The Ghost Engineer Phenomenon

Confession of an over-employed engineer on Reddit: 

“Boss thinks I'm overworked 😂 During our one-on-one my boss told me he thinks that they are piling too much work on me and he suggested to hire someone else to help me out. Now obviously this would be a disaster since I average about 5 hours a week. So basically I just discussed with my boss how I'm working out ways to deal with time management but they should save the company money and instead push his manager to give me a promotion. So now I'm getting promoted (no extra work just more money) and they are hiring nobody else. Crisis averted!” 

In the second half of 2024, researchers from Stanford University went viral for claims that 9.5% of software engineers at major tech companies get paid big bucks to do virtually nothing. The ongoing research, involving over 50,000 software engineers, is focused on developing a more accurate and effective way to measure software engineering productivity. 

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The researchers coined the term “ghost engineer,” explaining that it refers only to engineers whose primary responsibility is to write code. It excludes engineers in managerial roles and those found to contribute in other ways. To further validate the findings, they confirmed with the participating organizations that these individuals are not performing legitimate ancillary activities that would justify their low-code contributions, such as sales efforts, mentoring, or architecture work. 

The research’s methodology, model, and findings were met with widespread backlash from the software engineering community—similar to when McKinsey released their framework for measuring engineering productivity a year prior. 

However, the phenomenon of appearing hard at work while hardly working is not new. And there is plenty of anecdotal evidence, not to mention 392,000 members on a subreddit devoted to the topic. 

“Everyone thinks this is an exaggeration but there are so many software engineers, not just at FAANG [Facebook, Apple, Amazon, Netflix and Google], who I know personally who literally make ~2 code changes a month, few emails, few meetings, remote work, < 5 hours/ week, for ~$200-300k,” tweeted Deedy Das, a principal at Menlo Ventures, in November 2024. 

Over the last several years, the term “quiet quitting” has spread rampantly across the internet. It refers to doing the bare minimum requirements of one's job and putting in no more time, effort, or enthusiasm than absolutely necessary. 

In light of a Gallup poll suggesting that quiet quitters make up at least 50% of the US workforce, it’s important to consider how the situation impacts their peers, managers, company, and professional community.

Ghost engineers typically take quiet quitting one step further—often performing so minimally that they are not meeting the lowest requirements of their roles. However, their organizations are partially to blame for letting them get away with it. 

For the record, defining and measuring software engineering productivity is nuanced and complex. Beyond writing code, engineers spend time on design, planning, mentorship, and solving complex problems—activities that are essential but often hard to quantify. And yes, some roles, particularly at senior levels, don’t involve hands-on coding work.

That being said, for software engineers hired with the primary responsibility of writing code, consistently not doing so represents a real issue that warrants attention. What’s at stake? Organizational inefficiencies, missed deadlines, wasted resources, and decreased team morale will ultimately negatively affect the P&L and erode customer satisfaction. 

What can contribute to hidden underperformance?

There is likely no single reason for the ghost engineer phenomenon, but rather a combination of contributing factors, each requiring its own mitigation. 

The shift to remote work

Over the last decade, remote workers in the US tech sector have increased dramatically.  The COVID-19 pandemic caused a massive shift to remote work, with both the number and percentage of remote workers more than tripling. And while the percentage has plateaued and even slightly decreased in some sectors, it remains significantly higher than pre-pandemic levels. 

A 2024 study by the U.S. Bureau of Labor Statistics found that industries with a higher increase in remote work also experienced substantial increases in output, suggesting a positive correlation between remote work and productivity. 

But for all its advantages, some employees have taken this as an opportunity to play the system. Take, for example, “over-employment,” the practice wherein employees secretly take on two or more remote jobs simultaneously. In most cases, double-dipping developers struggle to dedicate sufficient time and effort to either role, which often shows up in the form of unavailability, inconsistency, and notable underperformance. 

Companies that thrive in this era are learning to address these hidden underperformance challenges, creating systems that balance autonomy with collaboration, ensuring every voice remains active and engaged.

Ambiguous expectations 

Many organizations recognize the importance of structured career progression frameworks for software engineers. Also known as career ladders, these frameworks describe clear advancement paths through multiple levels of seniority. However, they rarely include quantifiable contribution metrics that can be used to benchmark employees. Why is that? 

In the development world, there’s a pervasive belief that counting one’s contributions is taboo. The working assumption is that software engineers are incredibly smart and talented, will naturally know what’s expected of them, and will deliver great work. The uproar following McKinsey’s article on measuring software engineering productivity highlighted just how deeply this resistance runs. 

However, for some employees, the lack of clear expectations creates an environment where ambiguity can be exploited, making it easier to coast by with hidden underperformance or contribute only the bare minimum.

Organizational sluggishness 

As organizations grow in size and complexity, their processes must evolve to support new and maturing objectives. To combat the infamous sluggishness of large companies, more people are hired to coordinate, manage dependencies, and monitor progress of key initiatives. In fact, Faros AI’s data shows that up to 25% of software engineering employees are “bureaucrats”—roles that focus on process, not coding.

While having the right systems in place is critical, overcomplicating procedures can backfire. The abundance of meetings, new reporting requirements, and multi-step approval processes negatively impact overall productivity. When excessive bureaucracy stifles creativity and agility, morale also suffers. 

