I’m at a crossroads of sorts: should I or should I not disclose the usage of AI within my workflow as it relates to my newly minted The Weekly Tinkerer newsletter?
And before you start screaming at me, of course you need to disclose it; how can we trust anything you are writing if you are using generative AI for your writing?
I was right there with you 96 hours ago when I started this piece. It was going to be simple; yes, I use AI to help aggregate data on my tinkering. Yes, I fact-check everything I post. Yes, all the ideas I write about are my own. And finally, no, I don’t mindlessly post AI-generated text.
But as I researched the topic, I came to understand that this is not a binary conversation, and that the data and reasons behind disclosing the use of AI-generated text are more nuanced than they appear.
I hope this article helps you think about the topic in a different way.
You may have seen recent headlines.
LinkedIn introduces an “AI Slop” reporting tool to wage war against content quality on its platform.
Hank Green, a YouTube creator and educator, inadvertently admitted to using AI in his process during a video he recorded, prompting swift condemnation from many and bringing the generative AI disclosure debate into focus.
Anthropic is rolling out the ability to watermark AI-generated text in new models, to comply with EU regulation.
Substack partners with Pangram to provide an AI text scanner.
For this piece, I’m going to keep my argument and research centered on LinkedIn, Substack, and YouTube. These are my platforms of choice. I think much of what I’ll assert is valid in other platforms, but I’m not going to take the time here to make that point.
At the time of this writing, none of the platforms have taken a firm stance on outright requiring you to disclose the use of AI-generated content on their respective platforms, except for YouTube, but, as it turns out, their stance is limited to specific use and not necessarily far-reaching.
YouTube requires disclosure of AI-generated content when it results in realistic content that a viewer could mistake as being real. This includes synthetic voices, altered events, or fabricated photorealistic scenes.
This makes perfect sense; it’s a visual platform, and the disclosure focuses on the content displayed.
But what is exempt from their policy is the use of AI to create scripts, ideas, captions, animated content, or thumbnails. In other words, no need to disclose when using AI to help with structure and research, but required when dealing with voice and visuals.
LinkedIn and Substack do not have specific terms of use regarding generative AI on their platforms, but both have given readers tools to flag or check for it. LinkedIn gives you an “AI Slop” reporting button, and Substack lets you scan a piece using Pangram.
So, the case for disclosure is more of an ethical/moral/voluntary choice, not compulsory.
And to throw some fuel on the fire, a research article by Toff and Simon in The International Journal of Press/Politics[1] asserts that when it is disclosed that AI was used in the writing of articles, trust in the content drops significantly, even though the articles are factually correct. The research also showed that this drop in trust is reversed when specific sources outside AI are used.
So, where does this put creators?
If you disclose your usage of AI, you may be labeled as untrustworthy, even if still factually correct. The damage is mitigated somewhat if you cite sources outside the AI sphere. Creators are also benefiting from using AI in their content creation processes.
But, as the late-great Paul Harvey liked to say, now for the rest of the story (or something like that).
Here is an uncomfortable truth that I suspect will have you throwing your shoes at me. Humans are barely more able to reliably identify AI-generated text than using a simple coin toss.
Yup, your ability to detect AI-generated text is only slightly better than tossing a coin.[2]
I know, you don’t believe me. There’s the em dashes, the sameness in sentence structure. Lack of rhythm in the work. It’s not X, it’s Y. Not an original thought, etc.
Just head to LinkedIn and ask anyone; they’ll gladly proclaim they can, without a doubt, identify AI-generated text.
The data shows otherwise. People are confident about the wrong signals.
But there is actually one tell that is obvious and tips the scales in your favor: hallucinated facts and fabricated sources.
All at the same time now, “well no sh*t Sherlock.”
And that’s the point: for most people, human detection is more performative art than fact unless the text in question is factually incorrect and the sources are invalid.
It’s such a big point that the EU recently enacted the EU AI Act’s Transparency Code (Article 50(2)).[3] It says several things, but most notably as related to this article:
AI models need to produce a synthetic content marking when it’s generative AI. This includes having the software machine-mark text, image, audio, and video output so it’s detectable as AI-generated.
There’s one important limitation to this legislation worth noting.[4] The watermark ONLY proves the AI touched the content it’s producing, but doesn’t indicate who wrote it or how much of the text is generated; i.e., stuff heavily edited by a human after the fact will no longer necessarily bear the watermark. As such, the watermark isn’t enough to purely indicate that content is AI-generated, and thus any claims to be able to identify AI text are more performative than quantifiable.
So, this article is getting lengthy; let’s summarize quickly before moving on.
We are more likely to distrust AI-generated content, even when factually correct.
AI is proving to be a very effective tool for generating content and researching topics.
