Google's Results Are Infested, Open AI Is Using Their Playbook from the 2000s
Google’s web search is widely seen as having deteriorated into an ad- and SEO-cluttered experience, with AI Overviews often adding unreliable “slop” on top rather than improving result quality. Many commenters now turn to LLMs like ChatGPT, Claude, Perplexity, or paid engines such as Kagi for direct answers and research help, while simultaneously warning that AI outputs must be verified, are already being targeted by SEO-style manipulation, and will likely be monetized with ads just as search was. The core tension is between speed and convenience versus accuracy and trust, and whether any centralized answer engine—Google or OpenAI—can remain both useful and commercially sustainable without repeating the same enshittification cycle.
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.