The end of the rip-off economy: consumers use LLMs against information asymmetry

Access and meta-discussion

• Some commenters struggled to access the article via archive sites due to VPN/DNS blocking, sharing hosts‑file workarounds and noting that archive services appear to track reader locations.

Optimism: LLMs as an anti–rip‑off tool

• Several people reported strong practical wins:
  • Using LLMs to navigate airline regulations and extract €500‑scale compensation across multiple jurisdictions and carriers.
  • Having models explain medical procedure codes and pricing, or check bills for errors.
  • Parsing complex employment contracts (multi‑language, conflicting clauses, hidden penalties) and spotting traps.
  • Understanding government benefit systems and care options for relatives.
  • Decomposing home repairs/renovations, gas/electrical work, or contractor quotes into steps and costs to negotiate more confidently.
  • Debunking “BS” consumer products (e.g., skincare) by interpreting ingredient lists.
• Some argue that LLMs mainly raise the floor of consumer competence: you don’t need perfect answers, just enough structure and vocabulary to resist obvious scams and opacity.

Skepticism: arms race and corporate capture

• Many doubt the effect will last. They expect a repeat of SEO and reviews:
  • Companies poisoning training data, astroturfing forums, or buying “answer placement” so models subtly push their products.
  • Free assistants becoming ad‑driven and manipulated; high‑end, “loyal” agents reserved for wealthy users.
  • Vendors deploying stronger, specialized LLMs for negotiation and pricing, keeping their information advantage.
• Some see LLMs already being tuned for integrations (e.g., surfacing booking partners in language‑learning queries).

Reliability, cognition, and information quality

• Commenters stress that LLMs aren’t “a genius in your pocket”: 95%‑correct advice can be dangerous (e.g., electrical work), and plausible language encourages uncritical acceptance.
• There is concern that heavy LLM use makes people less inclined to think or write for themselves, accelerating a “dark age” of shallow understanding.
• Others note that the web itself is now heavily polluted with AI‑generated slop, fake reviews, and bots on platforms like Reddit, which feeds back into model quality.

Labor markets and “loyal agents”

• In hiring, LLM‑assisted applications and interview cheating are creating an arms race; companies respond with onsites and proctoring.
• A research effort on “loyal agents” is mentioned, aiming to define and enforce AI agents that are verifiably aligned with the user rather than advertisers or platforms.