Lessons from the PyLadies Panel “From Contributor to Founder”
There is a prevailing myth in the world of technology, a story that has been told over and over of how companies are born. The story usually goes something like this: a young tech entrepreneur, probably in a hoodie, probably somewhere in Silicon Valley, wakes up one morning with a singular, crystal-clear vision of the future. The entrepreneur rallies the support from venture capitalists and disrupts the industry. We are led to believe that to build something impactful, you must start with an ego to match.
But what if that story is wrong?
Recently, a group of women gathered at PyLadies Con 2025 for a panel discussion titled “From Contributor to Founder.” They are part of the Python community, a community of the programming language that powers everything from Netflix algorithms to NASA missions. The moderator, Sheena O’Connell, founder of the software training company Prelude, posed a simple question: “What is your secret sauce?”
The answers unraveled the Great Man Theory of Silicon Valley, thread by thread.
“At no point did I wake up and say, ‘I’m going to be a leader."
“At no point did I wake up and say, ‘I’m going to be a leader,’” responded Deborah Hanus, CEO of Sparrow. Instead, she watched six friends go on leave within a six-month period. She watched them drown in paperwork. She saw them spending hours navigating bureaucracy when they should have been focusing on their health or their families. It annoyed her. It seemed like a solvable problem. So, she solved it.
“It was sort of solving the problem that got me through the first several years."
This is the first lesson of the Accidental Architect: they are not driven by a vision of themselves as a CEO. They are driven by the need to address a specific, tangible pain point. They are not trying to be important; they are trying to be useful.
The women on this panel—Carol Willing, core developer of Python and Project Jupyter and founder of Willing Consulting; Leah Wasser, founder and executive director of pyOpenSci and maintainer of stravalib; Inessa Pawson, founder of Tech Alliance of SWFL, NumPy Steering Council member, open source program manager at OpenTeams; and Deborah Hanus, founder and CEO of Sparrow, all share a background in open source.
Open source community-driven projects often depend on volunteers, people who build software not for pay, but because they want the tool to exist. That reality creates a unique challenge for open source project leaders.
“In most startups, you are not going to be paying top dollar,” Willing explained. “So that ability to motivate volunteers transfers very nicely to people who could be making more down the street at a MAANG company.”
Another way open source experience forges great founders is through the discipline of listening to users. Open source projects that endure aren’t built in isolation; they’re shaped in an ongoing conversation with the people who use them and contribute to their development. That feedback loop makes it possible to build, piece by piece, what Inessa Pawson calls a “Minimum Lovable Product.”
Note the difference in terminology. Silicon Valley loves the MVP—Minimum Viable Product. Viable means it works. Lovable implies an emotional connection. It signals that the creator cares about the user’s experience enough to make it enjoyable, even in its earliest form.
Leah Wasser, who founded pyOpenSci to help standardize scientific software development practices, spoke candidly about the barrier that prevents most contributors from becoming founders. It isn’t a lack of skill. It is perfectionism.
“I tend to be more of a perfectionist type of person that wants things to be really good,” Wasser admitted. She described the daily struggle with the imposter syndrome, the feeling that she doesn’t belong or wasn’t qualified to take on certain work.
Her breakthrough came from a piece of advice she received from Carol Willing: “Take the first step.”
This creates a feedback loop that feels more like navigation than execution. You take a step, check your bearings, and correct the course. It’s the scientific method applied to business: hypothesis as compass, experimentation as movement, uncertainty as the terrain.
“Get comfortable with uncertainty. Most of your decisions should be made with about 70 percent of the information,” Inessa Pawson added. “If you wait for 100 percent clarity, you will never move.”
We tend to think of mentoring as a deep, years-long relationship with a Yoda-like figure who guides you through your entire professional journey. But in today’s fast-moving world, that kind of relationship is rare. Instead, Carol Willing advocates for micro-mentoring. A conversation here. A specific question there.
“It’s asking the questions that help people reflect,” Willing said. “There’s no guarantee that I can give somebody the path.”
Why? Because one true path doesn’t exist. “All the good ones [leaders],” her MIT professor once told Willing, “take a convoluted path.”
When Deborah reached the limits of her own experience running Sparrow, she realized there was no single person who had walked her specific road. So she turned to the “wisdom of the crowd”. She treated leadership like an open system, pulling signal from the noise, borrowing hard-won lessons from the people around her, and stitching them together into a mosaic built for the real world, not a movie script.
“I don’t think there’s a secret sauce. There is just the work to be done.”
There is the willingness to look at a hard problem—like employee leave forms or scientific software packaging—and say, “I can fix this.” There is courage in releasing something imperfect and humility in listening when the community shows you how to make it better and in asking others for help along the way.
“Careers last a long time,” Willing reminded the group. “Take your little step. See what you learn from that.”
It turns out that if you take enough little steps, and you are stubborn enough to keep going, you might just look back and realize that you built something truly impactful. And you have become a leader, without ever really intending to be one at all.
Every January, the global economic conversation moves to a small alpine town in Switzerland. Heads of state, founders, and technologists gather in Davos to debate the future. This year, beneath the familiar headlines about geopolitical tension and economic uncertainty, another theme dominated nearly every private conversation: artificial intelligence.
