"We have data on the performance of >50k engineers from 100s of companies. ~9.5% of software engineers do virtually nothing: Ghost Engineers.”
Last week, a tweet by Stanford researcher Yegor Denisov-Blanch went viral within Silicon Valley. “We have data on the performance of >50k engineers from 100s of companies,” he tweeted. “~9.5% of software engineers do virtually nothing: Ghost Engineers.”
Denisov-Blanch said that tech companies have given his research team access to their internal code repositories (their internal, private Githubs, for example) and, for the last two years, he and his team have been running an algorithm against individual employees’ code. He said that this automated code review shows that nearly 10 percent of employees at the companies analyzed do essentially nothing, and are handsomely compensated for it. There are not many details about how his team’s review algorithm works in a paper about it, but it says that it attempts to answer the same questions a human reviewer might have about any specific segment of code, such as:
- “How difficult is the problem that this commit solves?
- How many hours would it take you to just write the code in this commit assuming you could fully focus on this task?
- How well structured is this source code relative to the previous commits? Quartile within this list
- How maintainable is this commit?”
Ghost Engineers, as determined by his algorithm, perform at less than 10 percent of the median software engineer (as in, they are measured as being 10 times worse/less productive than the median worker).
Denisov-Blanch wrote that tens of thousands of software engineers could be laid off and that companies could save billions of dollars by doing so. “It is insane that ~9.5 percent of software engineers do almost nothing while collecting paychecks,” Denisov-Blanch tweeted. “This unfairly burdens teams, wastes company resources, blocks jobs for others, and limits humanity’s progress. It has to stop.”
The Stanford research has not yet been published in any form outside of a few graphs Denisov-Blanch shared on Twitter. It has not been peer reviewed. But the fact that this sort of analysis is being done at all shows how much tech companies have become focused on the idea of “overemployment,” where people work multiple full-time jobs without the knowledge of their employers and its focus on getting workers to return to the office. Alongside Denisov-Blanch’s project, there has been an incredible amount of investment in worker surveillance tools. (Whether a ~9.5 percent rate of workers not being effective is high is hard to say; it’s unclear what percentage of workers overall are ineffective, or what other industry’s numbers look like).
Over the weekend, a post on the r/sysadmin subreddit went viral both there and on the r/overemployed subreddit. In that post, a worker said they had just sat through a sales pitch from an unnamed workplace surveillance AI company that purports to give employees “red flags” if their desktop sits idle for “more than 30-60 seconds,” which means “no ‘meaningful’ mouse and keyboard movement,” attempts to create “productivity graph” based on computer behavior, and pits workers against each other based on the time it takes to complete specific tasks.
What is becoming clear is that companies are becoming obsessed with catching employees who are underperforming or who are functionally doing nothing at all, and, in a job market that has become much tougher for software engineers, are feeling emboldened to deploy new surveillance tactics.
“In the past, engineers wielded a lot of power at companies. If you lost your engineers or their trust or demotivated the team—companies were scared shitless by this possibility,” Denisov-Blanch told 404 Media in a phone interview. “Companies looked at having 10-15 percent of engineers being unproductive as the cost of doing business.”
Denisov-Blanch and his colleagues published a paper in September outlining an “algorithmic model” for doing code reviews that essentially assess software engineer worker productivity. The paper claims that their algorithmic code assessment model “can estimate coding and implementation time with a high degree of accuracy,” essentially suggesting that it can judge worker performance as well as a human code reviewer can, but much more quickly and cheaply.
I asked Denisov-Blanch if he thought his algorithm was scooping up people whose work contributions might not be able to be judged by code commits and code analysis alone. He said that he believes the algorithm has controlled for that, and that companies have told him specific workers who should be excluded from analysis because their job responsibilities extend beyond just pushing code.
“Companies are very interested when we find these people [the ghost engineers] and we run it by them and say ‘it looks like this person is not doing a lot, how does that fit in with their job responsibilities?’” Denisov-Blanch said. “They have to launch a low-key investigation and sometimes they tell us ‘they’re fine,’ and we can exclude them. Other times, they’re very surprised.”
He said that the algorithm they have developed attempts to analyze code quality in addition to simply analyzing the number of commits (or code pushes) an engineer has made, because number of commits is already a well-known performance metric that can easily be gamed by pushing meaningless updates or pushing then reverting updates over and over. “Some people write empty lines of code and do commits that are meaningless,” he said. “You would think this would be caught during the annual review process, but apparently it isn’t. We started this research because there was no good way to use data in a scalable way that’s transparent and objective around your software engineering team.”
Much has been written about the rise of “overemployment” during the pandemic, where workers take on multiple full-time remote jobs and manage to juggle them. Some people have realized that they can do a passable enough job at work in just a few hours a day or less.
