Hey #chi2026 #chi26. Want to karaoke tonight? Meet us here at 10 pm: maps.app.goo.gl/yvdfiTc1JBma...

I built the writing system I wish existed when I was a junior researcher.

Here it is:
x.com/acagamic/st...

I stayed silent for years perfecting my research approaches.

Really worked out great watching Chad quote my research in Forbes.

My ideas were going to shape the world any day now.

Any day.

How to grow your expert audience on X:

1. Post consistently
2. Engage with comments
3. Collaborate with peers
4. Realize consistently means every day for two years and briefly reconsider all your life choices...

Being the most qualified is a disadvantage.

You watch consultants with a fraction of your expertise land the keynotes, the board seats, the media quotes.

They don't know more.
They just publish more.

Every week you stay silent, the buzz compounds against you.

Your significance section should scare reviewers a little.

Show them what happens if this research doesn't get done.

Show them the cost of ignorance.
Show them the opportunity we're missing.

Make inaction feel expensive.

Your research is invisible where decisions get made.

Nobody Googles your h-index.

Ok, researchers do check your h-index.

But everyone else who controls your opportunities doesn't.

The VP hiring your competitor or the journalist covering your field.

None of them start with Google Scholar.

They start with X, LinkedIn, and newsletters.

Prolific experts aren't more disciplined.

They have a publishing system that removes decision fatigue from every step:

• What to write
• How to structure it
• When to post
• What to repurpose

You don't need more motivation.

You need fewer decisions per publishing cycle.

It's expert content with better assistance and distribution infrastructure attached.

Stop calling it AI content.

When a surgeon uses a robotic arm, nobody calls it robot surgery.

Example: The da Vinci Surgical System is robotic-assisted surgery.

When a researcher uses AI to structure 20 years of insight into weekly posts, it's not just AI content.

After 20 years of publishing 300+ papers, I've learned this:

Your writing schedule isn't about finding time.

It's about designing a system that doesn't depend on perfect conditions.

Stop waiting.
Start designing.

Discipline didn't fix my posting.

I tried motivation apps, content calendars, and accountability partners.

Nothing worked until I built a repeatable 60-minute weekly system that turns one core insight into five posts.

The bottleneck was never my willpower. It was my architecture.

AI content has two speeds.

Speed one:

Generic sloppedy slop that floods feeds and erodes trust.

Speed two:

AI-assisted systems that help a 20-year expert publish their actual insights 5x faster. Boom.

Most people only swim in the slop ocean at speed one.

Build real authority with speed two.

CHI conference mindset:

Present like everyone's career depends on remembering you.

#chi2026

More citations. Less influence.

Researchers with deep knowledge produce invisible content because expertise is a curse for us.

You see too much nuance to simplify.

The fix isn't dumbing down your work.

It's building a interpretation pathway between your brain and your audience.

Your CV impresses nobody online.

20 years building credentials for boardrooms and peer review.

Meanwhile, a 26-year-old with Canva outranks you because they post daily and you post quarterly.

Showing up and delivering, day after day, is what matters.

Research has become so easy to scale.

It's scary.

If you treat AI like a plagiarism, you're still stuck in the scarcity model.

Accepting it as the backbone that keeps everything running means publishing 3x more and better work because you're spending your time on problems that actually require human insight.

What used to take 3 weeks of reading now takes 3 hours of strategic review.

3. Automated citation and context mapping

Instead of manually tracking 200 papers, they're using AI to map citation networks (like Litmaps, Research Rabbit or Connect Papers), identify gaps, and surface relevant recent work.

2. Pre-submission peer review simulation

Before submitting, they're feeding drafts into AI trained on reviewer patterns.

It points out weak arguments, missing citations, methodological gaps—the stuff that gets you desk-rejected.

They're getting feedback in hours.

Here's how researchers are bypassing the bottleneck now:

1. Parallel paper production

They're not writing one paper at a time anymore. They're using AI to draft 3-4 papers simultaneously, like getting related literature, methods sections, discussions, while they focus on their analysis.

Most researchers spend 60-70% of their time on grunt work that has nothing to do with thinking:

→ Formatting references.
→ Rewriting the same lit review five different ways.
→ Waiting for co-authors to remember they promised edits.

The system mistakes suffering for scholarship.

The old system was built on scarcity:

• Single-output grinding (one paper = months of manual labor)
• Solo peer review (wait 6-12 months for feedback from 2-3 people)
• Opaque timelines (no visibility into what's blocking you)

That's not rigour but an arbitrary rule that blocks useful evidence.

Publish-or-perish was designed to fail most people.

Here's why the traditional model creates a bottleneck

(and 3 ways researchers are gaming the system with AI):

I supervised 30+ PhDs and rewrote hundreds of their drafts.

This is every writing protocol I built along the way.

The section-by-section system behind 150+ publications (and why nobody taught you this in grad school):
x.com/acagamic/st...

Reviewers don't reject proposals because the research is bad.

They reject because:
• They can't understand what you're doing
• They can't see why it matters
• They can't believe you'll finish

Being overly clever is less effective than direct communication.

The belief that you need long stretches to write is your actual writing block.

Not your workload.
Not your schedule.
Not your discipline.

Your belief.

Kill that belief and you'll publish more than you ever thought possible.

Your research question is too broad if you can't fit it on a Post-it note.

Too narrow if only three people on Earth care about the answer.

The sweet spot?

Specific enough to finish, broad enough to matter.