this post was submitted on 12 Feb 2026
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Fuck AI

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AI, in this case, refers to LLMs, GPT technology, and anything listed as "AI" meant to increase market valuations.

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Because they don't really search or index quality content (it's very expensive and hard to do) and their search implementation really sucks, they don't do any real improvement.

The process is like this:

  1. Take the user query and create 1-3 queries. For this process they use very stupid but fast and cheap models; because of that, sometimes they create very stupid search queries and, unlike a pro, they don't really know how to use search engines, like filtering, ranking, focusing...
  2. Combine these search results (it contains slop AI-generated summary pages, YouTube videos, maybe forums, maybe Wikipedia...).
  3. Use RAG with an LLM to find answers. LLMs will always try to find answers quickly, and instead of making a thinking loop in a long article they will use that slop page with a direct answer.

As you can see, there are many, many problems in this implementation:

  • The biggest problem is citation: they cite confidently but it's wrong.
  • They use low-quality data, like auto YouTube subtitles, improperly extracted tables and elements, content-farm sites, copycat sites, corporate blogs...
  • Their search results are low quality.
  • For the most important part (breaking down the user request) they use cheap, stupid models.
  • They handle all data in the same context instead of parallel requests (which is very expensive)

It's still strange to me: we always say "they have all the data, all the money, all the hardware..." but they still can't create a better AI search than random FOSS developers.

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[–] Zerush@lemmy.ml -2 points 4 weeks ago

Agree with it, despite using an AI search since almost 4 years, Andisearch, because it's the only one I tested with a pretty good accuracy (~90%). Anyway, it's always needed to contrast the information in the web, independent of results from AI or not, a lot of BS out there.

To this topic Andi said:

Based on recent examples from 2025-2026, AI search engines frequently provide confident but incorrect answers, demonstrating several key problems:

  1. Inconsistent results - Siri with Apple Intelligence gives different wrong answers to the same question when asked multiple times[^8].

  2. False confidence - AI provides detailed but completely incorrect information, like Google AI claiming a South Dakota team won North Dakota's championship[^8].

  3. Regression in quality - Traditional search results often work better than AI versions. As John Gruber notes, "old Siri... at least recognizes that Siri itself doesn't know the answer and provides a genuinely helpful response"[^8].

  4. Poor accuracy even on popular topics - Siri achieved only a 34% accuracy rate when asked about Super Bowl winners, with one stretch of 15 wrong answers in a row[^8].

The core issue appears to be that AI search engines prioritize providing definitive-sounding answers over accuracy, making them less reliable than traditional search results that simply link to authoritative sources.

[^8]: Daring Fireball - Siri Is Super Dumb and Getting Dumber