Welcome to the Age of Second-Hand Insight

Academic writing is getting smoother. That's the first thing you notice.

Literature reviews that read like they’ve been edited by a team of proofreaders. Frameworks that arrive fully formed. Discussion sections that hit every expected note yet somehow say very little.

We’ve crossed a line. We’re not just speeding up the process of writing. We’re outsourcing the thinking. And the results are showing.

Generative tools aren’t thinking. They’re blending.

Ask an LLM to analyse your data, and it will give you something. It will sound impressive. It will sound familiar. That’s because it is.

These tools don’t know your research. They don’t know your context, your field site, your participants. They’re not building new insight, they’re stitching together plausible responses from what’s already out there. Polished second-hand thinking.

And if that’s the starting point of your analysis… whose work is it really?

The illusion is that this is insight. It’s not. It’s consensus. Smoothed out. Repackaged. And often wrong.

The problem isn't automation. It's detachment.

The more we rely on generative tools to scaffold our writing, the easier it becomes to lose track of where our ideas came from. You highlight a few quotes, get some themes, plug it in, tidy it up.

You might be publishing findings that aren’t traceable to your data.

Not because you meant to. But because the tool filled in the blanks for you.

And when a reviewer, colleague, or co-author asks: “Where did this interpretation come from?” you need an answer that’s better than “It sounded right.”

Staying original when everything sounds familiar

This isn’t about purism. Or about banning tools or pretending we can write everything in a vacuum. It’s about making sure the interpretation in your paper still has you in it.

Here are a few ways to keep your work grounded:

1. Leave room for discomfort. Real analysis is messy. If it feels too easy, you probably stopped too early.

2. Pay attention to what doesn’t fit. Themes that emerge quickly are often the ones you were already expecting. Don’t ignore the outliers—they’re often where the real insight hides.

3. Write your first impressions down by hand. Before running anything through software or asking a chatbot to help, jot down your raw reactions. Those early notes carry your genuine thinking, unfiltered.

4. Use tools that support, not substitute. There’s nothing wrong with using software. But choose tools that show structure, support interpretation, and let you return to the data—not ones that summarise it for you in someone else’s voice.

The insight is yours. Or it’s not.

You can feel the difference between something you genuinely saw and something that was handed to you, clean and convincing, by a machine trained on everyone else.

So here’s the question. If your research is built on second-hand patterns, can you really call the conclusions your own? And if not, why are we trusting them?

Original thinking isn't about being the first. It’s about being responsible for what you say.

AI will keep getting better. It will keep sounding smarter. But the work of meaning-making… that changes policy, or improves lives, or shifts understanding… still belongs to researchers.

And the researchers who will stand out in this new landscape?
They’ll be the ones who can say "This insight came from me. And here’s how I know.”