Getting stuck

Getting stuck used to mean an involuntary pause in the writing process, a temporary inability to put words on the page. Getting stuck was a routine experience for many writers – especially academic ones whose words are meant to flow from years of reading and research. The pressure! 

Sometimes getting stuck was called ‘writer’s block’, although that phrase told only half the story. After all, we still managed to write emails and shopping lists in abundance. Getting stuck was more a state of mind, a realization that the swirling thoughts in your head could no longer be tamed and laid out neatly into coherent lines of prose. The only thing you could do was sit in front of a computer with your hands resting on the keyboard and wait until something happened. And it always did, eventually. 

Today, it’s impossible to get stuck in quite the same way. Generative AI has made the act of writing quick and painless. Can’t think of anything to say? No problem: just enter a half-formed idea, a mere fragment of a thought, into Claude or Copilot or ChatGPT and then GenAI will produce a series of sentences and paragraphs that finish the thought for you. It’s disconcerting, yet oddly alluring. We can now reach our destination faster, without any of the hair-pulling or teeth-gnashing that used to characterize so much of our daily writing practice. 

I’m not going to tell you that getting stuck is inherently valuable (although perhaps it is). I’m also not going to tell you that writer’s block provides a welcome occasion to take stock of your ideas (although, again, maybe it does). What I’m going to tell you is this: the rise of GenAI creates new ways of getting stuck. 

I started this blog in March 2024. My aim was to write a couple of blogposts every month about the craft of academic writing. By then, GenAI had been around for a while, but it was still relatively easy to ignore – especially for someone like me, a tenured mid-career academic with a solid list of publications to their name. Sure, I’d dabbled with ChatGPT out of curiosity and tried to keep up with recent developments in artificial intelligence. But, for the most part, I was able to push GenAI out of my mind for almost a year. 

In February 2025 I decided to write about GenAI for the first time. I realized that most of my students, and some of my colleagues, were using AI tools for producing text. It felt strange to remain silent on the most transformational development in writing since, well, take your pick: The electronic word processor? The printing press? The invention of wood-pulp paper? 

I was pretty happy with what I wrote about chatbots. But it felt like a full-stop, an ending of sorts. I subsequently tried, and failed, to write a follow-up blogpost on a different topic. A couple of weeks passed, then months – still no joy. I had gotten stuck. 

It wasn’t the same feeling of stuckness I had experienced before, back when I was writing my first book. That felt like a depleted battery, something that just needed to be recharged for a few days. Getting stuck now felt more permanent, more profound, like a decommissioned power station overrun by weeds and rats.

It wasn’t that AI would necessarily replace academics or make us redundant. It was rather the opposite: GenAI would soon become an essential tool for any academic who wants to write well. And that thought depressed me to the point of inertia. What is the point of writing without GenAI if GenAI can do it better and more efficiently than we ever could?

Other academics seem to have experienced a similar kind of despondency. In a recent blogpost, education scholar Mark Carrigan describes a book he’s been working on, one that explores the implications of GenAI for academic writing. Carrigan explains that he has been using GenAI – specifically Anthropic’s Claude – as a ‘conversation partner’ to develop his ideas. Not to autogenerate text, but to provide feedback and commentary and clarification. 

After having written a rough draft of 30,000 words, Carrigan fed his text into Claude and asked it to summarize the contents of the book. It did so, astoundingly well, and produced a set of propositions that tied together the themes of the book. Carrigan reflects on the results:

The sprawling mess of the first half of a manuscript, in which I could barely see the wood for the trees, immediately gave way to a feeling of the project being within my grasp. It felt so deeply and profoundly achievable through Claude’s intervention.

It should have been a moment of celebration for Carrigan, the point at which a jumble of notes arranged themselves into a crystalline argument. But it wasn’t: 

I felt my motivation for the project drain away, almost as if I could literally feel it leaving my body. That sense of the difficulty, the struggle with inarticulacy which characterizes the writing process for me, suddenly appeared as something which I could switch off at will.

That lack of motivation lingered. In a later blogpost, Carrigan tells us he has lost interest in his project altogether. His book lies unfinished.

GenAI can make us more productive as writers. It generates text quicker than us, and it reaches our conclusions before we have a chance to. GenAI reduces the struggle of writing to the point at which it becomes trivial. This is seductive, especially for early career researchers or writers pushing up against a deadline. 

But what we risk losing isn’t just the chance to develop as writers, to truly master our craft. We also risk losing our appetite for writing in the first place.