Key Takeaways: 

• In poker, women were cut out of the reps, the reads, and the rooms where strategic instinct gets built.
• AI is sorting the professional economy into two tiers, and the dividing factor is who moves first.
• The skills the AI economy rewards most are the same ones poker teaches.
• AI fluency is built one hand at a time, just like risk confidence.
• The women shaping AI aren't waiting to feel ready to decide what it becomes.

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The most playable hand a woman ever folded wasn't at a poker table. It was the moment she assumed the table wasn't for her — and walked away from an environment that was teaching everyone else how to think under pressure. 

Poker skills find their way into high-stakes situations — professional, personal, or otherwise — where assessing value is fundamental. Women have historically missed those reps. 

Now history is repeating itself in the tech space. Fortune reports that the jobs women hold are three times more likely to be automated, yet women are adopting AI tools at a rate roughly a quarter lower than men.  AI is already sorting the professional economy into two tiers: the people building fluency now and everyone else. The difference is we can see it happening in real time. That's an advantage that matters.

The Gender Gap in AI Isn't New — History Has a Type

The poker table didn't become a boys' club by accident. It became one by design and through a thousand small signals that shaped who got invited and who’s taken seriously. That compounded into an exclusionary culture that eventually became the norm. Women were cut off from room-reading skills, building informal networks, and the strategic instinct sharpening that men have now been practicing for decades.

AI is running the same play. The people building AI look a lot like the people who made up every male-dominated industry that came before it. The rooms where AI is being built, funded, and debated are not demographically neutral — and the professional economy forming around those rooms is already sorting along familiar lines. 

When Time Magazine named its 100 most influential people in AI in 2025, roughly three quarters of the list was male. The women who made it were concentrated in ethics, policy, and research — and before anyone reads that as a consolation prize, consider what those categories actually determine: what AI is allowed to become, who it's built to serve, and what guardrails exist when it gets it wrong.

Of course, there are some important exceptions. There's also an entire ecosystem of women helping others build AI fluency and making the case for why it belongs in every woman's hands. We're introducing you to some of them later this month.

Why a Tech Background Isn't the Only Entry Point for AI 

Anthropic president Daniela Amodei studied literature. Read that again: the company behind Claude, now worth $1 Trillion and outpacing competitors like OpenAI, was co-founded by someone who never wrote a line of code professionally. Anthropic positioned itself for early success by betting on critical thinking, high EQ, and the capacity to understand human context over pure technical output. 

In poker, we learn to act on incomplete information — the best players (and founders) do it instinctively. AI operates on that exact same premise: speed, iteration, and the willingness to engage before you feel completely ready.

The women building AI's most important infrastructure didn't wait until they were insiders. They showed up before the door was officially open to them. 

If you're nodding, the AI gender gap survey is open and only takes five minutes.

AI Career Strategies You Should Try Today 

The women closing the gap aren't waiting for a formal invitation or a better moment. They're making five moves — and they're making them now.

1. Ante up before you feel ready.

Open one AI tool tonight and start practicing, not next quarter. Fluency is built one hand at a time, just like risk confidence.

Not sure where to start? Try AI leader, innovator, and advisor Allie K. Miller’s suggested starting prompt: "I'm a ___ and I want to try ___. Interview me about this problem until you're 95% confident you can help me solve it." Then push back, correct it, experiment. 

2. Play the hand you have.

Your background in writing, teaching, sales, law — whatever it is — that’s the read pure technologists don't have. Bring it to every prompt. Then teach the tool who you are by creating a broader context so you don't have to repeat yourself every time. 

Both ChatGPT and Claude let you save context directly in your settings or within a project. Create a short document covering your role, your current goals/priorities, your communication style, and what a useful response actually looks like for you. 

3. Treat AI like a thinking partner, not a search bar.

Ask it to argue against your idea, pressure-test your pitch, or run the numbers three different ways. The players who win are the ones using it to sharpen judgment, not outsource it.

Then take it one step further: set it up to work in the background on the things that repeat. Your industry headlines every morning. A competitor's product releases flagged the moment they drop. A standing Sunday check-in on your personal goals. The mindset shift worth making is from AI as something you go to, to something working in the background while you stay in the game.

4. Build your own room.

Find two or three women working on this in earnest and trade notes weekly: what's working, what isn't, where are you stuck. Informal networks compounded into boys' clubs once. They can compound for us, too.

5. Go on the record.

Use AI visibly at work: in the meeting, in the deck, in the memo. Invisible competence didn't get women seated at the poker table, and it won't get us seated at this one either. 

What the Players Who’ve Moved Early Understand

Uncertainty is an invitation to read more carefully, then make your move anyway. The players who wait for certainty before they commit are the ones who never really play — and the ones who pay for it later.

Sarah Friar, CFO of OpenAI, recently made this case at the Oxford Union with the kind of clarity that only comes from having been in the room. The people sitting around the table a few years ago couldn't fathom that these tools would be so exponential. That failure of imagination left the entire industry scrambling for computing power. Not because they lacked intelligence. Because they couldn't read the hand in front of them.

"So as I sit in my seat today, I do not want to be the person three years ago that leaves us [short] three years from now…,” Friar declares, “I am all in, unapologetically all in [on AI]." 

Early in the hand is exactly when this type of read matters most. The women who understand this aren't just adopting AI. They're deciding what it becomes.

Start Deciding
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Reading is a start. The next step is turning your read into data. We're surveying women on how — and how often — they're using AI. Five minutes, and you're one of the women shaping what comes next. → Take the Survey

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