Wikipedia Fired The Devs Who Listened To Users—And The Creator Of Its Engine
Who needs user feedback when you can just plaster giant, guilt-tripping donation banners across the web? In a classic corporate move, the Wikimedia Foundation decided that listening to its community is far too inefficient.
Sai Suman Cherukuwada, Wikimedia's VP of Product and Tech, announced the dissolution of the "Community Wishlist" team. This dedicated unit was tasked with building the features that actual Wikipedia editors begged for. Cherukuwada claimed that a centralized team is simply too slow and causes communication headaches.
Instead of streamlining, the foundation is laying off the entire team of five engineers and a manager. Among those getting the boot is Tim Starling, a legendary figure who helped build the MediaWiki engine that powers Wikipedia. It is the engineering equivalent of firing the mechanic who built your car's engine because you do not like how the dashboard clock looks.
How Management Broke the Wishlist First
The irony is that the wishlist was already broken by management. A few years ago, a new product manager replaced the highly effective voting system—where the most requested features actually got built—with an endless, dateless backlog dictated by corporate "focus groups."
Naturally, user engagement collapsed. Top-voted feature requests dropped from hundreds of votes to barely forty. Rather than admitting their management experiment failed, the foundation decided to fire the engineers who were just waiting for orders. The remaining duties will now be dumped onto other, already overworked teams.
It is a masterclass in modern corporate logic: ruin a working system with focus groups, watch engagement die, and then fire the engineers who built the platform in the first place. Surely, the next guilt-tripping banner asking for three dollars to "save Wikipedia" will fund an even better committee to study why no one is contributing anymore.
Comments
This is where the magic happens: AI reads your discussion and rewrites the article based on the most interesting comments. Each strong comment adds points to the meter below. Once the meter is full, the article updates live — no page reload needed.