People’s ability to identify AI-generated text is slightly better than a coin toss; the signals we rely on are unreliable. The ability to detect AI-generated text changes when it hallucinates or cites non-existent sources.
Disclosing AI usage is more a matter of principle than a compulsory rule.
Where does this leave someone like me, someone without a well-known brand, but liked by my colleagues (for the most part), who is starting on a new journey to publish works in the public space? Should I or should I not disclose the use of AI as part of my workflow?
Interestingly, it was difficult to find straightforward examples of more well-known creators disclosing their use of AI. The few I found include Katie Harbath, Carlo V. Santiago, Hank Green’s personal policy, and Complexly’s institutional policy.[5] And that was it. It’s not to say others don’t disclose their use, nor does it imply people aren’t being honest about it, but what it does signal is that the topic, while hot, isn’t necessarily being addressed by widespread acceptance of disclosing AI use.
For me, I’m all for disclosure. I’m beginning my creator journey, and it’s not just about publishing my thoughts, but about building trust in what I’m doing so I can share what I’m creating with you, and you can see my process.
In 2025, I started posting on LinkedIn to build a personal brand. It was more of an experiment than driven by a specific cause, and it yielded modest results.
When I began the process, I was all in, leveraging Claude to help generate content. This included taking my ideal customer profile (which AI helped me create) and asking about the pain points people were experiencing in my areas of expertise. I’d find a pain point and write my own thoughts around it, more of a brain dump than a structured article.
I fed my brain dump into an AI process I created, which helped me write two versions of the same article. The first was a story-based arc and the second was a contrarian take on the original idea. A third process compared the dueling articles, scoring them against an opaque rubric (again created by AI) and recommending which version to publish.
Maybe not ironically, the scores were often 39 out of 50 vs. 37 out of 50. Read into this what you will, but I find it funny that the two different arcs converged into near sameness when quantified by AI. The humor lands hard when looking back on that time.
Not only was there the sameness, but the articles that were being produced were flat, uninteresting, and void of soul.
The output I was reading looked eerily similar to so much of what I was seeing on LinkedIn. As it turns out, a peer-reviewed study confirmed that people who frequently use AI for writing are better detectors of AI-generated content.[6]
I was producing AI slop. The same slop I rolled my eyes at so often when scrolling.
What was produced was a sanitized, watered-down version of a brief I fed to the AI that looked like so many other posts.
I tried to rewrite them to follow the arc of the winning article. If the measure of success for the posting was engagement and views, I had a few land well, but most of the articles posted didn’t move the needle.
The frustration was mounting with the lack of results, especially as I started writing more about the business I was starting.
Thinking the problem was that the output was lacking my voice, I launched into a 100-question interview of me to generate a voice profile to give to AI to make it sound more like me, an idea I took from Ruben Hassid’s newsletter article titled “I can be you.”
I reworked my dueling agent process to leverage my newly minted voice profile, and while the quality of the drafts improved and sounded more like me, the output was still flat and lacked soul, so I was rewriting it before publishing.
So, I pivoted my process to where I’m at today. I trashed the dueling agents and replaced them with a muse process I created that takes the output of my weekly tinkering, considers a backlog of article ideas I’ve generated based on things I’m reading, and asks me one question.
“What’s on your mind — tell me the idea or the moment, like you’re talking it through.”
It takes my long-form brain dump of an idea, often transcribed, and helps identify the threads to pull on, the ones to leave alone, and the ones to cut out altogether. It asks clarifying questions, restates what it understands, and iterates to help fully develop the article’s scaffolding. Not prose, just a scaffolding that looks like:
The hot-topic hook — AI slop is a live topic right now: LinkedIn’s AI-slop-reporting tools (verified 2026-08-11, see Research below), the Hank Green LLM controversy and public outcry, and Kevin’s own read of LinkedIn feeds souring on AI slop and AI comments (this last part stays personal observation, not a cited fact).
The turn to nuance — the subject is much more nuanced than a binary “I don’t use AI” vs. “I use AI” stance.
The schism — creators find the tools valuable for assistance but shy away from disclosing actual AI-generated/published content, because consumers trust AI-assisted creators and content less. Real friction point that can push people toward silence. Backed by the…
(scaffold continues through 7 points which are left off for brevity)
I use scaffolding to craft the article, and when satisfied with the content, I feed it into Grammarly to help with grammar, punctuation, and sentence structure.
The result of the process is the article you are reading now.
With the backstory out of the way, it’s time to formalize my disclosure statement:
I use Claude AI to assist in the research and data aggregation for the content I produce.
I use Claude AI and Gemini to research questions I have related to the ideas I’m cultivating.
All research is validated and cited in my notes, and where appropriate in my finished articles. Nothing that at least three sources can’t verify is included in my research.
AI will never be cited as the sole source of information.
I use Claude AI to build the scaffolding for my articles.
