Is Your Writing Yours? - by Sofia Quintero
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Is Your Writing Yours?<br>How To Think About The Risks And Benefits Of AI Authorship
Sofia Quintero<br>Jun 11, 2026
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The more I write the more paranoid I get about whether or not my writing sounds like AI or whether the ideas I’m sharing are truly mine and to what extent.<br>I keep asking myself, what is the right amount of friction in my process that would allow me to exercise my writing and thinking muscle and still take advantage of what AI can provide in the research and editing process.<br>Can we truly use AI and be good writers? Can we truly get better at producing insightful and original pieces while still using AI as an assistant?<br>When it comes to knowing your contribution and learning through the process, I have bad news. The answer is closer to a “no” and it is complicated.<br>It turns out we are very poor judges of our thinking process.<br>When you decide to use AI to plan, research and write an essay, you may feel you’re in control because you are doing the thinking, writing the prompts, and evaluating the output. That sense of control feels very real. Unfortunately, the literature so far indicates that it is all an illusion.<br>The introspection illusion as a concept has been studied for several decades (Nisbett & Wilson, 1977; Pronin, 2009). The insight is simple: we cannot reliably judge our own bias, nor can we trust the introspective analysis of our own thought process. And when we compare our judgment to others’, we fail to compare them fairly.<br>Most recently, Jakesch et al. (2023) developed a co-writing persuasion experiment in which 1,506 participants wrote about whether social media was good for society with the help of an opinionated AI assistant. The findings show the assistant shifted both what people wrote and what they later reported believing, with the AI influence opaque to them.<br>There is another psychological quirk that makes it even harder to assess whether what we are writing has been influenced or modified by AI. The Anchoring Effect is a cognitive bias in which we rely too heavily on the first piece of information we receive when making decisions.<br>For example, it is easy to find evidence of people using AI to generate the outline of whatever they are trying to write. In their words, this outline helps them deal with the terrifying prospect of a blank page. The issue is that once you get the outline, that outline is your anchor, and the research supports that it is extremely hard to deanchor yourself from that information (Epley & Gilovich, 2005; Wilson et al., 1996).
But what if you use AI in a way that constantly pushes back?<br>Maybe you create excellent system instructions or just magnificent prompts. If the AI is pushing back and helping shape your thoughts, then it is impossible to separate your thinking from the influence of AI.<br>Psychological ownership is a thing. When you use these models to help you produce better outcomes, you may feel like you own the thinking, but as you continue to use it you will also feel that it is not completely yours, and that duality is unsettling.<br>Should I stop using AI for my writing?
It depends on how the AI is designed to interact with you. Most of the research I’ve been reviewing points to major risks of cognitive offloading. However, there is also early evidence that if you can manage to force the model to only give you hints, forbidden from giving you final answers, let’s call this tutor mode, you can minimize harm (Bastani et al., 2025) and, in other studies with similar guardrails, even increase your rate of learning (Kestin et al., 2025).<br>The problem I have with the tutor use case is that writing is not always a guided learning process. Writing is a messy process of research, curiosity, asking what we really believe in, looking at the evidence and what we want to communicate. That is a messy process in which we are not always trying to learn concepts but connecting the ones we already know in new ways.<br>The common advice popularized by AI academics, practitioners and researchers is that maintaining friction where it matters is helpful. For instance, allowing AI to execute repetitive tasks associated with research or data structuring can be beneficial to the writing process. However, the consensus is that this AI application would be most dangerous for a novice: the premise is that without latent knowledge you don’t know the nuance around the insights you are evaluating, and therefore you can’t truly maintain epistemic integrity.<br>Then the question becomes, how do you know you are truly an expert or have enough experience to judge the results? As we established before, we don’t seem to be good at this, and we definitely don’t have good and objective ways to measure experience and expertise.<br>I have an MSc in Applied Neuroscience. Does that mean I can just assume I have enough expertise to judge the AI-assisted research process on subjects like cognition? Where does my expertise start...