Representation and Range in the Age of AI

I saw a LinkedIn post this week asking “Where are the women in AI?”

The author said his team was putting together an AI webinar panel. They wanted a strong female voice. They said it was harder than expected to find one. Stats followed, the usual suspects: Workforce percentages. Executive gaps. Silicon Valley examples. Then the invitation: if you’re a woman in AI, tell us your story.

The thread filled up fast.

Women tagging women. Names being dropped. Recommendations flying.

And as I read it, something felt off.

Search Behavior Shapes What We See

Women in AI are not hidden. We keynote conferences. We run labs. We publish. We build startups. We lead ethics programs. We advise boards. Some are loud. Some are quiet. Many are working at levels most people never see.

If you search for us with intention, you find us.

So when someone with 14,000+ followers says it was difficult, that tells me something. It tells me about how the search was conducted.

Digital platforms create echo chambers without announcing it. Your feed reflects who you already follow. Recommendations reflect the networks you’re already close to. The algorithm keeps serving up familiar names and cookie cutter content because familiarity drives engagement.

If you stay inside that loop, the world starts to look smaller than it is.

That’s not a gender issue. That’s a navigation issue.

Feeds Reflect Proximity, Not the Field

LinkedIn rewards posts that touch cultural fault lines. Gender gets engagement. Representation gets engagement. Asking the question, “Where are they?” gets engagement. It invites people to jump in and help. It turns the comments into a resource directory.

Which is exactly what happened.

The labor shifted downward.

Instead of demonstrating deep search work, the thread became the search work.

Representation Requires Execution

Now, to be clear, gender imbalance in AI is real. Senior roles skew heavily male. Funding skews heavily male. Executive titles skew heavily male. That conversation matters.

What also matters is execution.

If you run a strategy firm and you can’t locate highly visible women in AI before posting about it, I’m curious about the process. What keywords were used? How much time was spent beyond first-degree connections? Which circles were explored outside venture-backed tech clusters? How far beyond the immediate network did the search go?

Those questions are practical. They don’t perform as well on a feed. They don’t sound dramatic.

But they matter.

Division Thrives on Engagement Cycles

There’s another layer here that fascinates me.

When posts like this go live, the comments quietly divide into camps. Some celebrate the intent. Some express exhaustion. Some feel unseen despite years of visible work. The tone gets charged fast.

Attention systems love that charge. Division stretches conversation cycles. The platform benefits from longer threads.

Meanwhile, the operational issue stays simple.

AI is shaping hiring decisions, compliance systems, product design, and governance structures. The people in those rooms influence outcomes that touch millions. Finding strong voices for those conversations takes discipline. It takes curiosity. It takes more than scrolling your feed.

Some women in AI prefer research over self-promotion. Others don’t chase viral content. Many build serious infrastructure without branding themselves as influencers (including yours truly).

If your discovery model relies mostly on who shows up in your algorithmic field of view, you’re going to miss people.

Feeds are not directories. Instead, they reflect prior behavior.

So when I see “Where are the women?” I hear something different.

I hear “My network didn’t surface them.”

That’s solvable.

Leadership in Algorithmic Environments

This is why I talk about terrain so much.

Every digital environment has incentive patterns. Visibility flows through particular channels. Certain tones travel farther. Certain identities trigger engagement spikes. If you don’t understand those mechanics, you end up diagnosing scarcity where there’s simply limited exploration.

None of this requires anger. It requires awareness.

Representation conversations matter. So does execution and search discipline and network expansion. Algorithm literacy matters.

A strong leader expands their perimeter before declaring a deficit.

A serious search leaves footprints.

When someone asks where a group of capable people is, the first map worth examining is their own.

Because sometimes the missing piece isn’t representation.

It’s range.