Guarding the Gates of Knowledge

What happened

A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion. Remember the good old days of GPT-3 when we could ask the chatbot anything and it would almost instantly and very confidently spit back a plausible but false answer?

What the evidence says

About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion. Six years and several versions of a few different chatbots later, I still can’t trust the answers I get, even though Claude or Gemini often do useful work for me.

Evidence boundary

This establishes what the linked source reported. It does not, by itself, establish every causal or psychological interpretation that could follow.

Why it matters psychologically

Trust is part of the mechanism: people must infer credibility, intention and social meaning before the technology can influence behaviour. Identity becomes relevant when the system changes how people describe themselves, compare themselves with others or interpret feedback about who they are. For this report, the strongest PSYTECH reading is therefore the one tied to a measurable human outcome, not technological novelty on its own.

What remains uncertain

Platform-level patterns do not automatically show how an individual understood, trusted or acted on the information. Direct human outcome data would strengthen the interpretation.

Primary source

- Guarding the Gates of Knowledge — IEEE Spectrum. URL checked 2026-11-02.