And Why We Built a Tool to Prove It.♦Created with ChatGPT. The prompt: Using my reference image, create a new image with women walking up a hill and the title Women Rising. The hill should look like a pile of Substack stories. The women should be similar to the women in the reference image. Different ethnicities, sizes.Women are writing about technology on Substack. A lot of them.
Their work is thoughtful, well researched, and engaging.
But when you look at who shows up on Substack’s Technology Bestseller and Rising lists, a different story appears. One that has very little to do with talent, and everything to do with how visibility actually works.
Last fall, as Code Like a Girl started paying closer attention to Substack’s tech ecosystem, we went looking for more women to follow, subscribe to, and share. The Technology lists seemed like the obvious place to start.
What we found wasn’t shocking. But it was disappointing.
There were hardly any women on either list.
These lists aren’t decorative. They’re a signal. They say this is who matters. They’re one of Substack’s primary discovery mechanisms. Show up on a list and you gain followers, views, recommendations, and subscribers.
For our Medium readers, think of Substack’s Rising and Bestseller lists the way you’d think about Medium’s topic feeds, curation, or distribution boosts. They’re the mechanisms that decide what gets surfaced, shared, and rewarded, and who quietly disappears.
Visibility changes outcomes.
And once visibility concentrates, it compounds.
Substack’s discovery systems reward existing visibility. Lists, badges, and recommendations are all downstream of who is already being seen.
Every publishing platform has its own version of this — whether it’s badges, distribution boosts, recommendations, or algorithmic preference — but the effect is the same.
When women start with less exposure, they receive fewer recommendations, appear on fewer lists, and benefit less from compounding growth mechanisms.
The system isn’t designed to correct imbalance. It unintentionally reinforces it. And we’re going to do our best to counteract that.
So We Stopped Guessing and Started CountingLooking at the lists, it was clear women were underrepresented. But by how much? And how did that compare to the industry more broadly?
Women make up roughly 22% of the tech workforce. That number is already low, but it gave us a baseline.
So instead of relying on gut instinct, we reviewed the lists manually.
As of December 8:
- 13 women appeared on the Technology Rising list
- 10 women appeared on the Technology Bestseller list
- Each list contains 100 publications
Representation on these lists was worse than industry averages, not better.
At a minimum, you might hope visibility would roughly mirror participation. At best, you might hope it would exceed it, because overrepresentation is one of the few ways entrenched perceptions actually change.
Visibility doesn’t just reflect reality.
It shapes it.
That belief has been core to Code Like a Girl for nearly ten years.
The Work Isn’t the ProblemOver more than ten years on Medium, we’ve published work from over 1,000 writers, many of them women writing thoughtful, rigorous work about technology.
In our first five months on Substack, we’ve continued that work by publishing more than 45 women writing deeply informed technology stories.
Beyond our publication, we follow hundreds of women writing about tech. SheWritesAI alone tracks 600+ women writing about AI on Substack.
The women are here. They are writing. They are building in public.
What they’re missing is visibility.
Why Visibility CompoundsOne thing that made this impossible to ignore was watching how thresholds and badges change what gets seen.
Appearing on Rising or Bestseller lists increases exposure. Badges act as social proof that increases trust and improves conversion.
Research shared recently suggests that earning a Bestseller badge can temporarily increase paid conversion by roughly 25% for several months. — Thanks to Mack Collier for this info.
Early visibility doesn’t just feel good, it creates a structural advantage.
Why We Built the LeaderboardManually counting these lists every week wasn’t realistic.
And once you know something can be solved with code, doing it manually becomes unbearable.
Building this system wasn’t easy. I hadn’t written production code in nearly two decades. Modern tooling felt intimidating. But watching women in this community ship ambitious, technical work made one thing clear:
If they could build, so could I.
The story of how the tracker was built lives in a separate post, which I’ll link here.
This post is about why we’re tracking at all.
Why We’ll Keep Tracking ThisGoing forward, this becomes part of our rhythm. We will take weekly snapshots and share them with you, our readers.
This leaderboard isn’t about calling anyone out. It’s about refusing to let structural imbalance remain invisible.
Now that we’re watching, we plan to keep watching and sharing what we learn along the way.
Here’s what we learned in the first two weeks.
♦Here is what we have learned from our first two weeks tracking.How We’re Going to Move the NeedleTracking the lists is only the first step.
Our goal isn’t just to measure who shows up on the Technology lists; it’s to understand what actually helps more women get onto them.
