://x.com/birgenbilge_mk/status/1930558228590428457?s=46
- Lakera. “AI Risks: Exploring the Critical Challenges of Artificial Intelligence.” 2024, https://www.lakera.ai/blog/risks-of-ai.
- McKinsey & Company. “AI in the workplace: A report for 2025.” January 2025, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work.
- IBM. “AI Agents in 2025: Expectations vs. Reality.” March 2025, https://www.ibm.com/think/insights/ai-agents-2025-expectations-vs-reality.
- Simplilearn. “Top 15 Challenges of Artificial Intelligence in 2025.” May 2025, https://www.simplilearn.com/challenges-of-artificial-intelligence-article.
- EPIC. “Scraping for Me, Not for Thee: Large Language Models, Web Data, and Privacy-Problematic Paradigms.” February 2025, https://epic.org/scraping-for-me-not-for-thee-large-language-models-web-data-and-privacy-problematic-paradigms/.
- arXiv. “Ethical and social risks of harm from Language Models.” 2021, https://arxiv.org/abs/2112.04359.
- Harvard Gazette. “Ethical concerns mount as AI takes bigger decision-making role.” 2020, https://news.harvard.edu/gazette/story/2020/10/ethical-concerns-mount-as-ai-takes-bigger-decision-making-role/.