“I have friends who do this. There’s a lot of anecdotal evidence of people doing this for years and getting away with it. Working two, three, four hours a day and now there’s return-to-office mandates and they have to have their butt in a seat in an office for eight hours a day or so,” he said. “That may be where a lot of the friction with the return-to-office movement comes from, this notion that ‘I can’t work two jobs.’ I have friends, I call them at 11 am on a Wednesday and they’re sleeping, literally. I’m like, ‘Whoa, don’t you work in big tech?’ But nobody checks, and they’ve been doing that for years.”
Denisov-Blanch said that, with massive tech layoffs over the last few years and a more difficult job market, it is no longer the case that software engineers can quit or get laid off and get a new job making the same or more money almost immediately. Meta and X have famously done huge rounds of layoffs to its staff, and Elon Musk famously claimed that X didn’t need those employees to keep the company running. When I asked Denisov-Blanch if his algorithm was being used by any companies in Silicon Valley to help inform layoffs, he said: “I can’t specifically comment on whether we were or were not involved in layoffs [at any company] because we’re under strict privacy agreements.”
The company signup page for the research project, however, tells companies that the “benefits of participation” in the project are “Use the results to support decision-making in your organization. Potentially reduce costs. Gain granular visibility into the output of your engineering processes.”
Denisov-Blanch said that he believes “very tactile workplace surveillance, things like looking at keystrokes—people are going to game them, and it creates a low trust environment and a toxic culture.” He said with his research he is “trying to not do surveillance,” but said that he imagines a future where engineers are judged more like salespeople, who get commission or laid off based on performance.
“Software engineering could be more like this, as long as the thing you’re building is not just counting lines or keystrokes,” he said. “With LLMs and AI, you can make it more meritocratic.”
Denisov-Blanch said he could not name any companies that are part of the study but said that since he posted his thread, “it has really resonated with people,” and that many more companies have reached out to him to sign up within the last few days.
I’ve seen it first hand but I don’t know if 9.5% is the correct number. One software guy at my company works for 11 years at this company. He went through so much shit that at this point he doesn’t even sit under the software department anymore, he’s just under finance. All he does is upgrade GitLab once every quarter or so and then he just watches TV and messes around with his homelab in his free time. Comes to the office couple times a week for 3-4 hours to show everyone he is still alive then goes home.
It has not been peer reviewed.
I could make a paper in 5 minutes about how AI can be used to uniquely identify people by smelling their farts. Doesn’t mean anything unless it’s been peer reviewed.
Until this paper has been peer reviewed, I give it as much credit as I give a flat earth conspiracy person.
How much hubris/ignorance this guy has to believe his algorithm is accurate enough to detect “10%” of employees were deadbeats? What precision! If it found 50% deadbeats, that would mean the algorithm might be working.
The worst companies have only 10% deadbeats? Any company with only 10% deadbeats means their management team is doing a great job hiring/managing. Any company that only 50% deadbeat managers would be outstanding.What a fucking snitch. 9.5% of engineers gotta go, but the CEO getting paid buckets and buckets of money isn’t draining the company? Fire 9.5% of engineers that actually have knowledge and are skilled enough to demand a high price for their skills, or CEO fuck-all who comes in via zoom once a quarter and couldn’t open a pdf if they’re life depended on it. Hmm, what a hard choice 🤔
Ha! Hahaha! Hahahahaha!
Do you want AI to push garbage/useless code to push garbage/useless metrics? Because this is how you get your most skilled employees to do that.
“hey guy we had to let Jon go. His numbers just weren’t holding up over the last two quarters”
“Wtf?! That’s our team lead! Who’s going to sit with product and tell them No when they ask for something insane?”
“Yeah! Who’s going to help with our PR review?”
“And what about the juniors? He always made it a point to do pairing sessions with them!”
They are called managers not ghost engineers.
Alternatively they are on an engineering team and providing their expertise via other means beyond code submission. This entire thing sounds like a sledgehammer trying to do the work of a scalpel.
Reviews, planning, teaching, mentoring, testing produce little code.
Well, writing test scripts can produce shit-tons of code.
Yes, often more than the actual code. However there’s also manual testing, observing users for usability obstacles, visiting clients, and stuff like that.
Developing standards, best practices, conventions, etc. One of the most valuable people on my team wrote some incredible quality automations a few years ago, and the only coding he does at this point is updates to them when necessary. By volume, he’s easily bottom 5% this year, but we’d be much worse off without his expertise/advise and the fact he advocates for the team.
This is classic shit management metrics. It would take some time for the rot to set in after using a cudgel approach to a team, and by the time it did, the assholes responsible would have fucked off elsewhere with their huge bonuses.
