The librarian immediately attempts to sell you a vuvuzela

Original Article ↗ Hacker News Discussion ↗

Ad Pollution and LLM Manipulation

  • Strong expectation that LLMs will be monetized with ads and product bias, just as web search was.
  • Multiple mechanisms discussed:
    • Selling access to system prompts or first-message “ad tech” layers.
    • Auctioning token-level influence during generation.
    • Training-data poisoning by brands for long-term, subtle favoritism.
  • Fear that ads will be indistinguishable from genuine advice; “native” or subliminal bias toward certain products, architectures, or vendors (e.g., cloud patterns, frameworks).
  • Some note advertisers already target LLMs via SEO-like strategies and content mills; “GEO over SEO” is mentioned.

Search Engines, SEO, and “Dead Web”

  • Many describe Google as increasingly unusable: ignores exact phrases and operators, replaces terms with “synonyms,” overintegrates ads with results.
  • Nostalgia for Altavista and early Google where precise queries worked and ads were clearly separated.
  • SEO and now LLM-generated spam flood results, especially for commercial/product queries; some feel half or more of results are machine-written listicles.
  • Workarounds: multiple engines (DDG, Bing, Kagi), site-specific shortcuts, uBlacklist, domain-level blocking.

LLMs as a Replacement / Complement to Search

  • Some avoid LLMs for search due to hallucinations and staleness; others say LLMs massively improve learning and manual-like tasks.
  • Concern that LLM web-browsing just launders SEO spam from existing search.
  • Interest in specialized and local models (e.g., game-playing, niche domains) and in a “GNU GPT”–style factual, libre model.

Economic Incentives, Enshittification, and Dependence

  • Heavy AI investment seen as guaranteeing a push for extreme monetization: subscriptions + ads + enterprise licensing.
  • Parallel drawn to cable TV, streaming, and search: start clean, then gradually “enshittify.”
  • Scenario sketched where industries become dependent on “vibe coders” who can’t function without LLMs; once locked in, prices spike and weaker firms collapse.
  • Debate over ad-powered vs public or subscription models; many see AI as a symptom of broader capitalist incentive problems.

Public / Library-Like Alternatives

  • Desire for a “library web” or EU-style public search+AI: ad-free, curated, with spam-resistant ranking (downweight ad-heavy sites, upweight trusted institutions).
  • Counterpoint: public institutions can be underfunded, politically steered, or corrupt; contractors may reintroduce ads.

Environmental and Societal Externalities

  • Disagreement over how “massive” AI’s environmental harm is, but acknowledgment of large training and data-center energy use and water impact.
  • Some argue efficiency gains might offset costs; others stress that constant retraining and growth keep the footprint rising.

Overall Mood

  • Strong mix of nostalgia, frustration, and fatalism about both web search and future LLMs.
  • Pockets of optimism around open models, paid niche search (e.g., Kagi), and real-world libraries as remaining “librarians who don’t sell you vuvuzelas.”