Google's Results Are Infested, Open AI Is Using Their Playbook from the 2000s

Original Article ↗ Hacker News Discussion ↗

Perceived decline of Google Search

  • Many see modern Google Search as “enshittified”: more ads, SEO sludge, and UI clutter vs early-2000s fast, relevant, link‑oriented results.
  • Complaints include: AI overviews pushed on users, difficulty forcing literal queries, poor handling of code identifiers, and infested “best X” affiliate listicles.
  • Some argue the web’s underlying content degraded; others say Google’s ad incentives and product decisions actively caused that degradation.

AI Overviews and LLM Search: Helpfulness vs. Risk

  • A minority likes Google’s AI Overview as a way to skip ads and junk and get quick summaries (e.g., Bluetooth pairing steps).
  • Many report frequent, confident wrong answers, especially on technical, medical, and numerical topics (e.g., child vomiting diet, LD50 of caffeine, calorie recommendations, fictional movie sequels, passport rules).
  • Core tension: AI is often “good enough” for trivial queries but dangerously opaque and fallible for high‑stakes ones.

Trust, Hallucinations, and Verification Cost

  • Users accept “bullshit” from standalone chatbots more readily than from Google’s top results; Google is held to a higher standard.
  • Verifying AI answers can take as long as solving the problem directly, undermining the supposed time savings.
  • Some note that web-search-enabled LLMs can provide sources, but citations are sometimes fabricated or misrepresent the linked page.

Impact on the Web Ecosystem and Creators

  • Content creators describe AI summaries as “plundering” their work: extracting answers, stripping traffic, and weakening incentives to publish high‑quality guides.
  • Affiliate‑funded sites are seeing traffic drops as AI overviews answer queries without clicks, threatening business models that relied on organic search.

SEO, Advertising, and “Dark Google”

  • SEO is framed as a coordinated “dark” ecosystem gaming search and now aiming to poison LLM training data to bias brand mentions.
  • Several argue Google profits from low‑quality, ad‑heavy SEO sites via its ad network, so it has weak incentives to truly fix spam.
  • Widespread expectation that AI search (from Google or OpenAI) will eventually be monetized with embedded or blended ads, repeating search’s trajectory.

Alternatives and Coping Strategies

  • Many report partial migration to Kagi, Perplexity, Brave Search, DuckDuckGo, or local/open‑source LLMs; none are seen as perfect.
  • Workarounds include: appending “reddit”/“wikipedia” to queries, using separate tools for “search vs answer,” uBlock filters to hide AI, or using LLMs only for brainstorming.
  • LLMs are praised for “fuzzy recall” tasks (finding half‑remembered quotes, books, films, games) but also shown to fail badly on similar prompts.