Yeah, one of my projects right now has been delivering huge value with very few staff-hours being expended in coding. That’s because I (senior architect) and a couple software engineers researched the shit out of it before we started, and found a way to adapt free, existing, running code with minimal effort. I’ve seen two previous attempts to do this job fail expensively and catastrophically. So far, we’ve spent 15% of what either predecessor project cost, and we’ve already got operational code deployed and a solid proof of concept for the rest. That’s because of months of hard thinking and experimentation by my engineers and me. And yeah, that’s right, it meant doing some Big Design Up Front, and fuck you to every agile fanboi who thinks you can accomplish a highly complex integration project without doing that. We’ve already had a couple of those knobheads lose their jobs for failing at previous attempts, then opposing my approach. I’m hiring more real engineers with the freed-up headcount.
Some of this work is irreducibly hard and anyone who thinks they can factorize it into a bunch of parallelized trivial processing doesn’t know the problem space. Snitchware and truncheonware are not going to change that.
One of the best engineers I’ve worked with produced very little code at that point in his career. His primary responsibility was to do the research and planning that empowered the rest of the team to move quickly. Without a doubt, that team was far more productive due to his efforts. When needed, he could quickly whip out some top notch code, and he was heavy involved in the code review process. Writing code just wasn’t how he could deliver the most value.
Or architects, infrastructure engineer… plenty of peripheral functions are hired as « IT engineers » and not pushing code in a repo. What a weird article.
Sneaking the old “this is why people want remote work” in there certainly makes this feel like something big tech created to push RTO.
For real. Dude seems to think people can jack around and do nothing in office
The thing about being a big organization is that you need to have slack capacity most of the time in order to be able to go quickly in a different direction at certain times. If you don’t have excess capacity sitting idle, an unforeseen event can paralyze you
And slack capacity can be used effectively e.g., spend some time on process improvement. There’s always some saw to sharpen or some technical debt to repay.
I’ve noticed where I work a large number of employees who just seem to host meetings to justify their own existence.
They plan meetings, they have meetings to talk about other meetings. I’m really not sure what else they do.
We sell remote control radios to industry. I’ve spent a week talking a global customer about their issues. 1 bit is wrong in their data stream.
Typing more isn’t always doing more.
Yeah fr sometimes I need to sit on a problem for a week or talk with coworkers or other teams before a solution presents itself. Programming isn’t just writing code, that’s practically the last step.
Hell I spend most of my day just reading the old code and the docs just in case I find an opportunity to massively optimize things, and those have been some of my best projects.
The legendary 0.1x engineer
“We have to let you go as from our analysis you do mostly nothing, mr senior engineer”
1 week later everything is crashing and no one knows why
Ah yes, the classic evaluation of stupid shit that ends up shooting the company in the foot.
Yep.
This question doesn’t address what else these engineers do besides write code.
Who knows how many meetings they’re involved in to constrain the crazy from senior management?
Who knows how many meetings they’re involved in to constrain the crazy from senior management?
This is more than half of my job. Telling the company owners/other departments “No”. Or changing their request to something actually reasonable and selling them that they want that instead.
Sometimes the only way to get heard is for them to go attempt the simple, stupid approach and fail. Then their successors might pay attention.
Yes, but there’s also people actually not doing anything. I am dev lead and after building a team, which was a lot of work, I am at a point where I am doing fuck all on most days. Maybe join a few meetings, make some decisions and work on my own stuff otherwise.
Yeah, there are plenty of truly pointless workers, I’m not denying that. But doing stupid metrics like commit counting or lines of code per day is stupid and counter productive, and it emphasizes the out of touch and inhuman methods of corporate idiots
Makes me think of a trend in FTP gaming, where there was a correlation between play time and $ spent, so gaming companies would try and optimise for time played. They’d psychologically manipulate their players to spend more time in game with daily quests, battle passes, etc, all in an effort to raise revenues.
What they didn’t realise was that players spent time in game because it was fun, and they bought mtx because they enjoyed the game and wanted it to succeed. Optimising for play time had the opposite effect, and made the game a chore. Instead of raising revenues, they actually dropped.
This is why you always have to be careful when chasing metrics. If you pick wrong, it can have the opposite effect that you want.
When your data “scientists” don’t understand the difference between causation and correlation
And why economists and sociologists are important to have in the room when marketing and sales heads throw stupid fucking ideas on the table.
This is why you always have to be careful when chasing metrics. If you pick wrong, it can have the opposite effect that you want.
I don’t know where the adage came from but I find it very true:
Once you turn a metric into a target, it ceases to be a good metric.
Goodhart’s law! One of my personal favorites after working in the field of healthcare regulatory reporting.
They’re actually better known as Program and Product Managers. When engineers get tired of actual work, they try to convert to these jobs.
I think most people misunderstand what software engineers do. Writing code is only a small portion of the work for most. Analyzing defects and performance issues, supporting production support that ends up with unqualified people due to the way support us handled these days, writing documentation or supporting those who do, design work, QE/QA/QC support, code reviews, product meetings, and tons of other stuff. That’s why “AI” is not having any luck with just replacing even junior engineers, besides the fact that it just doesn’t work.