Everyone's Talking About AI - But Who's Building It?

The AI market is projected to be worth US$243.72 billion in 2025.  (Techinformed.com).  From headlines to hashtags, artificial intelligence is everywhere. It's reshaping industries, driving billion-dollar businesses, and in Healthcare; spotting diseases, predicting patient risk, accelerating trials and helping doctors make faster decisions. You’d be forgiven for thinking AI is the most exciting thing happening in tech right now—because it is.

But the question people aren’t asking is:

Who’s actually building this AI? And what does it mean when most of the builders look the same?

Beneath the Progress

Despite all the hype, the teams developing AI today are still overwhelmingly male and overwhelmingly lacking in diversity across gender, race and background. According to the Now AI Institute (Guardian - 'Disastrous lack of diversity in AI perpetuates bias). Diversity disaster has contributed to flawed systems that perpetuate gender and racial bias.

Consider this:

·         At the largest AI research labs (think OpenAI, DeepMind, Meta AI), women make up less than 20% of technical staff.

·         In machine learning conferences like NeurIPS or ICML, the percentage of female authors is still hovering in the low teens.

·         Women of colour? Often less than 1% in technical AI research roles globally.

·         UK-based studies show that CVs with male names receive more callbacks for tech roles. According to Hired female applicants are 13.2% less likely to get an interview (State of Wage Inequality in Tech)

For something as transformative as AI—which influences hiring algorithms, healthcare diagnostics, and even war strategies—this imbalance isn’t only a diversity issue. It’s a design flaw.

Why Representation Matters in AI

It matters deeply, not just for fairness but for the integrity, safety and societal impact of the technology itself and the outcome they can bring. 

It’s built by humans and reflects the values and assumptions of those that design them.  Capturing the blind spots of the people who create it. When the builders of AI systems are not representative of the world they’re shaping, we all pay the price. 

Those that build AI determine how it behaves.

This has been apparent with:

Recruitment algorithms that downgrade CVs with female-coded language..

These are consequences of the lack of representation in the data and development teams.

If we want a future that works for everyone, it needs to be built by everyone.

It’s about building better, safer and more inclusive technology.

So Why Aren’t More Women Building AI?

It’s not about talent. Women make up more than a third of STEM graduates. Many building startups, and mastering AI tools in growing numbers. But they face barriers at every level with just over 12% of STEM executives being women World Economic Forum Research.

• Lack of early encouragement in computer science and engineering.

• Male-dominated academic and workplace cultures that can be hostile or exclusionary.

• Fewer mentorship opportunities and career role models.

• A persistent pay and promotion gap, even in forward-thinking tech companies.

  
  

With Women under 25 earning 29% less than their male counterparts (WomeninTech.co.uk)

And let’s not forget: retention is just as big a challenge. Many women who enter AI and tech fields leave within 5–7 years, often citing burnout, bias, or lack of support.

What Needs to Change?

We can't just rely on individual women to "lean in" and fix this. Systemic issues need systemic solutions:

• Equitable hiring and promotion practices in AI labs and startups. Use inclusive language.

• Mentorship and sponsorship programs to guide their careers, actually that are actually resourced and measurable.

• Reward contributions equally

• Educate counter negative stereotypes of women in tech

It’s not enough to have women on the sidelines of AI. We need them at the whiteboard, the codebase, and the executive table.

The Bottom Line

AI is  shaping the future—but right now, it’s being shaped by a narrow population. If we want a future that works for everyone, it needs to be built by everyone.

What do you think it’ll take to bring more women into AI—beyond the usual platitudes? Drop your thoughts below or tag someone changing the game.