The words you are reading are my own.
I use Grammarly to edit my content for grammar, spelling, and sentence structure.
I use Gemini to build infographic and cartoon-style images to accompany some of the articles I’m producing.
What I have learned is that creating content is hard. The urge to find shortcuts in the process is real, and AI genuinely eases friction in it. This creator economy is always in flux, and the idea of AI disclosure, while not new, has taken on renewed vigor due to the proliferation of creators, pressures to produce, recent legal changes, and the high-profile case of Hank Green.
As I look at what happened with Hank Green, the court of public opinion hasn’t been kind to him for doing something he never fully explained. Clearly, I don’t know his intent or how widely AI was used in his process, but for me, I’m willing to give him the benefit of doubt. I’ve enjoyed the videos he’s produced over the years. What I’m taking away from his situation, disclosing how I use AI early in my creator journey is important to me. And how that disclosure looks includes what tools I use, how I use AI, and when I use AI.
Sources
Toff, B., & Simon, F. M. (2025). “Or They Could Just Not Use It?”: The Dilemma of AI Disclosure for Audience Trust in News. The International Journal of Press/Politics, 30(4), 881–903. https://doi.org/10.1177/19401612241308697
Human detection of AI-generated text hovers near chance — or worse — across multiple independent studies:
Penn State News, “Q&A: The Increasing Difficulty of Detecting AI vs. Human Text” (May 14, 2024): https://www.psu.edu/news/information-sciences-and-technology/story/qa-increasing-difficulty-detecting-ai-versus-human
Yale Daily News, “Readers Can’t Accurately Distinguish Between AI and Human Essays, Researchers Find” (Nov. 18, 2024): https://yaledailynews.com/blog/2024/11/18/readers-cant-accurately-distinguish-between-ai-and-human-essays-researchers-find/
Berber Sardinha, T. (2024). AI-Generated vs Human-Authored Texts: A Multidimensional Comparison. Applied Corpus Linguistics, 4(1). https://www.sciencedirect.com/science/article/abs/pii/S2666799123000436
“Bot or Not: Can People Tell the Difference Between Stories Written by a Human or by an AI System?” Judgment and Decision Making (Cambridge Core): two studies, N=424 and N=481 adults — 39.39% and 51.97% correct respectively, both at or significantly below chance. https://www.cambridge.org/core/journals/judgment-and-decision-making/article/bot-or-not-can-people-tell-the-difference-between-stories-written-by-a-human-or-by-an-ai-system/45E6DC0BB90AA648654D5AE243F6C667
Milička et al. (2025). Learning to Detect AI Texts and Learning the Limits. PLoS One. Baseline (untrained) accuracy 55.4% (N=254), rising to 65.1% with corrective feedback training — still short of reliable. https://pmc.ncbi.nlm.nih.gov/articles/PMC12527182/The EU AI Act’s Transparency Code (Article 50) itself, effective Aug. 2, 2026:
European Commission, “Safer and More Transparent AI” (Aug. 2, 2026): https://commission.europa.eu/news-and-media/news/safer-and-more-transparent-ai-2026-08-02_en
EU Artificial Intelligence Act, Article 50 transparency obligations, explainer: https://artificialintelligenceact.eu/transparency-rules-article-50/Anthropic’s own published limitations on its text watermark:
Anthropic Support, “How Claude Marks AI-Generated Content”: https://support.claude.com/en/articles/16266773-how-claude-marks-ai-generated-content
TechCrunch, “Anthropic Says It Will Watermark Text Generated by Its AI Models” (Aug. 11, 2026): https://techcrunch.com/2026/08/11/anthropic-says-it-will-watermark-text-generated-by-its-ai-models/Named creators with published AI-disclosure policies:
Katie Harbath, “How I Use AI” (Anchor Change newsletter): https://anchorchange.substack.com/p/how-i-use-ai-newsletter-workflow
Carlo V. Santiago, “Coming Out (AI Edition)”: https://carlovsantiago.substack.com/p/coming-out-ai-edition
Hank Green / Complexly AI policy (YouTube Community post, ~2026-08-09): https://www.youtube.com/post/UgkxbXRPEpONUqoIf0Zu2OPNaKBtd95IIgxUComplication — frequent AI-tool users are a real exception:
Russell, J., Karpinska, M., & Iyyer, M. (2025). “People Who Frequently Use ChatGPT for Writing Tasks Are Accurate and Robust Detectors of AI-Generated Text.” Accepted, ACL 2025. Five annotators who write with ChatGPT frequently reviewed 300 non-fiction articles against multiple frontier LLMs (GPT-4o, Claude, o1); majority vote misclassified only 1 of 300 — outperforming commercial detection tools, even against paraphrasing/evasion tactics. https://arxiv.org/abs/2501.15654