Everyone Is Worshipping the Wrong AI Heroes—What Hidden Figures Teaches Us About This Moment

Three lessons from three Black women who changed history—and what they reveal about who we’re erasing in the AI revolution right now.

https://commons.wikimedia.org/wiki/File:Hidden_Figures_cast_at_the_Kennedy_Space_Center_%2830765657104%29.jpg

In 1962, John Glenn refused to board the Friendship 7 capsule until one person had manually verified the IBM computer’s orbital trajectory calculations.

As part of the preflight checklist, Glenn asked engineers to “get the girl”—a 44-year-old Black mathematician named Katherine Johnson—to run the same orbital equations by hand on her desktop calculating machine [1]. His reported words: “If she says they’re good, then I am ready to go [2].”

The space race story the world remembers is astronauts and rocket scientists. The story that kept Glenn alive was about a mathematician the world forgot for fifty years.

We are making the exact same mistake in AI. Right now. In real time.

The myth we are already building

Open any major publication. Scroll any tech conference lineup. The story of AI has a predictable cast: founders on stages, researchers with viral papers, CEOs on magazine covers.

The narrative has already been compressed into a familiar shape—lone geniuses, moonshot companies, and billion-dollar bets. The same flattening that erased Katherine Johnson, Dorothy Vaughan, and Mary Jackson from the space race story is actively happening again, in a different field, with different names.

“If we repeat the same visibility mistakes in the AI era, we’ll repeat the same injustices — and build worse systems.”

The people doing the work that actually makes AI reliable, honest, and safe are largely invisible. Data annotators — the workers who label training data — are often paid less than $2 per hour and listed nowhere in the model papers they make possible [3]. Red teamers, evaluation researchers, ethicists, and UX researchers running studies with communities most harmed by model failure rarely appear in headlines.

We’ve seen this movie before—literally.

Three women, three roles, one lesson that didn’t stick

Hidden Figures (2016), based on Margot Lee Shetterly’s book of the same name, introduced most of the world to three women who were essential to NASA’s success—and had been erased from its official history for decades. Today, each role in AI systematically undervalues the contributions of these women.

What the space race story cost us—and what AI is about to cost

The flattening of the space race into “astronauts and rocket scientists” wasn’t just historically inaccurate. It had real consequences.

It meant the cognitive labor of hundreds of human computers — a workforce that was disproportionately female and significantly Black — went uncredited, uncompensated at a level commensurate with its value, and uninvited into leadership decisions about the programs they made possible.

AI is replicating this history precisely. The workers who label training data, moderate harmful content, and evaluate model outputs are the invisible foundation of every AI system you use—yet they are paid less than $2 per hour often, operating through precarious third-party contracts, and listed nowhere in the papers or products they make possible [3]. In May 2024, nearly 100 Kenyan data labelers wrote an open letter to President Biden describing their conditions as “modern-day slavery [7].”

Photo by NASA on Unsplash

The pattern repeating

The people closest to the edge cases, the failures, and the “boring” data work are the ones who actually determine a system’s trajectory. In aerospace, that was the human computers. In AI, it is the annotators, red teamers, ethicists, and community advocates. Anthropologist Mary L. Gray and computational social scientist Siddharth Suri documented this pattern in their 2019 book Ghost Work — the term now widely used to describe the invisible human labour underpinning automated systems. History has already told us what happens when we ignore these workers. We just haven’t been paying attention.

Three things you can do differently

This isn’t a piece about guilt. It’s a piece about pattern recognition. The good news about patterns is that once you see them, you can interrupt them.

1. Name the people doing the unglamorous work. Next time you cite an AI paper, look at who isn’t credited. Next time you share a model benchmark, ask who built the evaluation set and under what conditions. Consider redirecting some of the credit that naturally flows toward visibility.

2. Stop treating “non-technical” as a hierarchy. Domain expertise, ethical reasoning, community knowledge, and UX research are what make AI systems work for actual humans. The fact that FORTRAN was new to Dorothy Vaughan did not diminish her value. The experts in your field who are learning to work with AI are not lesser people. They are essential.

3. Show up for the structural fights. Mentoring, citing underrepresented researchers, supporting AI governance initiatives that include affected communities — these are not side projects. In the Hidden Figures story, the main plot wasn’t the rocket. It was a fight to be in the room.

When we tell the story of this AI era thirty years from now—when we make the documentary, write the biography, and build the exhibit—whose names will we have to rediscover?

More importantly, whose names are we actively erasing right now, while there is still time to stop?

Katherine Johnson’s name was missing from NASA’s history for fifty years. We found it eventually. But we only needed to find it because we lost it first.

We don’t have to do this exercise again.

Who are the hidden figures in your field?

If this reframing was useful, follow for more essays on AI, cognition, and the choices shaping this moment. Share it with someone doing the unglamorous work — they deserve to see themselves in this story.

Who is the “hidden figure” in your organisation’s AI work — the person doing essential work that nobody is naming out loud?

References

1. NASA Official Biography: Katherine Johnson. NASA Langley Research Center. “As a part of the preflight checklist, Glenn asked engineers to ‘get the girl’ — Johnson — to run the same numbers through the same equations that had been programmed into the computer, but by hand.” nasa.gov/centers-and-facilities/langley/katherine-johnson-biography
2. NASA Science. “Katherine Johnson (1918–2020).” Glenn’s quote: “If she says they’re good, then I am ready to go.” Johnson co-authored more than two dozen technical papers over her 33-year career. science.nasa.gov/people/katherine-johnson. Also: IEEE Spectrum (2023), “Katherine Johnson, the Hidden Figures Mathematician Who Got Astronaut John Glenn into Space.”
3. Gray, M.L. & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. HarperCollins. Also: TIME investigation (2023) — Kenyan data workers for ChatGPT paid less than $2/hr via Sama. UCLA Anderson Review (2025): “Ghost Workers in the AI Economy.” ILO (2025): “The Artificial Intelligence Illusion: How Invisible Workers Fuel the Automated Economy.”
4. NASA Official Biography: Dorothy Vaughan. “During her 28-year career, Vaughan prepared for the introduction of computers in the early 1960s by teaching herself and her staff the FORTRAN programming language. She later headed the programming section of the Analysis and Computation Division.” nasa.gov/people/dorothy-vaughan. Also confirmed by Wikipedia (Dorothy Vaughan) and Science Museum Blog (2021).
5. NASA Official Biography: Mary W. Jackson. “The classes were held at then-segregated Hampton High School. Mary needed special permission from the City of Hampton to join her white peers in the classroom… Mary completed the courses, earned the promotion, and in 1958 became NASA’s first black female engineer.” nasa.gov/people/mary-w-jackson-biography
6. Wikipedia: Mary Jackson (engineer), sourced from NASA biography and Britannica. “In 1979… she made a final, dramatic career change, leaving engineering and taking a demotion to fill the open position of Langley’s Federal Women’s Program Manager.” en.wikipedia.org/wiki/MaryJackson(engineer)
7. the World/Science Array (2025), citing open letter: “In May 2024, nearly 100 Kenyan data labelers wrote an open letter to U.S. President Joe Biden describing conditions they called ‘modern-day slavery.’” Also: CWA (2025) — “Ghost Workers in the AI Machine.” Center for Digital Society (2025). Rest of World / Hanna & Bender, The AI Con (2025).

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