As many as one in 10 coders are ‘ghost engineers’ Stanford researcher says, lurking online and doing no work

• A Stanford researcher says his algorithm pinpoints employees who are doing the bare minimum.
• Roughly 9.5% of coders are “ghost engineers” according to his research, which has not been peer-reviewed.
• The research underscores tech’s newfound mania with rooting out low performers.

Quiet quitting. Lazy-girl jobs. Bare-minimium Mondays.

Over the past two years, employees have expressed repeatedly that they are fed up with being asked to do too much.

Tough luck. The latest catchphrase to describe working less is “ghost engineer” — and it comes not from burnt-out employees but from a Stanford researcher whose team has developed an algorithm to help tech companies identify freeloading coders.

Stanford researcher and former Olympic-level weightlifter Yegor Denisov-Blanch ran the algorithm, which grades the quality and quantity of employees’ code repositories on GitHub, on the work of more than 50,000 employees across hundreds of companies.

Roughly 9.5%, he found, “do virtually nothing.”

Measuring output is difficult

Denisov-Blanch calls these workers “ghost engineers,” defined as software engineers who are only 10% as productive or less than their median colleague.

His research began as an attempt to find a better way to grade the performance of software engineers, he said in an interview with Business Insider.

“Software engineering is a black box,” Denisov-Blanch said. “Nobody knows how to measure software engineers’ performance. Existing measures are unreliable because they rate equal work differently.”

“It’s not fair when someone’s doing a very complicated change that’s only one line of code. And the person doing the very simple change that’s 1,000 lines gets rewarded,” he continued.

His algorithm attempts to resolve that tension, giving high ratings to engineers who write many lines of code only so long as that code is maintainable, solves complex problems, and is easy to implement.

Denisov-Blanch’s research has not been peer-reviewed.

There are other caveats. Industry-wide, the 9.5% figure could be an overstatement because Denisov-Blanch’s research team ran the algorithm only on companies that volunteered to participate in the study, introducing selection bias.

Conversely, while Denisov-Blanch’s team didn’t classify employees whose output is only 11% or 12% of the median engineer’s as “ghost engineers,” there’s a strong argument that those employees aren’t contributing much either, which could mean the 9.5% figure is an understatement.

The hunt for underperformers

Rooting out underperformers has lately become something of a mania among some in Silicon Valley.

In September, Y Combinator co-founder Paul Graham published an essay lauding a management style he called “founder mode,” which he distinguished from the conventional wisdom of, in his words, “hire good people and give them room to do their jobs.”

“In practice, judging from the report of founder after founder, what this often turns out to mean is: hire professional fakers and let them drive the company into the ground,” Graham wrote.

Heading the charge has been Elon Musk, who has spoken proudly about firing 80% of Twitter’s employees after buying the company in 2022. Twitter, now X, didn’t appear to experience significant outages or service interruptions following the staff reduction.

“Were there many mistakes along the way? Of course. But all’s well that ends well,” he told CNN. “This is not a caring-uncaring situation. It’s like, if the whole ship sinks, nobody’s got a job.”

More remote workers were superstar coders

Musk now aims to apply that same ruthless efficiency to the federal government. As co-chair of a new Department of Government Efficiency, he pledged in a Wall Street Journal op-ed to slash federal staffing, including by ending remote work to spur resignations.

“If federal employees don’t want to show up, American taxpayers shouldn’t pay them for the Covid-era privilege of staying home,” Musk wrote.

Denisov-Blanche’s research showed mixed results for remote work. On one hand, he found that the prevalence of “ghost engineers” among remote workers was more than double that among in-person workers.

But he also found that many more of the most effective engineers — employees whose performance was at least five times better than their median colleague — were working remotely than were in-person.