What "Meritocracy" Actually Gets Wrong

When people in tech call the industry a meritocracy, they usually mean something flattering: we hire and promote based on skill alone, not background or connections. It's a comforting story, and an old one. Michael Young coined the term in 1958 as a joke—an ironic critique of the English class system and its faith in academia and government, not an endorsement of it.

The myth is seductive precisely because it flatters those inside the system. If you've achieved stature, money, or influence, the story goes, you must simply be more able. That narrative of self-worth is easy to believe, and it may even sometimes be true. An industry can allocate promotions, funding, and talent based on demonstrated skill. But that causal chain is rarely clean. Success is amplified: more money, more respect, more retweets and upvotes mean more opportunities to succeed again. We reward people for being successful, and that makes them more likely to succeed further.

The Selection Problem

Even if the system works as advertised, it raises an uncomfortable question: how do we know we picked the best people? An organization can point to its overall success as proof its process works. But if success is self-reinforcing, a wide range of people might have done just as well. The people we didn't select are, by definition, invisible to that evaluation.

Who exactly are we not selecting? The data is stark. In a decade of working in operations, IT, and software development, the author reports working closely with zero women and two people of color. Many startups they interviewed at had no women on staff at all. Women hold only 27% of computer science jobs. They make up 10% of the applicant pool for physics tenure-track positions and 18% of hires. In chemistry, women are 21.6% of applicants and take 16.3% of positions. Mathematics doctorates are roughly three-quarters male. Even when women do land STEM jobs, they earn 14% less than their male counterparts.

This imbalance starts long before the interview stage. Girls show high test scores and interest in STEM through middle school. Women are well-represented in biology and earth science—both challenging technical disciplines. Yet they aren't even applying to physics and technology roles. Most engineers would love to interview more women, but the applicant pool itself is 90% male or 90% white. No amount of "meritocratic" hiring fixes that upstream filter.

What the Research Shows

The American Association of University Women's report Why So Few summarizes a large body of literature on this gap. The findings point to systematic factors rather than innate ability:

  • Men consistently outperform women in spatial reasoning tasks, but short training courses dramatically improve spatial skills—suggesting these differences are not fixed.
  • Women more often report hostile departmental cultures and leave the workplace earlier than men. The tenure track experience in physics or math, for example, is a common point of friction.
  • People judge women as less competent in "masculine" fields unless they are clearly successful—at which point they are considered less likable.
  • Recent shifts in girls' mathematical achievement indicate culture and education play major roles in steering girls toward or away from STEM.
  • Stereotype threat—being told that "men are better at coding" or "Asians are better at math"—reliably produces performance effects in tests, and awareness of it reduces its power.
  • Girls are less likely to interpret academic success in science and math as a sign they belong in those fields, so they don't pursue the classes, books, or degrees that would get them there.
  • Interest and ability aside, women face real workplace barriers: lack of family leave, no on-site childcare, few female mentors or colleagues, smaller paychecks, and being passed over for promotions.

The Loop Isn't Closing

These factors explain why engineering teams are overwhelmingly—often exclusively—male, why conferences are almost all white, and why pull requests come overwhelmingly from people who look like the author. This is what a meritocracy looks like in practice.

The uncomfortable conclusion is that success, ability, and even motivation are statistically shaped by a confluence of family, nutrition, housing, genetics, education, role models, and peers. Hiring without looking at gender or race isn't enough. The people hired today become the parents, teachers, and mentors of tomorrow. They influence whether the next generation of girls plays with logic games, builds things, and takes the math classes that lead to technical careers.

The question isn't whether the industry has good intentions. It's how many more years of "we just can't find qualified women" it will take before the feedback loop finally closes—or whether we'll keep waiting for a pool of candidates that the system itself never created.