At this tipping point, some engineers may decide it's not worth their while to invest effort in areas they see as beyond their control. Instead, they disengage and become ghost engineers, choosing to stay in the background and contribute just enough to avoid drawing attention.

How to spot ghost engineers

Fortunately, the first step to identifying ghost engineers in your organization is easier than leaders might think. Engineering tools and collaboration systems capture the digital breadcrumbs of engineers' contributions during their daily work. 

Platforms like Faros AI use this data to produce a sophisticated contribution analysis for engineers in coding roles while accounting for all the mitigating circumstances (parental leave, sick leave, vacation, etc.).  

Contribution need not be examined through a single lens alone. As mentioned above, developers contribute value by leading projects, designing solutions, mentoring junior team members, interviewing new candidates, and more. But the absence of code contribution—when it’s expected—should at least warrant further investigation. 

Once you have an initial readout, you can validate the data with line managers and determine whether issues stem from individual performance, misaligned expectations, or broader process inefficiencies. 

Three steps to address ghost engineers

Whether due to unclear expectations, disengagement, or a lack of accountability, ghost engineers can quietly drain productivity and morale. Addressing this issue requires a structured approach that combines clear expectations, data-driven insights, and qualitative feedback. Here’s how to tackle it effectively.

Step 1: Set clear expectations

With employee engagement sinking to a 10-year low, the importance of clear expectations cannot be overstated. When developers lack clarity around their roles, responsibilities, and project goals, confusion and frustration take root, creating the perfect storm for disengagement and burnout. Clear expectations and well-defined contribution baselines can eliminate ambiguity and give developers the direction to focus and thrive. 

Managers should clearly define expectations and role-specific productivity baselines, set SMART goals, and align individual contributions with team objectives to lay a foundation for developers to perform at their best. If you are concerned with hidden underperformance, this would be a good time to revisit your career ladders to ensure they accurately reflect your expectations. Then, make sure to communicate them clearly to your teams.

Setting clear expectations is just the start. To meet them, developers need the right tools, manageable workloads, and a culture that values their growth and contributions. When employees feel supported and recognized, they’re motivated to go beyond the minimum. 

Combine transparency with a clear connection to the company’s broader mission, and you create an environment where developers are engaged and empowered to deliver exceptional results, lowering the likelihood of hidden underperformance.

Step 2: Identify patterns of underperformance in data

To uncover patterns of underperformance, analyze an engineer’s visible activity across systems like GitHub, Jira, and their calendar over time. For instance, an engineer may have minimal code contributions or reviews in GitHub, while also showing low activity in task management systems like Jira or Asana—fewer tasks created, completed, or moved through workflows. Additionally, if calendar data shows they aren’t typically engaged in interviews, meetings, or collaborative sessions, this could signal potential hidden underperformance. 

Next, compare this data against team norms and peers in similar roles with similar expectations. Are others at the same seniority level or with similar workloads delivering more consistent results? Is this individual’s contributions near the average or far below? 

If workflows or dependencies are slowing multiple team members, the issue is likely not individual. However, repeated and sustained gaps across tasks, contributions, and collaboration—especially when team processes seem otherwise functional—are strong indicators of a deeper issue.

It’s critical to remember that different roles within a software engineering team will naturally have varied expectations and responsibilities, affecting how their data appears across tools and systems. That’s why clearly defining those expectations is so critical. 

For example, senior engineers or team leads may have less hands-on coding time, but should be contributing more through mentoring, design reviews, or cross-team collaboration, which would be evident in higher levels of code review activity or meeting facilitation. Junior developers, on the other hand, may be expected to focus more on individual coding tasks and have more direct output in GitHub or task management tools like Jira. 

For roles that span multiple responsibilities, such as full-stack developers or those involved in both coding and DevOps, you’ll want to evaluate a combination of activity across tasks, code contributions, and even collaboration efforts to get a clearer picture.

Step 3: Contextualize with qualitative insights

Holding regular 1:1s with individual team members, in conjunction with reviewing survey responses, is invaluable for uncovering additional context behind the numbers. These conversations and responses can reveal whether a lack of productivity stems from unclear expectations, personal challenges, or team-wide blockers. They also provide an opportunity for employees to share their perspectives on their workload, contributions, and any support they may need to improve their productivity.

Furthermore, team retrospectives complement these insights by surfacing feedback from colleagues who may have more direct visibility into an individual’s work. This is especially important for recognizing contributions that aren’t easily quantifiable, such as mentoring, resolving team-wide technical issues, or supporting cross-functional collaboration. 

By triangulating patterns from quantitative data with qualitative input from multiple angles, managers can assess performance holistically and identify the root causes of challenges.

Building a culture of accountability, efficiency, and transparency

Identifying the presence of ghost engineers and strategies to identify their hidden underperformance is not about creating a cutthroat environment or implementing practices like rank-and-yank, which can erode trust, collaboration, and morale. 

Instead, the focus should be building a culture rooted in transparency, accountability, and balance, wherein individuals and teams feel connected to, cared for, and supported by their managers. This means being upfront about expectations, fostering open communication, and using data and context to create a fair and objective process for evaluating software engineering performance and contributions. 

By striving for balance—encouraging innovation and creativity without overlooking hidden underperformance—companies can ensure their teams are productive, motivated, engaged, and aligned with the organization’s goals. 

Contact us today to learn more about how Faros AI can help connect the dots and reveal productivity issues in your organization.