Mind Readings Why Generative Ai Is Better At First Drafts

Bonisiwe Shabane

-Nov 18, 2025, 7:29 AM

mind readings why generative ai is better at first drafts

In this episode, explore why generative AI excels at creating first drafts. You’ll discover the key difference between first and final drafts in the writing process. You’ll understand why AI’s creative, probabilistic nature makes it ideal for getting initial ideas down. You’ll learn how to leverage AI for the messy “ugly first draft,” saving you time and effort. You’ll find out how to best integrate AI into your writing workflow for maximum efficiency. Watch now to master AI-assisted writing!

Can’t see anything? Watch it on YouTube here. What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video. In today’s episode, let’s talk about writing, first drafts, and final drafts. Why is AI better at the first draft than the final draft?

It’s not because AI can’t write. We know it can. Properly prompted, it does an amazing job. If it’s not, then it’s time to improve the prompts. How will people compose text moving forward, now that every author working with a digital word processor and internet access can use generative AI? Many will likely opt to write traditionally as they did before, but some will use AI in partnership to draft.

At this point, the methods a writer uses to develop a first draft feel like a dealer’s choice dilemma—ask AI to generate the draft for you, or bring some of your writing to the... If students use AI in their drafting process, I’m increasingly drawn toward advocating for the latter method. I don’t like the idea of students going to AI and prompting a first draft. I know some have argued that this could be a helpful method to fight the blank-page anxiety most writers feel. Others view this as helping maturing writers by giving them a template or outline to help them organize and scaffold their ideas. I think there may be some value in those approaches, especially in terms of helping struggling students who might otherwise balk at writing, but all of these approaches assume a maturing writer will then...

Those of us who’ve taught first-year writing likely raised a questioning eyebrow at that idea. Students struggle quite a bit when writing. For many, that struggle is a productive one, helping them exercise habits of thinking and self-inquiry, testing ideas, taking creative risks, and often failing. Anne Lamott’s Shitty First Drafts lays bare this process with frank elegance. I wish developers of LLMs would read it because as Lamott puts it, there’s a profound disconnect in how many fail to divorce the reality of the writing process from the end product: People tend to look at successful writers who are getting their books published and maybe even doing well financially and think that they sit down at their desks every morning feeling like a million...

But this is just the fantasy of the uninitiated. That fantasy of the uninitiated doesn’t see the often maddening process that goes into shaping and forming the words and sentences on the page. Lamott does a wonderful job of articulating this struggle and demystifying it: Rekha Thomas, Principal at Path Forward Marketing, advises high-growth companies on GTM strategy and provides fractional CMO services. "AI for first drafts" has rapidly become one of the most frequently touted use cases by marketers for B2B content workflows. Marketers often point to saving time as a value prop of using AI, but this messaging oversimplifies the benefit.

After all, not all content serves the same purpose. With the right inputs (messaging and positioning docs, brand and style guides), AI can quickly generate first drafts of product data sheets, proposals and technical assets to save significant time. Coupling these primary sources with prescriptive prompting about audience and channel empowers marketers to automate content creation at scale. While AI excels at speeding up drafts in these examples, it falls short when content demands originality, nuance and authenticity. Abraham Verghese, author of The Covenant of Water and Cutting for Stone, spoke on the Writing Excuses podcast about the idea of muddling through as part of his creative process, saying: "I think we... You just can't adopt someone else's method and have it work for you.

It doesn't always happen that way." Hello to all 8128+ of you from around the globe. This edition is about the fast growing “AI First Draft Dilemma” and what lawyers need to start doing and asking to save their a$$. See a helpful checklist at the end. You open the document your client just sent over. It looks polished.

The formatting is clean. The tone is confident. It's even structured like a solid legal memo. A few key facts are wrong. The structure, while familiar, doesn’t actually match the client's real situation. And there are a few assertions that sound plausible—but just don’t hold up under scrutiny.

You suspect this wasn't drafted by a human. Or at least not entirely. This one shift helped me write faster without losing my voice. I used to open ChatGPT, dump in my idea, and wait. It gave me perfect grammar. Clean structure.

But something felt off. The draft sounded like a LinkedIn ghostwriter and a textbook had a baby. No spice. No weird. No me. AI didn’t kill my creativity I did, by outsourcing my voice too early.

“AI is a time-saver. Just prompt it and polish the output.” I listened to the Teaching in Higher Ed podcast recently and heard an episode with Leon Furze, an educational consultant from Australia who is also the author of the book, Practical AI Strategies, Practical... Furze studies the implications of generative artificial intelligence (GenAI) on writing instruction and education. In the episode, Furze references a blog post he wrote a few months ago that challenged the use of GenAI tools like ChatGPT or Microsoft Copilot to help people write first drafts. If you’ve listened to any GenAI company marketing their technology, you’ve probably heard that the tools can help with brainstorming, outlining, and tackling the dreadful “blank page.” GenAI tools, the proponents argue, can offer...

Furze has some concerns with this approach. In his post, Furze outlines a few reasons to “be cautious of the AI first draft.” Furze’s first reason is pretty esoteric but important. Furze worries about capitalism, oppression, and the larger impacts on literacy and expression. He introduces a term called the “computational unconscious” which posits that technology and technology companies have created an invisible infrastructure that impacts human thought, communication, and interaction. It’s heady (and scary) stuff. While I share Furze’s concerns about the “computational unconscious,” I’d prefer to dig into one of his other reasons here.

Furze worries that GenAI can undermine the purpose of writing. He writes: “The purpose of writing isn’t just to demonstrate knowledge in the most expedient way. Writing is to explore knowledge, to connect and synthesize ideas, to create new knowledge, and to share. When students use AI to generate a first draft, they skip 90% of that work, creating something that may well be worth sharing, but which has not in any way helped them form and... This rationale resonates with me on a bunch of levels.

As a writer, I realize how difficult this process is. But I also realize the benefits. In a 2012 blog post, I shared my reasons for writing this blog. I wrote: This article gives a high-level overview of how LLMs work and their attendant limitations with accessible explanations and anecdotes throughout the piece. We also present advice on how people can introduce them into their workflows.

It's safe to say that AI is having a moment. Ever since OpenAI's conversational agent ChatGPT went unexpectedly viral late last year, the tech industry has been buzzing about large language models (LLMs), the technology behind ChatGPT. Google, Meta, and Microsoft, in addition to well-funded startups like Anthropic and Cohere, have all released LLM products of their own. Companies across sectors have rushed to integrate LLMs into their services: OpenAI alone boasts customers ranging from fintechs like Stripe powering customer service chatbots, to edtechs like Duolingo and Khan Academy generating educational material,... On the strength of these partnerships and widespread adoption, OpenAI is reported to be on pace to achieve more than a billion dollars in annual revenue. It's easy to be impressed by the dynamism of these models: the technical report on GPT-4, the latest of OpenAI's LLMs, shows that the model achieves impressive scores on a wide range of academic...

These splashy results might suggest the end of the knowledge worker, but there is a key difference between GPT-4 and a human expert: GPT-4 has no understanding. The responses that GPT-4 and all LLMs generate do not derive from logical reasoning processes but from statistical operations. Large language models are trained on vast quantities of data from the internet. Web crawlers –– bots that visit millions of web pages and download their contents –– produce datasets of text from all manner of sites: social media, wikis and forums, news and entertainment websites. These text datasets contain billions or trillions of words, which are for the most part arranged in natural language: words forming sentences, sentences forming paragraphs. In order to learn how to produce coherent text, the models train themselves on this data on millions of text completion examples.

Mind Readings Why Generative Ai Is Better At First Drafts

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