In the age of AI, how much of you needs to be in the writing for it to still be yours?

That’s the question I’ve been sitting with lately.

Not knowing who to ask, I asked ChatGPT.

Here’s what it said:

“The real ‘you’ in writing isn’t just about the words themselves—it’s about voice, perspective, and the unique way you connect ideas.”

Not a bad answer.

But maybe the better question is:

What makes writing yours at all?

Is it the rhythm?

The framing?

The stories you choose to tell with your data?

For me, this isn’t just philosophical.

It’s existential.

I’ve spent over a decade teaching clinical researchers how to write.

Manuscripts. Grant proposals. Abstracts.

I built frameworks.

Created systems.

Earned a reputation for treating academic writing as a structured, learnable craft.

But today?

Just three months after my last cohort,

I wouldn’t teach it the same way.

Because academic writing has changed.

Fundamentally.

The skills I built my career on?

Still useful—but no longer sufficient.

The bar for being good has been replaced by a new bar:

Being better than AI.

I’ve seen ChatGPT generate deep-dive literature reviews faster—and more coherently—than a research assistant could in a full day.

So what is authorship now?

Is it writing every word?

Or is it having the idea—the structure, the spark—and letting AI do the heavy lifting?

I don’t have a clean answer.

But I do know this:

The real challenge isn’t just writing well.

It’s writing in a world where AI-generated content is the default.

Every manuscript.

Every grant proposal.

Every discussion section.

You’re not just competing with researchers anymore.

You’re competing with machines.

And if I were teaching my writing course from scratch today, I wouldn’t start with “clarity” or “structure.”

I’d start here:

The bar has been raised.

This is the new baseline.

And that means we need to rethink how we teach (and practice) academic writing.

Here are 5 areas I believe matter more than ever:

1. The Taste Gap

Ira Glass said it best:

“The thing I’d like to say to you with all my heart is that most everybody I know who does interesting creative work went through a phase of years where with their good taste, they could tell what they were doing wasn’t as good as they wanted it to be… it didn’t have that special thing they wanted it to have… Everybody goes through that phase… and the most important thing you can do is do a lot of work.”

Taste isn’t optional.

You need to know what good looks like first to get good.

Not just passable. Not just “publishable.”

Good. Really good.

And taste? You don’t build it by just writing more.

You build it by reading better.

In my case, I didn’t truly understand good grant writing—even after taking dozens of grant writing courses—until I sat down with 25 funded NIH proposals and reverse-engineered each one.

Taste isn’t about talent.

It’s about exposure.

And no matter how great AI gets, you won’t be able to get great output unless you know what you want and have at least a vague sense of how to get there.

2. Meta-Skills > Mechanics

Anyone can dump results into a Word doc.

But knowing which 3 findings actually matter?

That’s a meta-skill.

Knowing how to translate results into a cohesive narrative with a clear throughline?

Also a meta-skill.

Making people care?

That’s the new edge.

It’s not just about saying what happened.

It’s about knowing why it matters, to whom, and how to say it in a way that moves them.

3. Ideas > Execution

This might feel counterintuitive.

But with AI closing the gap between idea and execution, ideas are becoming your real asset.

Execution is now faster, easier, and—frankly—commoditized.

Which is why I protect my half-baked ideas more than ever.

Not out of fear.

Out of awareness.

Because anyone with the same idea and a good prompt can ship it by the end of the day.

The next edge?

Creativity and discernment.

And that can’t be downloaded.

So build your own “inspiration vault.”

Track the ideas, curiosities, and fragments that resonate with you.

That’s the real intellectual property now.

4. Own Your Data

My advice to early researchers has always been:

Don’t start with collecting data. Start with using what already exists.

Institutional datasets.

Mentors’ projects.

Open access databases.

That advice still holds.

Secondary analysis is a fast way to get real reps.

But the real leverage?

Comes when you own the data.

Build your own cohort.

Design your own longitudinal study.

Track your own patients over time.

Someone once told me:

“If you don’t have your own data, you’re a beggar in research.”

I’ve never forgotten that.

In a world where anyone can write a paper with ChatGPT,

owning unique, high-quality data is the biggest moat.

5. Learn to Prompt Like a Pro

There’s a myth going around:

That with smarter AI, prompts won’t matter anymore.

That’s complete nonsense.

Garbage in, garbage out is still the rule.

Prompting isn’t just about getting a decent response.

It’s about framing your ideas, directing the output, and shaping the tone with intent.

When I was learning R, ChatGPT became my personal teaching assistant.

I didn’t just ask for code.

I asked for help debugging.

I asked for explanations.

I used it to understand machine learning methods that would’ve taken me weeks to learn otherwise.

The difference?

I didn’t treat it like a toy.

I treated it like a collaborator.

The best academic writers won’t just be better writers.

They’ll be better editors, synthesizers, and AI collaborators.

So no—AI isn’t the end of authorship.

But it’s the end of the default way we’ve been writing.

In this new world, Human + AI beats either alone.

But only if the human brings:

Clarity.

Discernment.

Vision.

Not just to write.

But to think.

To question.

To contribute.

That’s the new game.

(And yes, I used AI to write this.)