On Substack, women reach the lists by gaining paid subscribers. That means two things have to be true at the same time:
- More people need to find and read their work
- Their publications need to convert when readers arrive
We’re focusing on both.
Increase Visibility for Women Writing about TechMost people assume strong writing eventually rises on its own.
It doesn’t. It needs to be amplified. Here’s our approach.
1. Publishing three byline stories each weekWe will keep featuring women in tech writing on Substack and sharing their work directly with our subscribers.
2. Amplifying voices through NotesWe will continue highlighting, restacking, and engaging with women’s posts so their work travels beyond their immediate networks.
We have also started a new notes series spotlighting one woman writing about technology on Substack.
Here is the first one we published.
3. Normalize recommending Women in TechRecommendations are one of the simplest and most powerful tools available on Substack to boost a publication’s visibility.
We’ve seen this firsthand. In January alone, 65 of the 241 new subscribers who joined Code Like a Girl came through recommendations. That’s not a nice-to-have. That’s impact.
We follow and subscribe to hundreds of women writing about technology, AI, and robotics. But when we look across recommendation lists, a pattern keeps showing up: in roughly one third of them, women don’t appear at all.
To counteract this, we’ll continue to:
- Explain how recommendations actually work
Not as etiquette, but as infrastructure that shapes discovery. - Model intentional recommending
We recommend every writer we publish, because visibility should travel with the work. - Remind our community why this matters
Recommending women in tech isn’t symbolic; it directly affects who gets seen, followed, and paid attention to.
Help our Community Build Publications that ConvertOur monthly newsletter, Women Rising, is where we bring this work together with visibility analysis, practical publishing insights, and a celebration of the most-read Code Like a Girl stories across both Substack and Medium.
We’ll cover topics like branding, SEO, collaborations, and self-promotion that will always be grounded in real examples from within the Code Like a Girl community.
Next month's newsletter will be focused on branding. We’re currently working through our own visual rebrand, and we’ll share what we’re learning along the way.
If you want a sneak peek at what our image branding will look like, take a look at the image in this post.
For our Medium writers, this work continues here as well. We’re still submitting stories for boosting and featuring on Medium, and we’ll continue highlighting Medium stories as part of Women Rising.
We’ll close this edition by highlighting the most-read Code Like a Girl stories from January, across both Substack and Medium.
Top 3 CLAG Substack Stories in JanuaryYou are Already Technical Enough for AI by Alyssa Fu Ward, PhDA reframing of what “technical” actually means in an AI-mediated world. This piece shows how relational thinking, clarity, and judgment, which are skills women are often penalized for, are becoming central to effective AI use. You don’t need permission to belong here.
Raising the Girls Who Will Fix What We Broke by Neela 🌶️This essay dismantles the myth of the “leaky pipeline” and shows how girls are sorted out of technical confidence long before adulthood. It argues that STEM futures are decided at kitchen tables, not boardrooms and names exactly where the erosion begins.
Women Are Judged Twice as Hard for Using AI by Mariam VossoughA forensic look at how identical AI-assisted work is judged differently depending on who uses it and why neurodivergent women pay the highest price. By framing AI as access rather than cheating, this piece exposes the quiet competence penalties still shaping technical careers.
Top 3 CLAG Meidum Stories in JanuaryThe Supermodel in the Engineering Office by Denise JamesDenise tells a deeply human story about mentorship, confidence, and learning to stop making yourself small in technical spaces.
Spanning decades, the essay connects early career moments to later battles over ownership, credit, and boundaries. It’s a reminder that technical competence isn’t just about skill, it’s about believing yourself the first time you’re right.
The Mindset Shift That Separates Good Leaders from Great Ones by VinitaVinita examines the internal shift leaders must make as their scope grows — from solving problems themselves to creating conditions where others can succeed.
It challenges performance-based identity and highlights the quieter, harder work of trust, restraint, and systems thinking. A grounded take on leadership that resonates well beyond management titles.
I Scraped 10,000 Reddit Posts to Find Out Why Data Analysts Are Panicking by Ms.DataByteMs. DataByte takes a data-first approach to career anxiety in analytics, scraping thousands of Reddit posts to separate signal from noise.
The analysis reveals a “panic loop” driven less by AI job loss and more by skill overload and market uncertainty. Instead of hype, the piece offers evidence and a calmer, more grounded view of what’s actually happening in data careers.
Join Code Like a Girl on Substack or keep following along on Medium.
Note to our readers:
We will be taking some time off in starting March 12th through to March 22nd.
♦Women Rising: Why Women inTech Writers Are Invisible on Substack was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.