Bad Actors Are Grooming LLMs to Produce Falsehoods

Reliability, “Off-by-One” Errors, and Erosion of Truth

• A major concern is “off‑by‑one” style errors: answers that are almost correct but wrong in subtle ways, which most users won’t notice.
• As LLMs replace search or act as research agents, this risks quietly corrupting shared factual baselines (dates, laws, history, technical details).
• People already tend to treat computer output as authoritative; combining that with models that are “mostly right” but unverifiable by laypeople is seen as dangerous.

Propaganda, Source Credibility, and Epistemology

• Commenters argue it’s hard for LLMs to distinguish propaganda from truth because that distinction is often ideological and shifting over time.
• Some think the minimum is for models to honor their own knowledge of source credibility (e.g., not treating known disinfo networks as reliable).
• Others note that “how to discern truth” is an old epistemic problem, not unique to AI; filtering out harmful sources is something humans already do.
• There’s discussion of “firehose of falsehood” strategies, and that flooding LLM training/search corpora is a logical next step for state and commercial propagandists.

Trust, Bubbles, and Societal Impact

• One camp hopes that visible AI failures will accelerate public distrust and pop the “AI bubble”; another points out tabloids and partisan media show that many never lose trust in congenial sources.
• Some foresee LLMs intensifying filter bubbles via personalized models that mirror users’ ideological preferences, reinforcing existing divisions.
• Others argue humans have always self‑selected bubbles; LLMs and social media just scale the effect.

Utility vs Harm: Deep Split Among Users

• There is a sharp divide:
  • Critics: LLMs are “bullshit generators,” worsening the web’s signal‑to‑noise, adding confident errors, and encouraging intellectual laziness.
  • Supporters: they report substantial productivity and convenience gains (coding help, search, summarization, how‑to learning, casual conversation).
• Several note widespread “good enough” attitudes: many users don’t verify outputs and don’t prioritize precision unless stakes are high.

Limits of Current Models and Article Framing

• Multiple commenters stress that LLMs don’t “know” or “reason”; they pattern‑match text. Expecting them to “put two and two together” about propaganda is seen as anthropomorphism.
• Evaluating models against fixed truth/falsehood lists risks training them into ideological sycophants rather than critical reasoners.

Proposed Responses and Future Risks

• Suggested mitigations include: curated “high‑quality” source sets, Web‑of‑Trust‑style reputation systems, explicit source tracing for each fact, and separate filtering of search results before model consumption.
• Others think no technical fix can replace cultural changes: widespread skepticism toward anything on a screen and better media literacy.
• Several predict “LLM grooming” will become the new SEO/advertising game: brands, propagandists, and scammers optimizing content specifically to steer model outputs.