SlopStop: Community-driven AI slop detection in Kagi Search

Kagi’s AI Philosophy and User Control

• Many commenters like that Kagi’s AI summaries are opt‑in (e.g., only when adding “?”) and can be fully disabled; this is framed as “our AI, under your control” rather than forcing AI answers.
• Others call out perceived hypocrisy: Kagi News / Kite use LLMs to summarize news without obvious on‑page disclosure; several argue all AI usage should be clearly labeled per article.
• One example of a bad “AI summary” (apparently just scraped text, including an old HTML comment) leads some to doubt it was LLM‑generated at all, suggesting crude extraction or even manual work.

What Counts as “Slop”?

• Disagreement whether “slop” == “any AI content” or “low‑value, deceptive AI spam.”
• Kagi’s own framing (from staff in the thread):
  • 1. Not AI & Not Slop (good)
  • 2. Not AI & Slop (SEO spam)
  • 3. AI & Not Slop (high‑effort, human‑accountable AI use)
  • 4. AI & Slop (most garbage)
       Current focus: labeling AI vs not, then downranking obvious slop.
• Some insist “there is no good AI content”; others cite useful cases: translation, ESL polishing, high‑effort channels/newsletters, docs tied closely to code, bespoke research notes.

Trust, Disclosure, and Human vs Machine

• Strong current that undisclosed AI use is inherently deceptive and thus “slop,” regardless of surface quality.
• Several care about authorship and lived experience as part of value (“AI has never ridden a bike or sailed at sea”), even if text is accurate.
• Others say they don’t care about origin if content is correct, insightful, and clearly sourced; the real problem is unreviewed, hallucination‑prone output flooding the web.
• Broader fear: erosion of trust in blogs and web content generally, making it harder for new human authors to gain an audience.

Technical Approach to Slop Detection

• Kagi’s ML lead explains they lean on side‑channel signals more than pure text classification:
  • Domain‑level patterns, posting frequency, page formats, plugins, trackers/JS weight, link graphs, channel behavior.
  • Rollups at domain/channel level to scale; bias toward false negatives to avoid harming legitimate sites.
• Image/video slop: current models detect diffusion/GAN artifacts reasonably well; text detection via perplexity alone is weak.
• Multiple commenters describe this as an arms race akin to CAPTCHAs or GANs: generators will adapt to detectors; purely content‑based detection is likely doomed long‑term.

Crowdsourcing, Abuse, and the “Slop Wars”

• Kagi’s “SlopStop” starts as community‑driven: users report, a small trusted group reviews with tooling, then signals feed ranking.
• Concerns raised about brigading and “false AI accusations” as a new attack vector, especially on contentious topics or competitors.
• Some see value in adding “AI slop” as a report reason across forums and social sites; others warn that volunteer moderation is easily captured or gamed.

State of the Web and Search

• Widespread frustration with LLM‑generated SEO sites, multi‑paragraph filler for one‑sentence answers, and product‑review spam (including astroturfed Reddit threads showing up in search).
• Many praise Kagi as a paid, calmer alternative to ad‑driven search, but doubt any system can fully stop increasingly human‑like AI slop.
• Broader anxiety: if search engines and the open web drown in AI sludge, people will retreat to closed LLMs as arbiters of truth, privatizing knowledge and amplifying hallucinations.