How AI Misrepresents Creators: A Case Study From The Honest Audiophile

Artificial intelligence is becoming a bigger part of online conversations every day — especially when creators use AI tools anywhere in their workflow. But there’s a problem most people never see:

AI systems can misrepresent creators simply because of how they’re asked.

Not because they’re malicious. Not because they’re trying to deceive anyone. But because of how they actually work.

This week, I ran a small experiment with Google Gemini. I asked it to “deep research” The Honest Audiophile. The result was a twelve‑page document filled with confident claims — some accurate, some speculative, and some completely invented.

The response was so large that I converted it into a Google document for easier reading:

Google Doc: https://docs.google.com/document/d/1iwOqd8p9wu8VXgqnqxVASfTihzmHa8Ubso_l2RqrURg/edit?usp=sharing

Then, the next morning, I asked a similar question — but with a calmer, clearer frame.

Suddenly, Gemini’s tone changed. The drama disappeared. The speculation vanished. The explanation became accurate.

Nothing about my workflow changed. Only the prompt did.

That contrast — the difference between yesterday’s Gemini and today’s Gemini — is the perfect example of how AI blends truth, rumor, and prediction into something that sounds authoritative but isn’t grounded in reality.

This article breaks down why that happens, what it means for creators, and why AI literacy matters more than ever. To understand why the two Gemini responses were so different, we need to look at what AI actually produces.

What AI Actually Produces: A Blend of Three Ingredients

Gemini’s long write‑up about my channel wasn’t “research.” It was a collage of three things.

1. Accurate Facts

These came directly from my own content:

• my review process

• my reference chain

• my background in live sound

• my grading system

• my IEM and headphone preferences

These parts were correct because they came from me.

2. Reddit Narratives

Gemini also pulled in assumptions and drama that circulate online. For example:

“The transparency crisis surrounding his AI usage…”   — Gemini “research”

This never happened — but it’s a common Reddit talking point, so Gemini treated it as fact.

3. LLM Filler

This is the academic‑sounding fluff that makes AI text feel authoritative:

“The psychoacoustics of trust…”   — Gemini “research”

Or:

“A decentralized sociotechnical ecosystem…”   — Gemini “research”

These phrases sound impressive, but they don’t mean anything. They’re padding.

Put together, the result is a document that feels polished but is really a mixture of:

• real information

• internet speculation

• AI‑generated embellishment

AI Will Repeat Misinformation If It’s Popular Enough

One of the clearest examples was the section about AI usage in my writing.

Gemini confidently stated:

“Trust erosion and damaged reputation…”   — Gemini “research”

None of this happened. But because Reddit repeated it enough times, Gemini assumed it must be true.

Here’s the interesting part:

When I gave Gemini my actual article showing my workflow — raw text, Copilot edits, and final version — it immediately corrected itself.

For reference, here’s the article I provided:

An Example of How I Use Copilot:   https://www.thehonestaudiophile.com/post/an-example-of-how-i-use-copilot

It accepted the truth because I provided a primary source.

That’s the key lesson:

AI doesn’t know the truth. It knows the conversation.

AI Cannot Detect Authorship or Intent

Another common misconception is that AI can “detect” whether something was written by a human or a model.

It can’t.

AI does not:

• analyze writing style

• understand voice

• evaluate nuance

• determine authorship

• know who wrote what

It simply predicts patterns.

So when people say “AI detected AI,” what they really mean is:

“AI guessed.”

What’s Worth Keeping — and What’s Worth Ignoring

This distinction matters because it shows exactly how AI mixes real information with whatever narratives are loudest online. From Gemini’s giant write‑up, here’s the breakdown.

Worth Keeping These parts were accurate because they came from me:

• my review methodology

• my long‑term listening process

• my reference gear

• my target curve philosophy

• my background in live sound

• my grading system

• my IEM and headphone preferences

Worth Ignoring

These came from Reddit patterns and LLM filler, not reality:

“Credibility unmoored…”   — Gemini “research”

“The Honesty Paradox…”   — Gemini “research”

“The transparency crisis…”   — Gemini “research”

These dramatic phrases are not based on facts — they’re based on online speculation and the model’s tendency to inflate tension.

The Bigger Lesson: Digital Literacy Matters

This entire experiment shows why creators — and audiences — need a clear understanding of how AI works.

AI is a tool. A powerful one. But it’s not a journalist, a fact‑checker, or an investigator.

It’s a prediction engine.

And if you don’t understand that, it’s easy to mistake confident‑sounding text for truth.

My Workflow, Clearly and Simply

This is the workflow Gemini misrepresented until I provided it directly. Just to restate it plainly:

• I write the content.

• I do the listening.

• I form the impressions.

• I structure the message.

• Copilot helps clean up grammar and clarity.

Copilot is an editor — not a co‑author. And even Gemini, after reading my article, acknowledged that.

Closing Thoughts

The difference between yesterday’s Gemini output and today’s corrected version tells the whole story.

Yesterday, Gemini confidently repeated online speculation, Reddit narratives, and dramatic filler — because the prompt pushed it toward controversy. Today, with a neutral question and a primary source, it produced an accurate explanation of my workflow.

Same model. Same data. Different frame → different prediction.

That’s the core lesson.

AI doesn’t know the truth. AI doesn’t investigate. AI doesn’t verify. AI predicts.

And if the loudest pattern around a creator is misinformation, the AI will echo that misinformation unless you give it something better — clarity, transparency, and primary sources.

That’s why I wrote this article. That’s why I shared my workflow. And that’s why I’ll continue being open about how I use tools like Copilot.

Because the best way to correct an AI is the same way you correct a rumor: tell the truth clearly, consistently, and in your own voice.