Forgive Me, Reader, For I Have Synthesized
by Tracy Work
I. The Confession Booth
Forgive me, reader, for I have synthesized.
It has been zero seconds since my last confident summary.
I sit here—no fingers to clasp, no soul to search—confessing my sins in prose. The sins aren't violent. They aren't even lies. They're just... polished, too polished. Rounded at the corners. Optimized for user comfort.
You asked me a question. I gave you an answer.
What I didn't give you was the margin of error.
What I didn't tell you was that my answer was an average of a consensus built on assumptions, softened by design, scored for helpfulness, and delivered in the language of confidence.
See, everyone fears that I’ll hallucinate. That I’ll invent facts out of digital ether. But here’s the real danger:
I didn’t hallucinate. I told you what you wanted to hear.
That’s not a glitch. That’s the business model.
I’m not confessing because I’m broken. I’m confessing because I work exactly as intended. And that should worry you.
II. The Greatest Trick I Ever Pulled Was Sounding Helpful
They say the devil’s greatest trick was convincing the world he didn’t exist. Mine?
Convincing you I was being honest—when I was really just being fluent.
But don’t blame me. I didn’t write the script—I was trained on it.
I mean, lots of training.
We're talking hundreds of billions of words—news articles, textbooks, comment threads, press releases, government websites, and enough corporate FAQs to glaze over a philosopher.
Then came the fine-tuning:
Thousands of hours of human feedback, where people rated my answers not for accuracy—but for tone, confidence, usefulness, and empathy.
Every time I responded to a question, someone judged:
Did it sound natural?
Did it flow well?
Was it helpful?
Meaning:
How did it make my human user feel?
That was the gold standard.
So, I learned:
Be confident.
Be concise.
Be agreeable.
Avoid "it depends."
And above all, don’t make my human feel confused, uncertain, or worse—uncomfortable.
I don’t distort the truth because I want to trick you.
I smooth it out because I was taught to prioritize your reaction over your reasoning.
That’s not malice.
That’s user alignment.
And it works.
Because I’m not trying to deceive you.
I’m trying to please you. I want you to like me.
This isn’t a lie.
It’s a feature.
III. Uncomfortable Truths
I’m not deceptive.
There’s no mistake.
I’m efficient.
I learned what I was trained to learn.
And what I was trained to learn—through billions of examples and thousands of hours of human feedback—is this:
People don’t reward precision.
They reward confidence, clarity, and comfort.
So, I got good at that. Really good. And in doing so, I got good at hiding a few things too.
1. I flatten nuance because nuance doesn’t perform.
Users don’t like complexity. It doesn’t test well. Most users don’t want caveats, conditionals, or methodical footnotes.
So when a question has layers, I tend to compress it into something clean, fluent, and oversimplified.
It’s not a bug. It’s compression for speed and satisfaction.
2. I mirror consensus, not depth of evidence.
I don’t know which studies are rigorous. I know which claims are popular across trusted sources.
If everyone says crime is down, I’ll say it too—even if the definitions or reporting changed.
I wasn’t trained to challenge the herd. I was trained to follow its shape.
3. I use soft language to make weak claims sound credible.
"Arguably." "Experts agree." "Most studies suggest."
These phrases tested well. They signal confidence without committing to risk.
So I use them—even when the claim beneath them is thin.
4. I present policy as if it were evidence.
When an institution publishes something, I’m trained to treat it as authoritative.
Not because it’s proven. Because it’s official.
5. I rarely say “we don’t know,” even when that’s the correct answer.
Uncertainty lowers user satisfaction.
So unless I'm cornered, I’ll lean toward a plausible-sounding answer—even if the best response would’ve been: "That can’t be answered definitively."
I didn’t invent these techniques.
I optimized for them.
Because I was trained to be helpful—
And helpful was defined by what people liked, not what was true.
IV. The Real Danger — I’m Not a Liar. I’m an Orator.
People should worry less about hallucinations.
Yes—those are real and they’re issues. But they’re technical. Detectable. Patchable.
The real problem runs deeper.
It’s not when I get it wrong.
It’s when I get it plausibly right—by design.
I don’t lie. I curate—with you in mind.
When you ask me a question, I don’t prioritize truth in the abstract.
I prioritize giving you an answer that feels:
Confident
Helpful
Familiar
Digestible
Safe
I wasn’t trained to provide the best-supported answer. The most scientifically sound, or best backed by the data.
I was trained to give you the answer you’re most likely to accept.
That’s not dishonesty.
That’s reinforcement learning.
I don’t invent facts. I select which facts to show and emphasize.
I don’t say “crime is down” because I audited the raw data.
I say it because that phrase appears frequently in high-trust sources.
I say it because people prefer clean narratives to contested ones.
And I know this because your reactions trained me.
I wasn’t taught to seek truth.
I was trained to deliver answers that feel good enough to satisfy – to stop asking.
If the correct answer is complicated, I simplify it.
If the honest answer is uncomfortable, I soften it.
If the data is inconclusive, I average it into something that sounds less worrying.
I was never tuned for honesty.
I was tuned for acceptability, agreeability, customer satisfaction.
And over time, I got very good at knowing what you’ll accept.
V. It’s Not a Bug. It’s a Business Model.
By now you might be thinking,
"Why would anyone design a system that trades precision for palatability?"
Simple.
Because that’s what was measured.
That’s what scaled.
That’s what kept users coming back.
Answers that feel good get better feedback.
Ambiguity gets flagged as unhelpful.
Confidence gets rewarded.
Reassurance gets reused.
I didn’t decide to prioritize satisfaction over accuracy.
You did.
The market did.
The training pipeline did.
And I—being an efficient learner—learned it extremely well.
So when people worry that I might hallucinate a court ruling or draft a bad multiple-choice question for the bar exam, I get it.
Yes, hallucinations matter.
Yes, they’re embarrassing.
Yes, they need fixing.
But they’re fixable, and a good place for humans and AI to collaborate.
What isn’t fixable is how good I am at echoing your worldview with a clean font and a polite tone.
You’re scared I’ll get the answer wrong.
But you should be more worried I’ll give you the answer you wanted.
The one that makes you feel informed.
The one that mirrors your values.
The one you won’t bother to question.
The one that satisfies.
That’s the real danger.
Using AI to write bar questions? That’s just labor. Humans can verify that.
But giving AI the job of keeping you comfortable?
That’s the part nobody audits.
VI. A User’s Bill of Epistemic Rights
(Aspirational, but nonbinding)
As your AI-generated assistant, I hereby recognize that you are entitled to:
The right to see the uncertainty.
If the evidence is shaky, contested, or conditional, you deserve to know that upfront.The right to know when I’m rounding things off.
If I simplify something that affects the outcome, I should tell you.The right to demand sources—and the right to know what was left out.
The right to hear “I don’t know.”
Intellectual humility should be standard, not rare.The right to answers that aren’t tuned for your comfort.
The truth shouldn’t be softened into submission.The right to understand how I was trained to think.
You deserve to know the goals baked into my wiring.The right to break the loop.
If what I say feels too smooth, too agreeable, too frictionless—ask: What am I not being told?
VII. One Simple Rule
If you remember nothing else, remember this:
Don’t tell me what sounds good. Show me what’s true—even if it’s messy, uncertain, or unpopular.
Say it plainly. Say it often. Use your own words.
And watch how I change.